- fca-list - lists.cs.uni-kassel.de

Link for coordinating first working group meeting: FCA repository

by Tom Hanika

Dear FCA Researcher, as presented today at CONCEPTS 2024 [1] we will have the first meeting of the fcarepository.org working group in November. You are invited to participate in and contribute to this new FCA endeavor. To coordinate the date we set up a calendar tool [2]. Please indicate which dates would fit for you, if you want to participate. Best regards Tom [1] https://concepts2024.uca.es/ [2] https://terminplaner6.dfn.de/p/524cbe1df59d322f67043c5ad65a6009-870853 -- Dr. Tom Hanika Universität Kassel Tel.: +49 (0) 561 804 6252 https://www.kde.cs.uni-kassel.de/hanika ---------------------------------------------------------------------

6 days, 6 hours

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Workshop FCA4AI 2024 (Deadline extension)

by Amedeo Napoli

Sorry for duplicates. ------------------------------------------------------------------------------------------------------------------- -- FCA4AI (Twelfth Edition) -- "What can FCA do for Artificial Intelligence?'' co-located with ECAI 2024, Santiago de Compostela, Spain October 19th 2024 http://www.fca4ai.hse.ru/2024 ------------------------------------------------------------------------------------------------------------------- General Information. The eleven preceding editions of the FCA4AI Workshop (from ECAI 2012 until IJCAI 2023) showed that many researchers working in Artificial Intelligence are indeed interested in powerful techniques for classification and data mining provided by Formal Concept Analysis. The twelfth edition of FCA4AI will once more be co-located with the ECAI Conference, and thus be held in Santiago de Compostela. Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications and association rules) which can be used for many AI needs, e.g. knowledge processing, knowledge discovery, knowledge representation and reasoning, ontology engineering as well as information retrieval, recommendation, social network analysis and text processing. Thus, there are many "natural links'' between FCA and AI. Recent years have been witnessing increased scientific activity around FCA, in particular a strand of work emerged that is aimed at extending the possibilities of plain FCA w.r.t. knowledge processing, such as work on pattern structures and relational context analysis, as well as on hybridization with other formalisms. These extensions are aimed at allowing FCA to deal with more complex than just binary data, for solving complex problems in data analysis, classification, knowledge processing, etc. While the capabilities of FCA are extended, new possibilities are arising in the framework of FCA. As usual, the FCA4AI workshop is dedicated to the discussion of such issues, and in particular: - How can FCA support AI activities in knowledge discovery, knowledge representation and reasoning, machine learning, natural language processing and others? - Vice versa, how can the current developments in AI be adopted within FCA to help AI researchers solve complex problems in their domain? - Which role can FCA play in the new trends in AI, especially in ML, XAI, fairness of algorithms, and "hybrid systems'' combining symbolic and subsymbolic approaches? TOPICS OF INTEREST include but are not limited to: - Concept lattices and related structures: pattern structures, relational structures, distributive lattices. - Knowledge discovery and data mining: pattern mining, association rules, attribute implications, subgroup discovery, exceptional model mining, data dependencies, attribute exploration, stability, projections, interestingness measures, MDL principle, mining of complex data, triadic and polyadic analysis. - Knowledge and data engineering: knowledge representation, reasoning, ontology engineering, mining the web of data, text mining, data quality checking. - Analyzing the potential of FCA in supporting hybrid systems: how to combine FCA and data mining algorithms, such as deep learning for building hybrid knowledge discovery systems, producing explanations, and assessing system fairness. - Analyzing the potential of FCA in AI tasks such as classification, clustering, biclustering, information retrieval, navigation, recommendation, text processing, visualization, pattern recognition, analysis of social networks. - FCA and Large Language Models (LLMs). - Practical applications in agronomy, astronomy, biology, chemistry, finance, manufacturing, medicine, and others. The workshop will include time for audience discussion aimed at a better understanding of the issues, challenges, and ideas being presented. IMPORTANT DATES: Submission deadline: September 08 2024 Notification to authors: September 23 2024 Final version: October 06 2024 Workshop: October 19 2024 SUBMISSION DETAILS: The workshop welcomes submissions in pdf format following the CEURART style 1-column (to be downloaded at https://ceur-ws.org/Vol-XXX/CEURART.zip). Submissions can be: - technical papers between 8 and 12 pages, - system descriptions or position papers describing work in progress not exceeding 6 pages. Submissions are via EasyChair at https://easychair.org/conferences/?conf=fca4ai2024 The workshop proceedings will be published as CEUR proceedings (see preceding editions in CEUR Proceedings Vol-3489, Vol-3233, Vol-2972, Vol-2729, Vol-2529, Vol-2149, Vol-1703, Vol-1430, Vol-1257, Vol-1058, and Vol-939). WORKSHOP CHAIRS: Sergei O. Kuznetsov National Research University Higher Schools of Economics, Moscow, Russia Amedeo Napoli Université de Lorraine, CNRS, Inria, LORIA, Nancy, France Sebastian Rudolph Technische Universität Dresden, Germany PROGRAM COMMITTEE (under construction) ------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------------------

3 weeks, 1 day

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Second Rostock Open Science Workshop

by MPIDR - Career

Open science aims to make research results and materials freely accessible to everyone, with the goal of increasing knowledge circulation, increasing transparency, and providing the means to reproduce and generalize published findings. Venue: Max Planck Institute for Demographic Research (MPIDR), Rostock, Germany Dates: March 17-18, 2025 Website: https://www.demogr.mpg.de/en/news_events_6123/calendar_1921/second_rostock_… -- This mail has been sent through the MPI for Demographic Research. Should you receive a mail that is apparently from a MPI user without this text displayed, then the address has most likely been faked. If you are uncertain about the validity of this message, please check the mail header or ask your system administrator for assistance.

1 month, 2 weeks

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[KaRS 2024] 3rd CfP: 6th Knowledge-aware and Conversational Recommender Systems Workshop 2024 @ ACM RecSys

by Vito Walter Anelli

3rd Call for Papers Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS 2024) https://kars-workshop.github.io/2024/ Oct. 14th - Oct. 18th, 2024, Bari Submission deadline: August 30th, 2024, AoE We are pleased to invite you to contribute to the Sixth Knowledge-aware and Conversational Recommender Systems Workshop held in conjunction with the ACM International Conference on Recommender Systems (RecSys 2024), Bari, Italy, from October the 14th to October the 18th, 2024. # Scope Recommender systems have achieved ubiquity across various domains, ranging from e-commerce to media content suggestions, playing pivotal roles in facilitating user online experiences. However, despite their prevalence, these systems often encounter challenges in effectively engaging with human users. While data-driven algorithms have demonstrated success in uncovering latent connections within user-item interactions, they frequently overlook the central actor in this loop: the end-user. A prevalent behavior among human users, which is rarely encoded in recommendation engines, is the utilization of domain-specific knowledge. Fortunately, Knowledge-aware Recommender Systems are garnering increasing attention within the recommendation community. By leveraging explicit domain knowledge represented via ontologies or knowledge graphs, these approaches can understand the semantic relationships between users, items, and other entities, thus offering tailored recommendations to users and addressing inherent limitations of purely data-driven systems. Despite their existence for over two decades, their significance has been revitalized due to the Linked Open Data initiative and the availability of large knowledge-graphs such as DBpedia and Wikidata. Linked data and their ontologies underpin many recommendation approaches and challenges proposed in recent years, such as Knowledge Graph embeddings, hybrid recommendation, link prediction, knowledge transfer, interpretable recommendation, and user modeling. Moreover, a new wave in this domain is marked by the emergence of neuro-symbolic systems, integrating data-driven methodologies with symbolic reasoning. This fusion of machine learning systems, adept at harnessing data, with symbolic approaches, adept at leveraging knowledge, holds promise in enhancing recommendation quality, particularly in scenarios with sparse training data. In parallel, the rise of Conversational Recommender Systems (CRSs) highlights the crucial role of content features in facilitating user interactions, particularly in multi-turn dialogues between users and the system, which bring about novel challenges, such as the incorporation of both short- and long-term preferences, prompt adaptation to user feedback, the limited availability of datasets, and the evaluation beyond simple accuracy metrics. In this context, the emergence of Large Language Models (LLMs) is pivotal and has breathed new life into CRSs. LLMs significantly impact CRSs by leveraging their advanced capabilities in understanding user queries and generating relevant recommendations in natural language. They excel in processing complex and nuanced user inputs, allowing for more seamless and engaging interactions between users and the system. Moreover, LLMs contribute to the adaptability and responsiveness of CRSs by continuously learning from user interactions, thus refining their recommendations and improving the user experience over time. The **Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop** aims to spark a new generation of research that prioritizes user experience, engagement, and satisfaction over mere accuracy. Drawing upon a multifaceted set of expertise including **Machine Learning**, **Deep Learning**, **Human-Computer Interaction**, **Information Retrieval**, and **Information Systems**, this workshop endeavors to catalyze a fresh wave of research. KaRS serves as a dynamic forum where researchers and practitioners, scholars and industry professionals, converge to not only disseminate their latest findings but also to identify the emerging topics, delineate the next key challenges, and forecast the future opportunities for research and development. Through fostering active participation and facilitating the exchange of ideas, KaRS aims to cultivate an interdisciplinary community that collaborates on the topics of knowledge-aware and conversational recommenders, alongside the emerging topics of LLMs and neuro-symbolic methodologies, which further extend the scope of this new edition of KaRS. # Topics This workshop aims at establishing an interdisciplinary community with a focus on the exploitation of (semi-)structured knowledge and conversational approaches for recommender systems and promoting collaboration opportunities between researchers and practitioners. Topics of interest include, but are not limited to: - **Knowledge-aware Recommender Systems**. - Models and Feature Engineering: - Knowledge-aware data models based on structured knowledge sources (e.g., Linked Open Data, BabelNet, Wikidata, etc.) - Semantics-aware approaches exploiting the analysis of textual sources (e.g., Wikipedia, Social Web, etc.) - Knowledge-aware user modeling - Methodological aspects (evaluation protocols, metrics, and data sets) - Logic-based modeling of a recommendation process - Knowledge Representation and Automated Reasoning for recommendation engines - Deep learning methods to model semantic features - Large language models (LLMs) for Knowledge-aware Recommender Systems - Beyond-Accuracy Recommendation Quality: - Using knowledge bases and knowledge graphs to increase recommendation quality(e.g., in terms of novelty, diversity, serendipity, or explainability) - Explainable Recommender Systems - Knowledge-aware explanations to recommendations (compliant with the General Data Protection Regulation) - Online Studies: - Using knowledge sources for cross-lingual recommendations - Applications of knowledge-aware recommenders (e.g., music or news recommendation, off-mainstream application areas) - User studies (e.g., on the user's perception of knowledge-based recommendations), field studies, in-depth experimental offline evaluations - **Conversational Recommender Systems**. - Design of a Conversational Agent: - Design and implementation methodologies - Dialogue management (end-to-end, dialog-state-tracker models) - UX design - Dialog protocols design - Large language models (LLMs) for Conversational Recommender Systems - User Modeling and interfaces: - Critiquing and user feedback exploitation - Short- and Long-term user profiling and modeling - Preference elicitation - Natural language-, multi-modal-, and voice-based interfaces - Next-question problem - Methodological and Theoretical aspects: - Evaluation and metrics - Datasets - Theoretical aspects of conversational recommender systems # Submissions We invite three kinds of submissions, which address novel issues in Knowledge-aware and Conversational Recommender Systems: * **Long Papers** should report on substantial contributions of lasting value. **The Long papers must have a length of a minimum of 6 and a maximum of 8 pages (plus an unlimited number of pages for references)**. * **Short/Demo Papers** typically discuss exciting new work that is not yet mature enough for a long paper. In particular, novel but significant proposals will be considered for acceptance in this category despite not having gone through sufficient experimental validation or lacking a strong theoretical foundation. Applications of recommender systems to novel areas are especially welcome. **The Short/Demo papers must have a length of a minimum of 3 and a maximum of 5 pages (plus an unlimited number of pages for references)**. * **Position/Discussion Papers** describe novel and innovative ideas. Position papers may also comprise an analysis of currently unsolved problems, or review these problems from a new perspective, in order to contribute to a better understanding of these problems in the research community. We expect that such papers will guide future research by highlighting critical assumptions, motivating the difficulty of a certain problem, or explaining why current techniques are not sufficient, possibly corroborated by quantitative and qualitative arguments. **The Position/Discussion papers must have a length of a minimum of 2 and a maximum of 3 pages (plus an unlimited number of pages for references)**. Papers may range from theoretical works to system descriptions. We particularly encourage Ph.D. students or Early-Stage Researchers to submit their research. We also welcome contributions from the industry and papers describing ongoing funded projects which may result useful to the Knowledge-aware and Conversational Recommender Systems community. Submission will be peer-reviewed and accepted papers will appear in the **workshop proceedings** (CEUR workshop series). The review process is **single-blind**. Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. Long and short/demo paper submissions must be original work and may not be under submission to another venue at the time of review. Each accepted long or short paper will be included in the CEUR online Workshop proceedings and presented in a plenary session as part of the Workshop program. Original position/discussion accepted papers will be included in the CEUR online Workshop proceedings. Selected position/discussion papers will be invited as oral presentations. Submissions of full research papers must be in English, in PDF format in the **CEUR-WS two-column conference format** available as compressed archive (http://ceur-ws.org/Vol-XXX/CEURART.zip) or as Overleaf template (https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-w…). Papers must be submitted through EasyChair (https://easychair.org/conferences/?conf=recsys2024workshops) by selecting the track "KaRS: Sixth Knowledge-aware and Conversational Recommender Systems Workshop". # Important Dates * **Paper submissions deadline**: August 30th, 2024 * **Paper acceptance notification**: September 16th, 2024 * **Camera-ready deadline**: September 30th, 2024 * **Workshop day**: October 14th-18th, 2024 Deadlines refer to 23:59 (11:59 pm) in the AoE (Anywhere on Earth) time zone. Informativa Privacy - Ai sensi del Regolamento (UE) 2016/679 si precisa che le informazioni contenute in questo messaggio sono riservate e ad uso esclusivo del destinatario. Qualora il messaggio in parola Le fosse pervenuto per errore, La preghiamo di eliminarlo senza copiarlo e di non inoltrarlo a terzi, dandocene gentilmente comunicazione. Grazie. Privacy Information - This message, for the Regulation (UE) 2016/679, may contain confidential and/or privileged information. If you are not the addressee or authorized to receive this for the addressee, you must not use, copy, disclose or take any action based on this message or any information herein. If you have received this message in error, please advise the sender immediately by reply e-mail and delete this message. Thank you for your cooperation.

1 month, 2 weeks

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Next ESAO webinars

by Cassia TROJAHN

Dear all, The Educational Series on Applied Ontology (ESAO) [1] is open for everyone and welcomes students, researchers and practitioners alike. Three sessions are scheduled to take place in the second semester of 2024 (details on [1]): **September 10th, 16:00 CEST** Olivier Kutz (Free University of Bozen-Bolzano, Italy) Title: TBA Maria Hedblom (Jönköping University, Sweden) Title: Cognitive foundations for KR **November 12th, 16:00 CET** Sandra Lovrencic (University of Zagreb Faculty of organization and informatics) Title: Concepts Jérôme Euzenat (INRIA & Univ. Grenoble Alpes, France) Title: Ontology evolution **December 17th, 16:00 CET** Sergio De Cesare (University of Westminster, UK) Title: Ontology-driven conceptual modelling Maria Keet (University of Cape Town, South Africa) Title: Competency questions We count on your participation! Best regards Cassia Trojahn On behalf of the ESAO organisers [1] https://wiki.iaoa.org/index.php/Edu:ESAO

1 month, 3 weeks

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Call for papers FCA4AI 12th Workshop co-located with ECAI 2024 at Santiago de Compostela, Spain

by Amedeo Napoli

Apologies for duplicates... ------------------------------------------------------------------------------------------------------------------- -- FCA4AI (Twelfth Edition) -- "What can FCA do for Artificial Intelligence?'' co-located with ECAI 2024, Santiago de Compostela, Spain October 19th 2024 http://www.fca4ai.hse.ru/2024 ------------------------------------------------------------------------------------------------------------------- General Information. The eleven preceding editions of the FCA4AI Workshop (from ECAI 2012 until IJCAI 2023) showed that many researchers working in Artificial Intelligence are indeed interested in powerful techniques for classification and data mining provided by Formal Concept Analysis. The twelfth edition of FCA4AI will once more be co-located with the ECAI Conference, and thus be held in Santiago de Compostela. Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications and association rules) which can be used for many AI needs, e.g. knowledge processing, knowledge discovery, knowledge representation and reasoning, ontology engineering as well as information retrieval, recommendation, social network analysis and text processing. Thus, there are many "natural links'' between FCA and AI. Recent years have been witnessing increased scientific activity around FCA, in particular a strand of work emerged that is aimed at extending the possibilities of plain FCA w.r.t. knowledge processing, such as work on pattern structures and relational context analysis, as well as on hybridization with other formalisms. These extensions are aimed at allowing FCA to deal with more complex than just binary data, for solving complex problems in data analysis, classification, knowledge processing, etc. While the capabilities of FCA are extended, new possibilities are arising in the framework of FCA. As usual, the FCA4AI workshop is dedicated to the discussion of such issues, and in particular: - How can FCA support AI activities in knowledge discovery, knowledge representation and reasoning, machine learning, natural language processing and others? - Vice versa, how can the current developments in AI be adopted within FCA to help AI researchers solve complex problems in their domain? - Which role can FCA play in the new trends in AI, especially in ML, XAI, fairness of algorithms, and "hybrid systems'' combining symbolic and subsymbolic approaches? TOPICS OF INTEREST include but are not limited to: - Concept lattices and related structures: pattern structures, relational structures, distributive lattices. - Knowledge discovery and data mining: pattern mining, association rules, attribute implications, subgroup discovery, exceptional model mining, data dependencies, attribute exploration, stability, projections, interestingness measures, MDL principle, mining of complex data, triadic and polyadic analysis. - Knowledge and data engineering: knowledge representation, reasoning, ontology engineering, mining the web of data, text mining, data quality checking. - Analyzing the potential of FCA in supporting hybrid systems: how to combine FCA and data mining algorithms, such as deep learning for building hybrid knowledge discovery systems, producing explanations, and assessing system fairness. - Analyzing the potential of FCA in AI tasks such as classification, clustering, biclustering, information retrieval, navigation, recommendation, text processing, visualization, pattern recognition, analysis of social networks. - FCA and Large Language Models (LLMs). - Practical applications in agronomy, astronomy, biology, chemistry, finance, manufacturing, medicine, and others. The workshop will include time for audience discussion aimed at a better understanding of the issues, challenges, and ideas being presented. IMPORTANT DATES: Submission deadline: August 25 2024 Notification to authors: September 16 2024 Final version: October 06 2024 Workshop: October 19 2024 SUBMISSION DETAILS: The workshop welcomes submissions in pdf format following the CEURART style 1-column (to be downloaded at https://ceur-ws.org/Vol-XXX/CEURART.zip). Submissions can be: - technical papers between 8 and 12 pages, - system descriptions or position papers describing work in progress not exceeding 6 pages. Submissions are via EasyChair at https://easychair.org/conferences/?conf=fca4ai2024 The workshop proceedings will be published as CEUR proceedings (see preceding editions in CEUR Proceedings Vol-3489, Vol-3233, Vol-2972, Vol-2729, Vol-2529, Vol-2149, Vol-1703, Vol-1430, Vol-1257, Vol-1058, and Vol-939). WORKSHOP CHAIRS: Sergei O. Kuznetsov National Research University Higher Schools of Economics, Moscow, Russia Amedeo Napoli Université de Lorraine, CNRS, Inria, LORIA, Nancy, France Sebastian Rudolph Technische Universität Dresden, Germany PROGRAM COMMITTEE (under construction) ------------------------------------------------------------------------------------------------------------------- -------------------------------------------------------------------------------------------------------------------

2 months

1
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[KaRS 2024] 2nd CfP: 6th Knowledge-aware and Conversational Recommender Systems Workshop 2024 @ ACM RecSys

by Vito Walter Anelli

2nd Call for Papers Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS 2024) https://kars-workshop.github.io/2024/ Oct. 14th - Oct. 18th, 2024, Bari Submission deadline: August 30th, 2024, AoE We are pleased to invite you to contribute to the Sixth Knowledge-aware and Conversational Recommender Systems Workshop held in conjunction with the ACM International Conference on Recommender Systems (RecSys 2024), Bari, Italy, from October the 14th to October the 18th, 2024. # Scope Recommender systems have achieved ubiquity across various domains, ranging from e-commerce to media content suggestions, playing pivotal roles in facilitating user online experiences. However, despite their prevalence, these systems often encounter challenges in effectively engaging with human users. While data-driven algorithms have demonstrated success in uncovering latent connections within user-item interactions, they frequently overlook the central actor in this loop: the end-user. A prevalent behavior among human users, which is rarely encoded in recommendation engines, is the utilization of domain-specific knowledge. Fortunately, Knowledge-aware Recommender Systems are garnering increasing attention within the recommendation community. By leveraging explicit domain knowledge represented via ontologies or knowledge graphs, these approaches can understand the semantic relationships between users, items, and other entities, thus offering tailored recommendations to users and addressing inherent limitations of purely data-driven systems. Despite their existence for over two decades, their significance has been revitalized due to the Linked Open Data initiative and the availability of large knowledge-graphs such as DBpedia and Wikidata. Linked data and their ontologies underpin many recommendation approaches and challenges proposed in recent years, such as Knowledge Graph embeddings, hybrid recommendation, link prediction, knowledge transfer, interpretable recommendation, and user modeling. Moreover, a new wave in this domain is marked by the emergence of neuro-symbolic systems, integrating data-driven methodologies with symbolic reasoning. This fusion of machine learning systems, adept at harnessing data, with symbolic approaches, adept at leveraging knowledge, holds promise in enhancing recommendation quality, particularly in scenarios with sparse training data. In parallel, the rise of Conversational Recommender Systems (CRSs) highlights the crucial role of content features in facilitating user interactions, particularly in multi-turn dialogues between users and the system, which bring about novel challenges, such as the incorporation of both short- and long-term preferences, prompt adaptation to user feedback, the limited availability of datasets, and the evaluation beyond simple accuracy metrics. In this context, the emergence of Large Language Models (LLMs) is pivotal and has breathed new life into CRSs. LLMs significantly impact CRSs by leveraging their advanced capabilities in understanding user queries and generating relevant recommendations in natural language. They excel in processing complex and nuanced user inputs, allowing for more seamless and engaging interactions between users and the system. Moreover, LLMs contribute to the adaptability and responsiveness of CRSs by continuously learning from user interactions, thus refining their recommendations and improving the user experience over time. The **Sixth Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop** aims to spark a new generation of research that prioritizes user experience, engagement, and satisfaction over mere accuracy. Drawing upon a multifaceted set of expertise including **Machine Learning**, **Deep Learning**, **Human-Computer Interaction**, **Information Retrieval**, and **Information Systems**, this workshop endeavors to catalyze a fresh wave of research. KaRS serves as a dynamic forum where researchers and practitioners, scholars and industry professionals, converge to not only disseminate their latest findings but also to identify the emerging topics, delineate the next key challenges, and forecast the future opportunities for research and development. Through fostering active participation and facilitating the exchange of ideas, KaRS aims to cultivate an interdisciplinary community that collaborates on the topics of knowledge-aware and conversational recommenders, alongside the emerging topics of LLMs and neuro-symbolic methodologies, which further extend the scope of this new edition of KaRS. # Topics This workshop aims at establishing an interdisciplinary community with a focus on the exploitation of (semi-)structured knowledge and conversational approaches for recommender systems and promoting collaboration opportunities between researchers and practitioners. Topics of interest include, but are not limited to: - **Knowledge-aware Recommender Systems**. - Models and Feature Engineering: - Knowledge-aware data models based on structured knowledge sources (e.g., Linked Open Data, BabelNet, Wikidata, etc.) - Semantics-aware approaches exploiting the analysis of textual sources (e.g., Wikipedia, Social Web, etc.) - Knowledge-aware user modeling - Methodological aspects (evaluation protocols, metrics, and data sets) - Logic-based modeling of a recommendation process - Knowledge Representation and Automated Reasoning for recommendation engines - Deep learning methods to model semantic features - Large language models (LLMs) for Knowledge-aware Recommender Systems - Beyond-Accuracy Recommendation Quality: - Using knowledge bases and knowledge graphs to increase recommendation quality(e.g., in terms of novelty, diversity, serendipity, or explainability) - Explainable Recommender Systems - Knowledge-aware explanations to recommendations (compliant with the General Data Protection Regulation) - Online Studies: - Using knowledge sources for cross-lingual recommendations - Applications of knowledge-aware recommenders (e.g., music or news recommendation, off-mainstream application areas) - User studies (e.g., on the user's perception of knowledge-based recommendations), field studies, in-depth experimental offline evaluations - **Conversational Recommender Systems**. - Design of a Conversational Agent: - Design and implementation methodologies - Dialogue management (end-to-end, dialog-state-tracker models) - UX design - Dialog protocols design - Large language models (LLMs) for Conversational Recommender Systems - User Modeling and interfaces: - Critiquing and user feedback exploitation - Short- and Long-term user profiling and modeling - Preference elicitation - Natural language-, multi-modal-, and voice-based interfaces - Next-question problem - Methodological and Theoretical aspects: - Evaluation and metrics - Datasets - Theoretical aspects of conversational recommender systems # Submissions We invite three kinds of submissions, which address novel issues in Knowledge-aware and Conversational Recommender Systems: * **Long Papers** should report on substantial contributions of lasting value. **The Long papers must have a length of a minimum of 6 and a maximum of 8 pages (plus an unlimited number of pages for references)**. * **Short/Demo Papers** typically discuss exciting new work that is not yet mature enough for a long paper. In particular, novel but significant proposals will be considered for acceptance in this category despite not having gone through sufficient experimental validation or lacking a strong theoretical foundation. Applications of recommender systems to novel areas are especially welcome. **The Short/Demo papers must have a length of a minimum of 3 and a maximum of 5 pages (plus an unlimited number of pages for references)**. * **Position/Discussion Papers** describe novel and innovative ideas. Position papers may also comprise an analysis of currently unsolved problems, or review these problems from a new perspective, in order to contribute to a better understanding of these problems in the research community. We expect that such papers will guide future research by highlighting critical assumptions, motivating the difficulty of a certain problem, or explaining why current techniques are not sufficient, possibly corroborated by quantitative and qualitative arguments. **The Position/Discussion papers must have a length of a minimum of 2 and a maximum of 3 pages (plus an unlimited number of pages for references)**. Papers may range from theoretical works to system descriptions. We particularly encourage Ph.D. students or Early-Stage Researchers to submit their research. We also welcome contributions from the industry and papers describing ongoing funded projects which may result useful to the Knowledge-aware and Conversational Recommender Systems community. Submission will be peer-reviewed and accepted papers will appear in the **workshop proceedings** (CEUR workshop series). The review process is **single-blind**. Submitted papers will be evaluated according to their originality, technical content, style, clarity, and relevance to the workshop. Long and short/demo paper submissions must be original work and may not be under submission to another venue at the time of review. Each accepted long or short paper will be included in the CEUR online Workshop proceedings and presented in a plenary session as part of the Workshop program. Original position/discussion accepted papers will be included in the CEUR online Workshop proceedings. Selected position/discussion papers will be invited as oral presentations. Submissions of full research papers must be in English, in PDF format in the **CEUR-WS two-column conference format** available as compressed archive (http://ceur-ws.org/Vol-XXX/CEURART.zip) or as Overleaf template (https://www.overleaf.com/latex/templates/template-for-submissions-to-ceur-w…). Papers must be submitted through EasyChair (https://easychair.org/conferences/?conf=recsys2024workshops) by selecting the track "KaRS: Sixth Knowledge-aware and Conversational Recommender Systems Workshop". # Important Dates * **Paper submissions deadline**: August 30th, 2024 * **Paper acceptance notification**: September 16th, 2024 * **Camera-ready deadline**: September 30th, 2024 * **Workshop day**: October 14th-18th, 2024 Deadlines refer to 23:59 (11:59 pm) in the AoE (Anywhere on Earth) time zone. Informativa Privacy - Ai sensi del Regolamento (UE) 2016/679 si precisa che le informazioni contenute in questo messaggio sono riservate e ad uso esclusivo del destinatario. Qualora il messaggio in parola Le fosse pervenuto per errore, La preghiamo di eliminarlo senza copiarlo e di non inoltrarlo a terzi, dandocene gentilmente comunicazione. Grazie. Privacy Information - This message, for the Regulation (UE) 2016/679, may contain confidential and/or privileged information. If you are not the addressee or authorized to receive this for the addressee, you must not use, copy, disclose or take any action based on this message or any information herein. If you have received this message in error, please advise the sender immediately by reply e-mail and delete this message. Thank you for your cooperation.

2 months

1
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0 0

[CfP] [CoKA] : --- Final Call --- "Conceptual Knowledge Acquisition: Challenges, Opportunities, and Use Cases" workshop at CONCEPTS 2024

by Tom Hanika

***CoKA: --- Final Call --- Call for Contributions*** DEADLINE EXTENDED TO: *August 7th, 2024* (23:59 AoE) ================================================================ Conceptual Knowledge Acquisition: Challenges, Opportunities, and Use Cases Workshop at the 1st International Joint Conference on Conceptual Knowledge Structures (CONCEPTS 2024) September 9–13 2024, Cádiz, Spain Workshop Website: https://www.kde.cs.uni-kassel.de/coka/ Conference website: https://concepts2024.uca.es ================================================================ Formal concept analysis (FCA) can help make sense of data and the underlying domain --- provided the data is not too big, not too noisy, representative of the domain, and if there is data in the first place. What if you don’t have such data readily available but are prepared to invest in collecting it and have access to domain experts or other reliable queryable sources of information? Conceptual exploration comes to the rescue! Conceptual exploration is a family of knowledge-acquisition techniques within FCA. The goal is to build a complete implicational theory of a domain (with respect to a fixed language) by posing queries to a domain expert. When properly implemented, it is a great tool that can help organize the process of scientific discovery. Unfortunately, proper implementations are scarce and success stories of using conceptual exploration are somewhat rare and limited in scope. With this workshop, we intend to analyze the situation and, maybe, find a solution. If - you succeeded in acquiring new knowledge about or building a satisfying conceptual representation of some domain with conceptual exploration before; - you attempted conceptual exploration in application to your problem but failed miserably; - you want to use conceptual exploration to analyze some domain, but you don’t know where and how to start; - you are aware of alternatives to conceptual exploration; then come to the workshop to share your experiences, insights, ideas, and concerns with us! ================== Keywords and Topics ================== Knowledge Acquisition and Capture Conceptual Exploration Design Patterns and Paradigmatic Examples successful use cases and real-world applications challenges and lessons learned application principles missing theoretical foundations missing technical infrastructure integration with other theories and technologies ========================= Duration, Format, and Dates ========================= We invite contributions in the form of an extended abstract of up to two pages. In addition, supplementary material, such as data sets, detailed descriptions, or visualizations, may be submitted. The workshop is planned for half a day within the conference dates and at the same venue. It will consist of several short presentations each followed by a plenary discussion. Please send your contributions until *August 7th, 2024* (23:59 AoE) to tom.hanika(a)uni-hildesheim.de. If you are not sure whether your contribution matches the topics or the format of the workshop, you are welcome to contact the organizers prior to submitting the abstract. An acceptance notification will be sent within two weeks upon receiving the submission. =================== Workshop Organizers =================== - Tom Hanika, University of Hildesheim - Sergei Obiedkov, TU Dresden - Bernhard Ganter, Ernst-Schröder-Zentrum, Darmstadt -- Dr. Tom Hanika Universität Kassel Tel.: +49 (0) 561 804 6252 https://www.kde.cs.uni-kassel.de/hanika ---------------------------------------------------------------------

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LAST CFP for KoDis/CAKR 2024, the Joint Workshop on Knowledge Diversity and Cognitive Aspects of KR (colocated with KR 2024) - DEADLINE: 17.07.2024

by Lucia Gomez Alvarez

CALL FOR PAPERS Joint Workshop on Knowledge Diversity and Cognitive Aspects of KR (KoDis/CAKR) ============================================================================== Co-located with the 21st International Conference on Principles of Knowledge Representation and Reasoning (KR 2024), November 2 – 8, 2024 in Hanoi, Vietnam This workshop is the joint continuation of the previous Workshop on Cognitive Aspects of KR (CAKR) and of the Workshop on Knowledge Diversity (KoDis). In view of the partial overlap of topics and target audience, we organise the KoDis and CAKR workshops jointly this year. Website: https://kodis-cakr24.krportal.org/ Important Dates: ---------------- All dates are given Anywhere on Earth (AoE). - Papers due: July 17, 2024 - Notification to authors: August 21, 2024 - Camera-ready version due: September 18, 2024 - Workshop date: November 2, 3, or 4, 2024 Overview: --------- The KoDis workshop intends to create a space of confluence and a forum for discussion for researchers interested in knowledge diversity in a wide sense, including diversity in terms of diverging perspectives, different beliefs, semantic heterogeneity and others. The importance of understanding and handling the different forms of diversity that manifest between knowledge formalisations (ontologies, knowledge bases, or knowledge graphs) is widely recognised and has led to the proposal of a variety of systems of representation, tackling overlapping aspects of this phenomenon. Besides understanding the phenomenon and considering formal models for the representation of knowledge diversity, we are interested in the variety of reasoning problems that emerge in this context, including joint reasoning with possibly conflicting sources, interpreting knowledge from alternative viewpoints, consolidating the diversity as uncertainty, reasoning by means of argumentation between the sources and pursuing knowledge aggregations among others. A non-exhaustive list of topics of interest for the KoDis workshop is given below. - Philosophical and cognitive analysis of knowledge diversity. - Formal models for the representation of knowledge diversity. - Ontological approaches capturing multiple perspectives and viewpoints. - Context and concept formation in such systems. - Consistency (or not) in multi-perspective systems; assessment and mitigation of inconsistencies. - Communication between knowledge-diverse systems. - Argumentation-based approaches for dealing with inconsistency. - Aggregation of diverse or inconsistent knowledge; judgement aggregation. - Uncertainty in the context of knowledge diversity. - Applications of formal models of knowledge diversity. The CAKR workshop deals with cognitively adequate approaches to knowledge representation and reasoning. Knowledge representation is a lively and well-established field of AI, where knowledge and belief are represented declaratively and suitable for machine processing. It is often claimed that this declarative nature makes knowledge representation cognitively more adequate than e.g. sub-symbolic approaches, such as machine learning. This cognitive adequacy has important ramifications for the explainability of approaches in knowledge representation, which in turn is essential for the trustworthiness of these approaches. However, exactly how cognitive adequacy is ensured has often been left implicit, and connections with cognitive science and psychology are only recently being taken up. The goal of the CAKR workshop is to bring together experts from fields including artificial intelligence, psychology, cognitive science and philosophy to discuss important questions related to cognitive aspects of knowledge representation, such as: - How can we study the cognitive adequacy of approaches in AI? - Are declarative approaches cognitively more adequate than other approaches in AI? - What is the connection between cognitive adequacy and explanatory potential? - How to develop benchmarks for studying cognitive aspects of AI? - Which results from psychology are relevant for AI? - What is the role of the normative-descriptive distinction in current developments in AI? Call for Papers: --------------- We invite both long and short papers, as well as reports on recently published papers in reputed venues. Submissions will be peer-reviewed to ensure quality and relevance to the workshop. At least one author of each accepted paper will be required to attend the workshop to present the contribution. Submissions should be of one of the following types: - long papers reporting unpublished research (10–12 pages excluding references), - short papers reporting unpublished research (5–6 pages excluding references), or - extended abstracts (up to 3 pages including references) presenting work relevant to the workshop already published in other conferences or journals. Such an abstract should summarize the contributions of the article and its relevance for the workshop, as well as include bibliographic details of the article and a link to the article. Publication: ----------- We plan to publish informal proceedings in the CEUR Workshop Proceedings. Organizing Committee: --------------------- Lucía Gómez Alvarez, Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France Jonas Haldimann, FernUniversität in Hagen, Germany Jesse Heyninck, OpenUniversiteit, the Netherlands; University of Cape Town and CAIR, South Africa Srdjan Vesic, CRIL CNRS Univ. Artois, France

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ESAO Webinar n.11 on Tuesday July 9th, 2024 16:30 CEST –- Panel: Applied Ontologies in the the Neuro-symbolic Age -- Call for Participation

by Cassia TROJAHN

Dear all, The Educational Series on Applied Ontology (ESAO) [1] is open for everyone and welcomes students, researchers and practitioners alike. The 11th ESAO webinar will be held on Tuesday, July 9, 2024 at 16:30 CET, together with FOIS 2024 online. --------------------------- When and how to connect Tuesday July 9th, 2024 16:30 CEST Duration: 90 minutes Video conference (via Zoom) : => Requires *free* registration to FOIS online [2] --------------------------- Program Title: Applied Ontologies in the the Neuro-symbolic Age 10:30-12:00 EDT / 14:30-16:00 UTC / 16:30-18:00 CEST / 16:30-18:00 SAST / 11:30-13:00 UTC-3 Panelists: Pascal Hitzler (Kansas State University), Pawel Garbacz (The John Paul II Catholic University of Lublin), Fabian Neuhaus (Otto von Guericke University Magdeburg), Luciano Serafini (Fondazione Bruno Kessler), John Beverley (University of Buffalo) Moderator: Mehwish Alam (Télécom Paris, Institut Polytechnique de Paris, France) Abstract: In the evolving landscape of AI, the panel on "Applied Ontologies in the Age of Neuro-Symbolic AI" will discuss the integration of symbolic AI (based on knowledge representation and formal logic) and statistical (based on artificial neural networks). The aim is to cover a broader overview on the topic, definitions, impact on research in both statistical and symbolic AI (and in other fields), and future perspectives. See details [3]. Best regards Cassia Trojahn, Frank Loebe, Laure Vieu On behalf of the IAOA Education Committee [1] https://wiki.iaoa.org/index.php/Edu:ESAO [2] https://www.utwente.nl/en/eemcs/fois2024/registration/online-registration/ [3] https://www.utwente.nl/en/eemcs/fois2024/program/panels/?

2 months, 2 weeks

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