{"id":9663,"date":"2024-04-22T14:36:50","date_gmt":"2024-04-22T12:36:50","guid":{"rendered":"https:\/\/www.kde.cs.uni-kassel.de\/?page_id=9663"},"modified":"2024-04-22T14:37:17","modified_gmt":"2024-04-22T12:37:17","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.kde.cs.uni-kassel.de\/en\/hirth\/publications","title":{"rendered":"publications"},"content":{"rendered":"<div id=\"trailimageid\"><img decoding=\"async\" id=\"ttimg\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/loading.gif\"><\/div> <p>Scientific Works of <a href=\"https:\/\/www.kde.cs.uni-kassel.de\/hirth\">Johannes Hirth<a>.<\/p>\n \n <div class=\"bibsonomycsl_jump_list\">[<a class=\"bibsonomycsl_publications-headline-jumplabel\" href=\"#jmp_2024\" title=\"Goto 2024\">2024<\/a>] [<a class=\"bibsonomycsl_publications-headline-jumplabel\" href=\"#jmp_2023\" title=\"Goto 2023\">2023<\/a>] [<a class=\"bibsonomycsl_publications-headline-jumplabel\" href=\"#jmp_2022\" title=\"Goto 2022\">2022<\/a>] [<a class=\"bibsonomycsl_publications-headline-jumplabel\" href=\"#jmp_2021\" title=\"Goto 2021\">2021<\/a>] [<a class=\"bibsonomycsl_publications-headline-jumplabel\" href=\"#jmp_2019\" title=\"Goto 2019\">2019<\/a>]<\/div><ul class=\"bibsonomycsl_publications\">\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2024\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2024<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/misc.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Draude, C., Engert, S., Hess, T., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M., Zwingmann, N.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Verrechnung \u2013 Design \u2013 Kultivierung: Instrumentenkasten f\u00fcr die Gestaltung fairer Gesch\u00e4ftsmodelle durch Ko-Valuation<\/span><\/span>, https:\/\/plattform-privatheit.de\/p-prv-wAssets\/Assets\/Veroeffentlichungen_WhitePaper_PolicyPaper\/whitepaper\/WP_2024_FAIRDIENSTE_1.0.pdf, (2024). https:\/\/doi.org\/10.24406\/publica-2497.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/plattform-privatheit.de\/p-prv-wAssets\/Assets\/Veroeffentlichungen_WhitePaper_PolicyPaper\/whitepaper\/WP_2024_FAIRDIENSTE_1.0.pdf\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-be31cee0b76f9489f6d607a17bd881e6\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-be31cee0b76f9489f6d607a17bd881e6\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.24406\/publica-2497\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/be31cee0b76f9489f6d607a17bd881e6\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-be31cee0b76f9489f6d607a17bd881e6\"><p>@misc{claude2024verrechnung,<br\/>  address = {Karlsruhe},<br\/>  author = {Draude, Claude and Engert, Simon and Hess, Thomas and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, J\u00f6rn and Stumme, Gerd and Uhlmann, Markus and Zwingmann, Nina},<br\/>  edition = 1,<br\/>  editor = {Friedewald, Michael and Ro\u00dfnagel, Alexander and Geminn, Christian and Karaboga, Murat},<br\/>  howpublished = {White Paper},<br\/>  keywords = {itegpub},<br\/>  month = {03},<br\/>  publisher = {Fraunhofer-Institut f\u00fcr System- und Innovationsforschung ISI},<br\/>  series = {Plattform Privatheit},<br\/>  title = {Verrechnung \u2013 Design \u2013 Kultivierung: Instrumentenkasten f\u00fcr die Gestaltung fairer Gesch\u00e4ftsmodelle durch Ko-Valuation},<br\/>  year = 2024<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-be31cee0b76f9489f6d607a17bd881e6\"><p>%0 Generic<br\/>%1 claude2024verrechnung<br\/>%A Draude, Claude<br\/>%A Engert, Simon<br\/>%A Hess, Thomas<br\/>%A Hirth, Johannes<br\/>%A Horn, Viktoria<br\/>%A Kropf, Jonathan<br\/>%A Lamla, J\u00f6rn<br\/>%A Stumme, Gerd<br\/>%A Uhlmann, Markus<br\/>%A Zwingmann, Nina<br\/>%B Plattform Privatheit<br\/>%C Karlsruhe<br\/>%D 2024<br\/>%E Friedewald, Michael<br\/>%E Ro\u00dfnagel, Alexander<br\/>%E Geminn, Christian<br\/>%E Karaboga, Murat<br\/>%I Fraunhofer-Institut f\u00fcr System- und Innovationsforschung ISI<br\/>%R 10.24406\/publica-2497<br\/>%T Verrechnung \u2013 Design \u2013 Kultivierung: Instrumentenkasten f\u00fcr die Gestaltung fairer Gesch\u00e4ftsmodelle durch Ko-Valuation<br\/>%U https:\/\/plattform-privatheit.de\/p-prv-wAssets\/Assets\/Veroeffentlichungen_WhitePaper_PolicyPaper\/whitepaper\/WP_2024_FAIRDIENSTE_1.0.pdf<br\/>%7 1<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/phdthesis.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Conceptual Data Scaling in Machine Learning<\/span><\/span>, (2024). https:\/\/doi.org\/10.17170\/kobra-2024100910940.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-ee1e60a9f300cfca6f746491d4a9d445\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-ee1e60a9f300cfca6f746491d4a9d445\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-ee1e60a9f300cfca6f746491d4a9d445\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.17170\/kobra-2024100910940\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/ee1e60a9f300cfca6f746491d4a9d445\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-ee1e60a9f300cfca6f746491d4a9d445\">Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-ee1e60a9f300cfca6f746491d4a9d445\"><p>@phdthesis{doi:10.17170\/kobra-2024100910940,<br\/>  abstract = {Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.},<br\/>  author = {Hirth, Johannes},<br\/>  keywords = {Scaling},<br\/>  school = {Kassel, Universit\u00e4t Kassel, Fachbereich Elektrotechnik\/Informatik},<br\/>  title = {Conceptual Data Scaling in Machine Learning},<br\/>  year = 2024<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-ee1e60a9f300cfca6f746491d4a9d445\"><p>%0 Thesis<br\/>%1 doi:10.17170\/kobra-2024100910940<br\/>%A Hirth, Johannes<br\/>%D 2024<br\/>%R 10.17170\/kobra-2024100910940<br\/>%T Conceptual Data Scaling in Machine Learning<br\/>%X Information that is intended for human interpretation is frequently represented in a structured manner. This allows for a navigation between individual pieces to find, connect or combine information to gain new insights. Within a structure, we derive knowledge from inference of hierarchical or logical relations between data objects. For unstructured data there are numerous methods to define a data schema based on user interpretations. Afterward, data objects can be aggregated to derive (hierarchical) structures based on common properties. There are four main challenges with respect to the explainability of the derived structures. First, formal procedures are needed to infer knowledge about the data set, or parts of it, from hierarchical structures. Second, what does knowledge inferred from a structure imply for the data set it was derived from? Third, structures may be incomprehensibly large for human interpretation. Methods are needed to reduce structures to smaller representations in a consistent, comprehensible manner that provides control over possibly introduced error. Forth, the original data set does not need to have interpretable features and thus only allow for the inference of structural properties. In order to extract information based on real world properties, we need methods that are able to add such properties. With the presented work, we address these challenges using and extending the rich tool-set of Formal Concept Analysis. Here, data objects are aggregated to closed sets called formal concepts based on (unary) symbolic attributes that they have in common. The process of deriving symbolic attributes is called conceptual scaling and depends on the interpretation of the data by the analyst. The resulting hierarchical structure of concepts is called concept lattice. To infer knowledge from the concept lattice structures we introduce new methods based on sub-structures that are of standardized shape, called ordinal motifs. This novel method allows us to explain the structure of a concept lattice based on geometric aspects. Throughout our work, we focus on data representations from multiple state-of-the-art machine learning algorithms. In all cases, we elaborate extensively on how to interpret these models through derived concept lattices and develop scaling procedures specific to each algorithm. Some of the considered models are black-box models whose internal data representations are numeric with no clear real world semantics. For these, we present a method to link background knowledge to the concept lattice structure. To reduce the complexity of concept lattices we provide a new theoretical framework that allows us to generate (small) views on a concept lattice. These enable more selective and comprehensibly sized explanations for data parts that are of interest. In addition to that, we introduce methods to combine and subtract views from each other, and to identify missing or incorrect parts.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Abdulla, M., Hirth, J., Stumme, G.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">The Birkhoff Completion of\u00a0Finite Lattices<\/span>.<\/span> In: Cabrera, I.P., Ferr\u00e9, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 20\u201335. Springer Nature Switzerland, Cham (2024).<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-295c39417c8443a31a7aab2a003f0f32\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-295c39417c8443a31a7aab2a003f0f32\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-295c39417c8443a31a7aab2a003f0f32\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/295c39417c8443a31a7aab2a003f0f32\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-295c39417c8443a31a7aab2a003f0f32\">We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-295c39417c8443a31a7aab2a003f0f32\"><p>@inproceedings{10.1007\/978-3-031-67868-4_2,<br\/>  abstract = {We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.},<br\/>  address = {Cham},<br\/>  author = {Abdulla, Mohammad and Hirth, Johannes and Stumme, Gerd},<br\/>  booktitle = {Conceptual Knowledge Structures},<br\/>  editor = {Cabrera, Inma P. and Ferr\u00e9, S\u00e9bastien and Obiedkov, Sergei},<br\/>  keywords = {itegpub},<br\/>  pages = {20--35},<br\/>  publisher = {Springer Nature Switzerland},<br\/>  title = {The Birkhoff Completion of\u00a0Finite Lattices},<br\/>  year = 2024<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-295c39417c8443a31a7aab2a003f0f32\"><p>%0 Conference Paper<br\/>%1 10.1007\/978-3-031-67868-4_2<br\/>%A Abdulla, Mohammad<br\/>%A Hirth, Johannes<br\/>%A Stumme, Gerd<br\/>%B Conceptual Knowledge Structures<br\/>%C Cham<br\/>%D 2024<br\/>%E Cabrera, Inma P.<br\/>%E Ferr\u00e9, S\u00e9bastien<br\/>%E Obiedkov, Sergei<br\/>%I Springer Nature Switzerland<br\/>%P 20--35<br\/>%T The Birkhoff Completion of\u00a0Finite Lattices<br\/>%X We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science.<br\/>%@ 978-3-031-67868-4<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Horn, V., Hirth, J., Holfeld, J., Behmenburg, J.H., Draude, C., Stumme, G.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content<\/span>.<\/span> In: Nordic Conference on Human-Computer Interaction. Association for Computing Machinery, Uppsala, Sweden (2024). https:\/\/doi.org\/10.1145\/3679318.3685414.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-6fd27e97604d62808e8e60e7d0eb049f\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1145\/3679318.3685414\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-6fd27e97604d62808e8e60e7d0eb049f\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-6fd27e97604d62808e8e60e7d0eb049f\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.1145\/3679318.3685414\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/6fd27e97604d62808e8e60e7d0eb049f\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-6fd27e97604d62808e8e60e7d0eb049f\">Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user\u2019s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user\u2019s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-6fd27e97604d62808e8e60e7d0eb049f\"><p>@inproceedings{hci-lattice,<br\/>  abstract = {Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user\u2019s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user\u2019s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.},<br\/>  address = {New York, NY, USA},<br\/>  author = {Horn, Viktoria and Hirth, Johannes and Holfeld, Julian and Behmenburg, Jens Hendrik and Draude, Claude and Stumme, Gerd},<br\/>  booktitle = {Nordic Conference on Human-Computer Interaction},<br\/>  keywords = {Formal-Journalism-Navigation},<br\/>  publisher = {Association for Computing Machinery},<br\/>  series = {NordiCHI 2024},<br\/>  title = {Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content},<br\/>  year = 2024<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-6fd27e97604d62808e8e60e7d0eb049f\"><p>%0 Conference Paper<br\/>%1 hci-lattice<br\/>%A Horn, Viktoria<br\/>%A Hirth, Johannes<br\/>%A Holfeld, Julian<br\/>%A Behmenburg, Jens Hendrik<br\/>%A Draude, Claude<br\/>%A Stumme, Gerd<br\/>%B Nordic Conference on Human-Computer Interaction<br\/>%C New York, NY, USA<br\/>%D 2024<br\/>%I Association for Computing Machinery<br\/>%R 10.1145\/3679318.3685414<br\/>%T Disclosing Diverse Perspectives of News Articles for Navigating between Online Journalism Content<br\/>%U https:\/\/doi.org\/10.1145\/3679318.3685414<br\/>%X Today, exposure to journalistic online content is predominantly controlled by news recommender systems, which often suggest content that matches user\u2019s interests or is selected according to non-transparent recommendation criteria. To circumvent resulting trade-offs like polarisation or fragmentation whilst ensuring user\u2019s autonomy, we explore how different perspectives within online news can be disclosed instead for guiding navigation. To do so, we developed an interactive prototype that displays article titles in correspondence to their argumentative orientation. In order to investigate how the usage of our novel navigation structure impacts the choice of news articles and user experience, we conducted an exploratory user study assessing the impact of the design parameters chosen. Implications are drawn from the study results and the development of the interactive prototype for the exposure to diversity in the context of navigating news content online.<br\/>%@ 9798400709661<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/misc.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hirth, J., Hanika, T.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">The Geometric Structure of Topic Models<\/span><\/span>, (2024). https:\/\/doi.org\/10.48550\/arxiv.2403.03607.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-1b0ed3ebbff2d484aea73656a8c15398\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-1b0ed3ebbff2d484aea73656a8c15398\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.48550\/arxiv.2403.03607\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/1b0ed3ebbff2d484aea73656a8c15398\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-1b0ed3ebbff2d484aea73656a8c15398\"><p>@misc{hirth2024geometric,<br\/>  author = {Hirth, Johannes and Hanika, Tom},<br\/>  keywords = {selected},<br\/>  publisher = {arXiv},<br\/>  title = {The Geometric Structure of Topic Models},<br\/>  year = 2024<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-1b0ed3ebbff2d484aea73656a8c15398\"><p>%0 Generic<br\/>%1 hirth2024geometric<br\/>%A Hirth, Johannes<br\/>%A Hanika, Tom<br\/>%D 2024<br\/>%I arXiv<br\/>%R 10.48550\/arxiv.2403.03607<br\/>%T The Geometric Structure of Topic Models<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Draude, C., D\u00fcrrschnabel, D., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Conceptual Mapping of\u00a0Controversies<\/span>.<\/span> In: Cabrera, I.P., Ferr\u00e9, S., and Obiedkov, S. (eds.) Conceptual Knowledge Structures. pp. 201\u2013216. Springer Nature Switzerland, Cham (2024).<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-8c35bc18259b038c3b82766cc1fd5a8b\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-8c35bc18259b038c3b82766cc1fd5a8b\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-8c35bc18259b038c3b82766cc1fd5a8b\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/8c35bc18259b038c3b82766cc1fd5a8b\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-8c35bc18259b038c3b82766cc1fd5a8b\">With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-8c35bc18259b038c3b82766cc1fd5a8b\"><p>@inproceedings{10.1007\/978-3-031-67868-4_14,<br\/>  abstract = {With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.},<br\/>  address = {Cham},<br\/>  author = {Draude, Claude and D\u00fcrrschnabel, Dominik and Hirth, Johannes and Horn, Viktoria and Kropf, Jonathan and Lamla, J{\u00f6}rn and Stumme, Gerd and Uhlmann, Markus},<br\/>  booktitle = {Conceptual Knowledge Structures},<br\/>  editor = {Cabrera, Inma P. and Ferr\u00e9, S\u00e9bastien and Obiedkov, Sergei},<br\/>  keywords = {itegpub},<br\/>  pages = {201--216},<br\/>  publisher = {Springer Nature Switzerland},<br\/>  title = {Conceptual Mapping of\u00a0Controversies},<br\/>  year = 2024<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-8c35bc18259b038c3b82766cc1fd5a8b\"><p>%0 Conference Paper<br\/>%1 10.1007\/978-3-031-67868-4_14<br\/>%A Draude, Claude<br\/>%A D\u00fcrrschnabel, Dominik<br\/>%A Hirth, Johannes<br\/>%A Horn, Viktoria<br\/>%A Kropf, Jonathan<br\/>%A Lamla, J{\u00f6}rn<br\/>%A Stumme, Gerd<br\/>%A Uhlmann, Markus<br\/>%B Conceptual Knowledge Structures<br\/>%C Cham<br\/>%D 2024<br\/>%E Cabrera, Inma P.<br\/>%E Ferr\u00e9, S\u00e9bastien<br\/>%E Obiedkov, Sergei<br\/>%I Springer Nature Switzerland<br\/>%P 201--216<br\/>%T Conceptual Mapping of\u00a0Controversies<br\/>%X With our work, we contribute towards a qualitative analysis of the discourse on controversies in online news media. For this, we employ Formal Concept Analysis and the economics of conventions to derive conceptual controversy maps. In our experiments, we analyze two maps from different news journals with methods from ordinal data science. We show how these methods can be used to assess the diversity, complexity and potential bias of controversies. In addition to that, we discuss how the diagrams of concept lattices can be used to navigate between news articles.<br\/>%@ 978-3-031-67868-4<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2023\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2023<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/article.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Conceptual Views on Tree Ensemble Classifiers<\/span>.<\/span> International Journal of Approximate Reasoning. 108930 (2023). https:\/\/doi.org\/https:\/\/doi.org\/10.1016\/j.ijar.2023.108930.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-42b778646bbde71a9383f30e710f9a98\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0888613X23000610\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-42b778646bbde71a9383f30e710f9a98\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-42b778646bbde71a9383f30e710f9a98\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1016\/j.ijar.2023.108930\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/42b778646bbde71a9383f30e710f9a98\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-42b778646bbde71a9383f30e710f9a98\">Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-42b778646bbde71a9383f30e710f9a98\"><p>@article{https:\/\/doi.org\/10.48550\/arxiv.2302.05270,<br\/>  abstract = {Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.},<br\/>  author = {Hanika, Tom and Hirth, Johannes},<br\/>  journal = {International Journal of Approximate Reasoning},<br\/>  keywords = {itegpub},<br\/>  pages = 108930,<br\/>  title = {Conceptual Views on Tree Ensemble Classifiers},<br\/>  year = 2023<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-42b778646bbde71a9383f30e710f9a98\"><p>%0 Journal Article<br\/>%1 https:\/\/doi.org\/10.48550\/arxiv.2302.05270<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%D 2023<br\/>%J International Journal of Approximate Reasoning<br\/>%P 108930<br\/>%R https:\/\/doi.org\/10.1016\/j.ijar.2023.108930<br\/>%T Conceptual Views on Tree Ensemble Classifiers<br\/>%U https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0888613X23000610<br\/>%X Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are often used to compensate for this disadvantage. Yet, their ability for local explanations, and in particular for global explanations, is limited. In the present work we propose an algebraic method, rooted in lattice theory, for the (global) explanation of tree ensembles. In detail, we introduce two novel conceptual views on tree ensemble classifiers and demonstrate their explanatory capabilities on Random Forests that were trained with standard parameters.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hirth, J., Horn, V., Stumme, G., Hanika, T.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Automatic Textual Explanations of\u00a0Concept Lattices<\/span>.<\/span> In: Ojeda-Aciego, M., Sauerwald, K., and J\u00e4schke, R. (eds.) Graph-Based Representation and Reasoning. pp. 138\u2013152. Springer Nature Switzerland, Cham (2023).<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-e6f3519d40173a2999256c6e5ae3cead\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-e6f3519d40173a2999256c6e5ae3cead\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-e6f3519d40173a2999256c6e5ae3cead\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/e6f3519d40173a2999256c6e5ae3cead\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-e6f3519d40173a2999256c6e5ae3cead\">Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-e6f3519d40173a2999256c6e5ae3cead\"><p>@inproceedings{10.1007\/978-3-031-40960-8_12,<br\/>  abstract = {Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.},<br\/>  address = {Cham},<br\/>  author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},<br\/>  booktitle = {Graph-Based Representation and Reasoning},<br\/>  editor = {Ojeda-Aciego, Manuel and Sauerwald, Kai and J\u00e4schke, Robert},<br\/>  keywords = {itegpub},<br\/>  pages = {138--152},<br\/>  publisher = {Springer Nature Switzerland},<br\/>  title = {Automatic Textual Explanations of\u00a0Concept Lattices},<br\/>  year = 2023<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-e6f3519d40173a2999256c6e5ae3cead\"><p>%0 Conference Paper<br\/>%1 10.1007\/978-3-031-40960-8_12<br\/>%A Hirth, Johannes<br\/>%A Horn, Viktoria<br\/>%A Stumme, Gerd<br\/>%A Hanika, Tom<br\/>%B Graph-Based Representation and Reasoning<br\/>%C Cham<br\/>%D 2023<br\/>%E Ojeda-Aciego, Manuel<br\/>%E Sauerwald, Kai<br\/>%E J\u00e4schke, Robert<br\/>%I Springer Nature Switzerland<br\/>%P 138--152<br\/>%T Automatic Textual Explanations of\u00a0Concept Lattices<br\/>%X Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard scales.<br\/>%@ 978-3-031-40960-8<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Ganter, B., Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Scaling Dimension<\/span>.<\/span> In: D\u00fcrrschnabel, D. and L\u00f3pez-Rodr\u00edguez, D. (eds.) Formal Concept Analysis - 17th International Conference, ICFCA 2023, Kassel, Germany, July 17-21, 2023, Proceedings. pp. 64\u201377. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-35949-1_5.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-35949-1_5\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-ae364c1012bb099c906b24ecdc7f688d\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-ae364c1012bb099c906b24ecdc7f688d\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-35949-1_5\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/ae364c1012bb099c906b24ecdc7f688d\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-ae364c1012bb099c906b24ecdc7f688d\"><p>@inproceedings{DBLP:conf\/icfca\/GanterHH23,<br\/>  author = {Ganter, Bernhard and Hanika, Tom and Hirth, Johannes},<br\/>  booktitle = {Formal Concept Analysis - 17th International Conference, ICFCA 2023, Kassel, Germany, July 17-21, 2023, Proceedings},<br\/>  editor = {D\u00fcrrschnabel, Dominik and L\u00f3pez-Rodr\u00edguez, Domingo},<br\/>  keywords = {itegpub},<br\/>  pages = {64--77},<br\/>  publisher = {Springer},<br\/>  series = {Lecture Notes in Computer Science},<br\/>  title = {Scaling Dimension},<br\/>  volume = 13934,<br\/>  year = 2023<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-ae364c1012bb099c906b24ecdc7f688d\"><p>%0 Conference Paper<br\/>%1 DBLP:conf\/icfca\/GanterHH23<br\/>%A Ganter, Bernhard<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%B Formal Concept Analysis - 17th International Conference, ICFCA 2023, Kassel, Germany, July 17-21, 2023, Proceedings<br\/>%D 2023<br\/>%E D\u00fcrrschnabel, Dominik<br\/>%E L\u00f3pez-Rodr\u00edguez, Domingo<br\/>%I Springer<br\/>%P 64--77<br\/>%R 10.1007\/978-3-031-35949-1_5<br\/>%T Scaling Dimension<br\/>%U https:\/\/doi.org\/10.1007\/978-3-031-35949-1_5<br\/>%V 13934<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/article.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hirth, J., Horn, V., Stumme, G., Hanika, T.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Ordinal Motifs in Lattices<\/span>.<\/span> Information Sciences. 120009 (2023). https:\/\/doi.org\/https:\/\/doi.org\/10.1016\/j.ins.2023.120009.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-f3c5809943daadd698a01e8af7ab6feb\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523015943\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-f3c5809943daadd698a01e8af7ab6feb\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-f3c5809943daadd698a01e8af7ab6feb\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1016\/j.ins.2023.120009\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/f3c5809943daadd698a01e8af7ab6feb\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-f3c5809943daadd698a01e8af7ab6feb\">Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose \/ordinal motifs\/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-f3c5809943daadd698a01e8af7ab6feb\"><p>@article{hirth2023ordinal,<br\/>  abstract = {Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose \/ordinal motifs\/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.},<br\/>  author = {Hirth, Johannes and Horn, Viktoria and Stumme, Gerd and Hanika, Tom},<br\/>  journal = {Information Sciences},<br\/>  keywords = {selected},<br\/>  pages = 120009,<br\/>  title = {Ordinal Motifs in Lattices},<br\/>  year = 2023<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-f3c5809943daadd698a01e8af7ab6feb\"><p>%0 Journal Article<br\/>%1 hirth2023ordinal<br\/>%A Hirth, Johannes<br\/>%A Horn, Viktoria<br\/>%A Stumme, Gerd<br\/>%A Hanika, Tom<br\/>%D 2023<br\/>%J Information Sciences<br\/>%P 120009<br\/>%R https:\/\/doi.org\/10.1016\/j.ins.2023.120009<br\/>%T Ordinal Motifs in Lattices<br\/>%U https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523015943<br\/>%X Lattices are a commonly used structure for the representation and analysis of relational and ontological knowledge. In particular, the analysis of these requires a decomposition of a large and high-dimensional lattice into a set of understandably large parts. With the present work we propose \/ordinal motifs\/ as analytical units of meaning. We study these ordinal substructures (or standard scales) through (full) scale-measures of formal contexts from the field of formal concept analysis. We show that the underlying decision problems are NP-complete and provide results on how one can incrementally identify ordinal motifs to save computational effort. Accompanying our theoretical results, we demonstrate how ordinal motifs can be leveraged to retrieve basic meaning from a medium sized ordinal data set.<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2022\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2022<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/article.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Sch\u00e4fermeier, B., Hirth, J., Hanika, T.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Research Topic Flows in Co-Authorship Networks<\/span>.<\/span> Scientometrics. (2022). https:\/\/doi.org\/10.1007\/s11192-022-04529-w.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-aa092a3903693a9dfe27e456a6b55d33\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-aa092a3903693a9dfe27e456a6b55d33\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-aa092a3903693a9dfe27e456a6b55d33\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.1007\/s11192-022-04529-w\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/aa092a3903693a9dfe27e456a6b55d33\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-aa092a3903693a9dfe27e456a6b55d33\">In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-aa092a3903693a9dfe27e456a6b55d33\"><p>@article{schafermeier2022research,<br\/>  abstract = {In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.},<br\/>  author = {Sch\u00e4fermeier, Bastian and Hirth, Johannes and Hanika, Tom},<br\/>  journal = {Scientometrics},<br\/>  keywords = {selected},<br\/>  month = 10,<br\/>  title = {Research Topic Flows in Co-Authorship Networks},<br\/>  year = 2022<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-aa092a3903693a9dfe27e456a6b55d33\"><p>%0 Journal Article<br\/>%1 schafermeier2022research<br\/>%A Sch\u00e4fermeier, Bastian<br\/>%A Hirth, Johannes<br\/>%A Hanika, Tom<br\/>%D 2022<br\/>%J Scientometrics<br\/>%R 10.1007\/s11192-022-04529-w<br\/>%T Research Topic Flows in Co-Authorship Networks<br\/>%X In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/article.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Knowledge cores in large formal contexts<\/span>.<\/span> Annals of Mathematics and Artificial Intelligence. (2022). https:\/\/doi.org\/10.1007\/s10472-022-09790-6.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-a4d3512aa7b869d73a586832d3c37299\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1007\/s10472-022-09790-6\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-a4d3512aa7b869d73a586832d3c37299\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-a4d3512aa7b869d73a586832d3c37299\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.1007\/s10472-022-09790-6\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/a4d3512aa7b869d73a586832d3c37299\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-a4d3512aa7b869d73a586832d3c37299\">Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-a4d3512aa7b869d73a586832d3c37299\"><p>@article{Hanika2022,<br\/>  abstract = {Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.},<br\/>  author = {Hanika, Tom and Hirth, Johannes},<br\/>  journal = {Annals of Mathematics and Artificial Intelligence},<br\/>  keywords = {itegpub},<br\/>  month = {04},<br\/>  title = {Knowledge cores in large formal contexts},<br\/>  year = 2022<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-a4d3512aa7b869d73a586832d3c37299\"><p>%0 Journal Article<br\/>%1 Hanika2022<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%D 2022<br\/>%J Annals of Mathematics and Artificial Intelligence<br\/>%R 10.1007\/s10472-022-09790-6<br\/>%T Knowledge cores in large formal contexts<br\/>%U https:\/\/doi.org\/10.1007\/s10472-022-09790-6<br\/>%X Knowledge computation tasks, such as computing a base of valid implications, are often infeasible for large data sets. This is in particular true when deriving canonical bases in formal concept analysis (FCA). Therefore, it is necessary to find techniques that on the one hand reduce the data set size, but on the other hand preserve enough structure to extract useful knowledge. Many successful methods are based on random processes to reduce the size of the investigated data set. This, however, makes them hardly interpretable with respect to the discovered knowledge. Other approaches restrict themselves to highly supported subsets and omit rare and (maybe) interesting patterns. An essentially different approach is used in network science, called k-cores. These cores are able to reflect rare patterns, as long as they are well connected within the data set. In this work, we study k-cores in the realm of FCA by exploiting the natural correspondence of bi-partite graphs and formal contexts. This structurally motivated approach leads to a comprehensible extraction of knowledge cores from large formal contexts.<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/misc.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hirth, J., Hanika, T.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Formal Conceptual Views in Neural Networks<\/span><\/span>, https:\/\/arxiv.org\/abs\/2209.13517, (2022). https:\/\/doi.org\/10.48550\/ARXIV.2209.13517.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/arxiv.org\/abs\/2209.13517\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-d40a5033ba022c8f88208c8ec83eb171\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-d40a5033ba022c8f88208c8ec83eb171\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.48550\/ARXIV.2209.13517\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/d40a5033ba022c8f88208c8ec83eb171\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-d40a5033ba022c8f88208c8ec83eb171\"><p>@misc{https:\/\/doi.org\/10.48550\/arxiv.2209.13517,<br\/>  author = {Hirth, Johannes and Hanika, Tom},<br\/>  keywords = {sai},<br\/>  publisher = {arXiv},<br\/>  title = {Formal Conceptual Views in Neural Networks},<br\/>  year = 2022<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-d40a5033ba022c8f88208c8ec83eb171\"><p>%0 Generic<br\/>%1 https:\/\/doi.org\/10.48550\/arxiv.2209.13517<br\/>%A Hirth, Johannes<br\/>%A Hanika, Tom<br\/>%D 2022<br\/>%I arXiv<br\/>%R 10.48550\/ARXIV.2209.13517<br\/>%T Formal Conceptual Views in Neural Networks<br\/>%U https:\/\/arxiv.org\/abs\/2209.13517<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/article.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">On the lattice of conceptual measurements<\/span>.<\/span> Information Sciences. 613, 453\u2013468 (2022). https:\/\/doi.org\/https:\/\/doi.org\/10.1016\/j.ins.2022.09.005.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_export bibsonomycsl_abstract\"><a rel=\"abs-752bc4553487e6472b22faf56e744cad\"  href=\"#\">Abstract<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025522010489\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-752bc4553487e6472b22faf56e744cad\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-752bc4553487e6472b22faf56e744cad\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1016\/j.ins.2022.09.005\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/752bc4553487e6472b22faf56e744cad\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_abstract\" style=\"display:none;\" id=\"abs-752bc4553487e6472b22faf56e744cad\">We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.<\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-752bc4553487e6472b22faf56e744cad\"><p>@article{hanika2020lattice,<br\/>  abstract = {We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.},<br\/>  author = {Hanika, Tom and Hirth, Johannes},<br\/>  journal = {Information Sciences},<br\/>  keywords = {selected},<br\/>  pages = {453-468},<br\/>  title = {On the lattice of conceptual measurements},<br\/>  volume = 613,<br\/>  year = 2022<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-752bc4553487e6472b22faf56e744cad\"><p>%0 Journal Article<br\/>%1 hanika2020lattice<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%D 2022<br\/>%J Information Sciences<br\/>%P 453-468<br\/>%R https:\/\/doi.org\/10.1016\/j.ins.2022.09.005<br\/>%T On the lattice of conceptual measurements<br\/>%U https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025522010489<br\/>%V 613<br\/>%X We present a novel approach for data set scaling based on scale-measures from formal concept analysis, i.e., continuous maps between closure systems, for which we derive a canonical representation. Moreover, we prove that scale-measures can be lattice ordered using the canonical representation. This enables exploring the set of scale-measures by the use of meet and join operations. Furthermore we show that the lattice of scale-measures is isomorphic to the lattice of sub-closure systems that arises from the original data. Finally, we provide another representation of scale-measures using propositional logic in terms of data set features. Our theoretical findings are discussed by means of examples.<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2021\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2021<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Quantifying the Conceptual Error in Dimensionality Reduction<\/span>.<\/span> In: Braun, T., Gehrke, M., Hanika, T., and Hernandez, N. (eds.) Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings. pp. 105\u2013118. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-86982-3_8.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-86982-3_8\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-bccd47524af1f4528212c2b971ed9c54\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-bccd47524af1f4528212c2b971ed9c54\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.1007\/978-3-030-86982-3_8\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/bccd47524af1f4528212c2b971ed9c54\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-bccd47524af1f4528212c2b971ed9c54\"><p>@inproceedings{DBLP:conf\/iccs\/HanikaH21,<br\/>  author = {Hanika, Tom and Hirth, Johannes},<br\/>  booktitle = {Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings},<br\/>  editor = {Braun, Tanya and Gehrke, Marcel and Hanika, Tom and Hernandez, Nathalie},<br\/>  keywords = {selected},<br\/>  pages = {105--118},<br\/>  publisher = {Springer},<br\/>  series = {Lecture Notes in Computer Science},<br\/>  title = {Quantifying the Conceptual Error in Dimensionality Reduction},<br\/>  volume = 12879,<br\/>  year = 2021<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-bccd47524af1f4528212c2b971ed9c54\"><p>%0 Conference Paper<br\/>%1 DBLP:conf\/iccs\/HanikaH21<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%B Graph-Based Representation and Reasoning - 26th International Conference on Conceptual Structures, ICCS 2021, Virtual Event, September 20-22, 2021, Proceedings<br\/>%D 2021<br\/>%E Braun, Tanya<br\/>%E Gehrke, Marcel<br\/>%E Hanika, Tom<br\/>%E Hernandez, Nathalie<br\/>%I Springer<br\/>%P 105--118<br\/>%R 10.1007\/978-3-030-86982-3_8<br\/>%T Quantifying the Conceptual Error in Dimensionality Reduction<br\/>%U https:\/\/doi.org\/10.1007\/978-3-030-86982-3_8<br\/>%V 12879<br\/><\/p><\/div><\/div><\/li><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Exploring Scale-Measures of Data Sets<\/span>.<\/span> In: Braud, A., Buzmakov, A., Hanika, T., and Ber, F.L. (eds.) Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings. pp. 261\u2013269. Springer (2021). https:\/\/doi.org\/10.1007\/978-3-030-77867-5_17.<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-77867-5_17\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-6ddf8be80b973b6693c80c1b7b3bfd91\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-6ddf8be80b973b6693c80c1b7b3bfd91\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/dx.doi.org\/10.1007\/978-3-030-77867-5_17\" target=\"_blank\">DOI<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/6ddf8be80b973b6693c80c1b7b3bfd91\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-6ddf8be80b973b6693c80c1b7b3bfd91\"><p>@inproceedings{DBLP:conf\/icfca\/HanikaH21,<br\/>  author = {Hanika, Tom and Hirth, Johannes},<br\/>  booktitle = {Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings},<br\/>  editor = {Braud, Agn{{\u00e8}}s and Buzmakov, Aleksey and Hanika, Tom and Ber, Florence Le},<br\/>  keywords = {closure},<br\/>  pages = {261--269},<br\/>  publisher = {Springer},<br\/>  series = {Lecture Notes in Computer Science},<br\/>  title = {Exploring Scale-Measures of Data Sets},<br\/>  volume = 12733,<br\/>  year = 2021<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-6ddf8be80b973b6693c80c1b7b3bfd91\"><p>%0 Conference Paper<br\/>%1 DBLP:conf\/icfca\/HanikaH21<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%B Formal Concept Analysis - 16th International Conference, ICFCA 2021, Strasbourg, France, June 29 - July 2, 2021, Proceedings<br\/>%D 2021<br\/>%E Braud, Agn{{\u00e8}}s<br\/>%E Buzmakov, Aleksey<br\/>%E Hanika, Tom<br\/>%E Ber, Florence Le<br\/>%I Springer<br\/>%P 261--269<br\/>%R 10.1007\/978-3-030-77867-5_17<br\/>%T Exploring Scale-Measures of Data Sets<br\/>%U https:\/\/doi.org\/10.1007\/978-3-030-77867-5_17<br\/>%V 12733<br\/><\/p><\/div><\/div><\/li>\n<\/ul>\n<a class=\"bibsonomycsl_publications-headline-anchor \" name=\"jmp_2019\"><\/a><h3 class=\"bibsonomycsl_publications-headline\" style=\"font-size: 1.1em; font-weight: bold;\">2019<\/h3>\n<ul class=\"bibsonomycsl_publications\"><li class=\"bibsonomycsl_pubitem\"><div class=\"bibsonomycsl_preview_border bibsonomycsl_preview_thumb\">\n                                        <span>\n                                            <img decoding=\"async\" class=\"bibsonomycsl_preview\" style=\"z-index: 1;\" src=\"https:\/\/www.kde.cs.uni-kassel.de\/wp-content\/plugins\/bibsonomy-csl\/img\/entrytypes\/inproceedings.jpg\" \/>\n                                        <\/span>\n                                 <\/div><div class=\"bibsonomycsl_entry\"><div class=\"csl-bib-body\">\n  <div class=\"csl-entry\"><div class=\"csl-left-margin\"><span style=\"display: none;\">1.<\/span><\/div><div class=\"csl-right-inline\"><span class=\"csl-author\">Hanika, T., Hirth, J.: <\/span><span class=\"csl-title\"><span class=\"csl-title\">Conexp-Clj - A Research Tool for FCA.<\/span><\/span> In: Cristea, D., Ber, F.L., Missaoui, R., Kwuida, L., and Sertkaya, B. (eds.) ICFCA (Supplements). pp. 70\u201375. CEUR-WS.org (2019).<\/div><\/div>\n<\/div><span class=\"bibsonomycsl_url\"><a href=\"http:\/\/dblp.uni-trier.de\/db\/conf\/icfca\/icfca2019suppl.html#HanikaH19\" target=\"_blank\">URL<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_bibtex\"><a rel=\"bib-432006ec0f26e5c6c5e26900faadc7e3\" href=\"#\">BibTeX<\/a><\/span><span class=\"bibsonomycsl_export bibsonomycsl_endnote\"><a rel=\"end-432006ec0f26e5c6c5e26900faadc7e3\" href=\"#\">EndNote<\/a><\/span><span class=\"bibsonomycsl_url\"><a href=\"https:\/\/www.bibsonomy.org\/bibtex\/432006ec0f26e5c6c5e26900faadc7e3\/hirth\" target=\"_blank\">BibSonomy<\/a><\/span><div style=\"clear: left\"> <\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_bibtex\" style=\"display:none;\" id=\"bib-432006ec0f26e5c6c5e26900faadc7e3\"><p>@inproceedings{conf\/icfca\/HanikaH19,<br\/>  author = {Hanika, Tom and Hirth, Johannes},<br\/>  booktitle = {ICFCA (Supplements)},<br\/>  crossref = {conf\/icfca\/2019suppl},<br\/>  editor = {Cristea, Diana and Ber, Florence Le and Missaoui, Rokia and Kwuida, L\u00e9onard and Sertkaya, Baris},<br\/>  keywords = {itegpub},<br\/>  pages = {70-75},<br\/>  publisher = {CEUR-WS.org},<br\/>  series = {CEUR Workshop Proceedings},<br\/>  title = {Conexp-Clj - A Research Tool for FCA.},<br\/>  volume = 2378,<br\/>  year = 2019<br\/>}<br\/><\/p><\/div><div class=\"bibsonomycsl_collapse bibsonomycsl_pub_endnote\" style=\"display:none;\" id=\"end-432006ec0f26e5c6c5e26900faadc7e3\"><p>%0 Conference Paper<br\/>%1 conf\/icfca\/HanikaH19<br\/>%A Hanika, Tom<br\/>%A Hirth, Johannes<br\/>%B ICFCA (Supplements)<br\/>%D 2019<br\/>%E Cristea, Diana<br\/>%E Ber, Florence Le<br\/>%E Missaoui, Rokia<br\/>%E Kwuida, L\u00e9onard<br\/>%E Sertkaya, Baris<br\/>%I CEUR-WS.org<br\/>%P 70-75<br\/>%T Conexp-Clj - A Research Tool for FCA.<br\/>%U http:\/\/dblp.uni-trier.de\/db\/conf\/icfca\/icfca2019suppl.html#HanikaH19<br\/>%V 2378<br\/><\/p><\/div><\/div><\/li><\/ul>","protected":false},"excerpt":{"rendered":"<p>Scientific Works of Johannes Hirth. [2024] [2023] [2022] [2021] [2019] 2024 1.Draude, C., Engert, S., Hess, T., Hirth, J., Horn, V., Kropf, J., Lamla, J., Stumme, G., Uhlmann, M., Zwingmann, N.: Verrechnung \u2013 Design \u2013<a class=\"moretag\" href=\"https:\/\/www.kde.cs.uni-kassel.de\/en\/hirth\/publications\"> Read more&hellip;<\/a><\/p>\n","protected":false},"author":17,"featured_media":0,"parent":3928,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-9663","page","type-page","status-publish","hentry"],"translation":{"provider":"WPGlobus","version":"3.0.2","language":"en","enabled_languages":["de","en"],"languages":{"de":{"title":true,"content":true,"excerpt":false},"en":{"title":false,"content":false,"excerpt":false}}},"_links":{"self":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/9663","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/comments?post=9663"}],"version-history":[{"count":2,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/9663\/revisions"}],"predecessor-version":[{"id":9967,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/9663\/revisions\/9967"}],"up":[{"embeddable":true,"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/pages\/3928"}],"wp:attachment":[{"href":"https:\/\/www.kde.cs.uni-kassel.de\/en\/wp-json\/wp\/v2\/media?parent=9663"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}