3 research outputs found
Querying Triadic Concepts through Partial or Complete Matching of Triples
In this paper, we introduce a new method for querying triadic concepts
through partial or complete matching of triples using an inverted index, to
retrieve already computed triadic concepts that contain a set of terms in their
extent, intent, and/or modus. As opposed to the approximation approach
described in Ananias, this method (i) does not need to keep the initial triadic
context or its three dyadic counterparts, (ii) avoids the application of
derivation operators on the triple components through context exploration, and
(iii) eliminates the requirement for a factorization phase to get triadic
concepts as the answer to one-dimensional queries. Additionally, our solution
introduces a novel metric for ranking the retrieved triadic concepts based on
their similarity to a given query. Lastly, an empirical study is primarily done
to illustrate the effectiveness and scalability of our approach against the
approximation one. Our solution not only showcases superior efficiency, but
also highlights a better scalability, making it suitable for big data
scenarios
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
Workshop NotesInternational Workshop ``What can FCA do for Artificial Intelligence?'' (FCA4AI 2015)
International audienceThis volume includes the proceedings of the fourth edition of the FCA4AI --What can FCA do for Artificial Intelligence?-- Workshop co-located with the IJCAI 2015 Conference in Buenos Aires (Argentina). 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) which can be used for many AI needs, e.g. knowledge discovery, learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval and text processing. There are many ``natural links'' between FCA and AI, and the present workshop is organized for discussing about these links and more generally for improving the links between knowledge discovery based on FCA and knowledge management in artificial intelligence