Formal Concept Analysis (FCA) provides a knowledge discovery framework enabling both (1) conceptual clustering of data objects and (2) pattern/association discovery. It was thought as a mathematical approach to the design of concept hierarchies (called concept lattices) from a sets of observations (introduced as object x attribute tables, called formal contexts). FCA, as most data mining approaches focuses on a single data table, whereas Linked Data are inherently multi-table, a.k.a. multi-relational. Relational Concept Analysis (RCA) is a Multi-relational data mining (MRDM) method extending FCA.
RCA has been applied to practical problems from a wide range of fields such as software engineering, hydroecology, neurology, data interlinking, linguistics. In this tutorial, we will focus on the way RCA can support various ontology engineering tasks. First we bring to the audience an understanding of the mathematical foundations of the RCA method and the algorithms used in the iterative lattice construction. We present existing tools as well as examples of RCA applications from the literature. In the second part, the focus will be on the intricate links between RCA and ontologies. We present a small number of ontology engineering scenarios and show how RCA-based tools support them through proper analysis of the data.
Petko Valtchev is Associate Professor with the Computer Science department of University of Quebec at Montreal(UQAM). His Ph.D. was awarded in 1999 by J. Fourier University, Grenoble. He is member of the Editorial Board of the International conference on Formal Concept Analysis (FCA) and has served as a member of the program committees of top-tier conferences (AAAI, IJCAI, ISWC). He has been researching on knowledge discovery and data mining with/from ontologies and knowledge bases. In this context, he designed a number of methods and practical tools exploiting concept analysis.
Mickael Wajnberg is a student, currently enrolled in a PhD at University of Quebec at Montreal (Québec, Canada) and at Université de Lorraine (France) , he currently works on RCA and knowledge extraction. He did a Math and Physics Prepa before he got an Engineering Degree (M. Sc equivalent) at Telecom Nancy(France) and a M. Sc at University of Quebec at Chicoutimi (Québec, Canada) in Computer Science, he specialized in algorithms and theory for computer science.