FCA4AI 2023
IJCAI 2023
The ten preceding editions of the FCA4AI Workshop (see http://www.fca4ai.hse.ru/ ) showed that many researchers working in Articial Intelligence are deeply interested by a well-founded method for classication and data mining such as Formal Concept Analysis (see https://upriss.github.io/fca/fca.html ).
The FCA4AI Workshop Series started with ECAI 2012 (Montpellier) and the last edition was co-located with IJCAI-ECAI 2022 (Vienna, Austria). The FCA4AI workshop has now a long history and all proceedings are available as CEUR proceedings (see http://ceur-ws. org/, volumes 939, 1058, 1257, 1430, 1703, 2149, 2529, 2729, 2972, and 3233). This year, the workshop has again attracted researchers from dierent countries working on actual and important topics related to FCA, showing the diversity and the richness of the relations between FCA and AI.
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classication. FCA allows one to build a concept lattice and a system of dependencies, i.e., implications and association rules, which can be used for many AI needs, e.g. knowledge discovery, machine learning, knowledge representation and reasoning, natural language and text processing. Recent years have been witnessing increased scientic activity around FCA. In particular an important line of work is aimed at extending the possibilities of FCA w.r.t. data and knowledge processing, and dealing with complex data. These extensions open new directions for AI practitioners. Accordingly, the workshop will investigate the following issues:
How can FCA support AI activities such as knowledge discovery, knowledge representation and reasoning, machine learning, natural language processing, information retrieval. . .
How can FCA be extended for helping AI researchers to solve new and complex problems, in particular how to combine FCA and neural classiers for allowing interpretability and producing valuable explanations. . .
First of all we would like to thank all the authors for their contributions and all the PC members for their reviews and their precious collaboration. The papers submitted to the workshop were carefully peer-reviewed by three members of the program committee. The order of the papers in the proceedings (see table of contents in page 5) follows the program of the workshop (see http://fca4ai.hse.ru/2023/ ).
Copyright '2023 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
1
Knowledge Base pattern structures-based Classication of underground forums: A case study
Abdulrahim Ghazal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Interval pattern structures for interpreting K-nearest neighbor approach in lazy classication
Alan Tomat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3 4 5 6 7
Using Multimodal Clustering on Formal Contexts in Event Extraction with Neural Nets Mikhail Bogatyrev, Dmitry Orlov, and Ilya Zapyantsev . . . . . . . . . . . . . 25 Full polynomial probabilistic FCA-based knowledge extraction Dmitry V. Vinogradov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 A Note on Counting Basic Choice Functions with Formal Concept Analysis Dmitry I. Ignatov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Egor Dudyrev, Sergei O. Kuznetsov, and Amedeo Napoli . . . . . . . . . . . . 69