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Section: Research Program

Knowledge Systems and Web of Data


knowledge engineering, web of data, semantic web, ontology, description logics, classification-based reasoning, case-based reasoning, information retrieval.

The web of data constitutes a good platform for experimenting ideas on knowledge engineering and knowledge discovery. Following the principles of semantic web, a software agent may be able to read, understand, and manipulate information on the web, if and only if the knowledge necessary for achieving those tasks is available: this is why knowledge bases (domain ontologies) are of main importance. OWL is the knowledge representation language used to design ontologies and knowledge bases, which is based on description logics (DLs [68]). In OWL, knowledge units are represented by classes (DL concepts) having properties (DL roles) and instances. Concepts can be organized within a partial order based on a subsumption relation, and the inference services are based on satisfiability, classification-based reasoning and case-based reasoning (CBR).

Actually, there are many interconnections between concept lattices in FCA and ontologies, e.g. the partial order underlying an ontology can be supported by a concept lattice. Moreover, a pair of implications within a concept lattice can be adapted for designing concept definitions in ontologies. Accordingly, we are interested here in two main challenges: how the web of data, as a set of potential knowledge sources (e.g. DBpedia, Wikipedia, Yago, Freebase) can be mined for helping the design of definitions and knowledge bases and how knowledge discovery techniques can be applied for providing a better usage of the web of data (e.g. LOD classification).

Accordingly, a part of the research work in Knowledge Engineering is oriented towards knowledge discovery in the web of data, as, with the increased interest in machine processable data, more and more data is now published in RDF (Resource Description Framework) format. Particularly, we are interested in the completeness of the data and their potential to provide concept definitions in terms of necessary and sufficient conditions [69]. We have proposed algorithms based on FCA and Redescription Mining which allow data exploration as well as the discovery of definition (bidirectional implication rules).