Team, Visitors, External Collaborators
Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: Research Program

Knowledge Systems and Web of Data

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

The web of data constitutes a good platform for experimenting ideas on knowledge engineering (KE) and knowledge discovery. 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 domain knowledge and ontologies are of main importance. OWL (“Web Ontology Language” is based on description logics (DLs [72]) and is the representation language commonly used for designing ontologies. In OWL, knowledge units are represented by classes having properties and instances. Concepts are organized within a partially ordered set based on a subsumption relation, and the inference services are based on subsumption and classification.

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 provide a possible materialization of a concept definition in an ontology. In this way, we study how the web of data, considered as a set of knowledge sources, e.g. DBpedia, Wikipedia, Yago, Freebase, can be mined for guiding the design of a knowledge base, and further, how knowledge discovery techniques can be applied for allowing a better usage of the web of data, e.g. Linked Open Data (LOD) classification and completion.

Then, 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. We have proposed algorithms based on FCA and Redescription Mining which allow data exploration as well as the discovery of definition (bidirectional implication rules).