Proceedings of the International Workshop "What can FCA do for Artificial Intelligence?" (FCA4AI 2014)

International audienceThis is the third edition of the FCA4AI workshop, whose first edition was organized at ECAI 2012 Conference (Montpellier, August 2012) and second edition was organized at IJCAI 2013 Conference (Beijing, August 2013, see http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the workshop is to investigate two main main issues: how can FCA support various AI activities (knowledge discovery, knowledge representation and reasoning, learning, data mining, NLP, information retrieval), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain

Discovering and Comparing Relational Knowledge, the Example of Pharmacogenomics

Article in Proceedings of the EKAW Doctoral Consortium 2018 co-located with the 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018)Pharmacogenomics (PGx) studies the influence of the genome in drug response, with knowledge units of the form of ternary relationships genomic variation-drug-phenotype. State-of-the-art PGx knowledge is available in the biomedical literature as well as in specialized knowledge bases. Additionally, Electronic Health Records of hospitals can be mined to discover such knowledge units that can then be compared with the state of the art, in order to confirm or temper relationships lacking validation or clinical counterpart. However, both discovering and comparing PGx relationships face multiple challenges: heterogeneous descriptions of knowledge units (languages, vocabularies and granularities), missing values and importance of the time dimension. In this research, we aim at proposing a framework based on Semantic Web technologies and Formal Concept Analysis to discover, represent and compare PGx knowledge units. We present the first results, consisting of creating an integrated knowledge base of PGx knowledge units from various sources and defining comparison methods, as well as the remaining issues to tackle

Combining Concept Annotation and Pattern Structures for Guiding Ontology Mapping

International audienceFormal Concept Analysis (FCA) is a mathematical framework classifying in formal concepts a set of objects w.r.t. their common attributes. To this aim, FCA relies on a binary incidence relationship indicating whether an object has an attribute. On one hand, in order to consider more complex descriptions for objects (e.g., intervals, graphs), FCA has been extended with Pattern Structures. On the other hand, in a previous work, we introduced the notion of Concept Annotation, adding a third dimension to formal concepts, computed over the extent, without modifying the original classification. In this paper, we combine Concept Annotation with the formalism of Pattern Structures and we consider multiple annotation possibilities, i.e., multiple annotations for one concept and computing the annotation over the intent. We illustrate our approach and its interest with two use cases: (i) suggesting mappings between ontology classes and (ii) finding specific classes frequently associated as domain and range of a predicate

Proceedings of the 5th International Workshop "What can FCA do for Artificial Intelligence?", FCA4AI 2016(co-located with ECAI 2016, The Hague, Netherlands, August 30th 2016)

International audienceThese are the proceedings of the fifth edition of the FCA4AI workshop (http://www.fca4ai.hse.ru/). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification that can be used for many purposes, especially for Artificial Intelligence (AI) needs. The objective of the FCA4AI workshop is to investigate two main main issues: how can FCA support various AI activities (knowledge discovery, knowledge representation and reasoning, learning, data mining, NLP, information retrieval), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain. Accordingly, topics of interest are related to the following: (i) Extensions of FCA for AI: pattern structures, projections, abstractions. (ii) Knowledge discovery based on FCA: classification, data mining, pattern mining, functional dependencies, biclustering, stability, visualization. (iii) Knowledge processing based on concept lattices: modeling, representation, reasoning. (iv) Application domains: natural language processing, information retrieval, recommendation, mining of web of data and of social networks, etc

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Workshop Notes of the Seventh International Workshop "What can FCA do for Artificial Intelligence?"

International audienceThese are the proceedings of the seventh edition of the FCA4AI workshop (http://www.fca4ai.hse.ru/) co-located with the IJCAI 2019 Conference in Macao (China). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at classification and knowledge discovery that can be used for many purposes in Artificial Intelligence (AI). The objective of the FCA4AI workshop is to investigate two main issues: how can FCA supports various AI activities (knowledge discovery, knowledge engineering, machine learning, data mining, information retrieval, recommendation. . . ), and how can FCA be extended in order to help AI researchers to solve new and complex problems in their domain