Overall Objectives
Application Domains
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
Bibliography
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## Section: New Results

### Knowledge Engineering and Web of Data

Participants : Nacira Abbas, Alexandre Bazin, Miguel Couceiro, Adrien Coulet, Florence Le Ber, Pierre Monnin, Amedeo Napoli, Justine Reynaud, Yannick Toussaint.

A first research topic in this axis relies on knowledge discovery in the web of data. This follows the increase of data published in RDF (Resource Description Framework) format and the interest in machine processable data. The quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud. In the team, we are particularly interested in the completeness and the quality of data and their potential to provide concept definitions in terms of necessary and sufficient conditions [73], [74]. We have proposed a novel technique based on Formal Concept Analysis which classifies subsets of RDF data into a concept lattice. This allows data exploration as well as the discovery of implication rules which are used to automatically detect possible completions of RDF data and to provide definitions. Experiments on the DBpedia knowledge base show that this kind of approach is well-founded and effective [41] [10]. In addition, it should be noticed that this research work also involves redescription mining, showing the potential complementarity between definition mining and redescription mining.

The second topic in this axis is related to dependencies [77]. In the relational database model, functional dependencies (FDs) indicate a functional relation between sets of attributes: the values of a set of attributes are determined by the values of another set of attributes. FDs can be generalized into relational dependencies, also known as “link keys” in the web of data [76]. For example, link keys may identify the same book or article in different bibliographical data sources, where a link key is a statement of the form: $\left\{〈\mathrm{𝚊𝚞𝚝𝚎𝚞𝚛},\mathrm{𝚌𝚛𝚎𝚊𝚝𝚘𝚛}〉,〈\mathrm{𝚝𝚒𝚝𝚛𝚎},\mathrm{𝚝𝚒𝚝𝚕𝚎}〉\right\}\phantom{\rule{3.33333pt}{0ex}}linkkey\phantom{\rule{3.33333pt}{0ex}}〈\mathrm{𝙻𝚒𝚟𝚛𝚎},\mathrm{𝙱𝚘𝚘𝚔}〉$ stating that whenever an instance of the class Livre has the same values for properties auteur and titre as an instance of class Book has for properties creator and title , then they denote the same entity. Such link keys are more complex than FDs in databases in several respects and they raise new problems to solve [2].

One main objective of this research work is to follow the lines initiated in recent papers [29], and to extend to link keys the characterization of FDs and of Similarity Dependencies within FCA and pattern structures. Indeed, this is one of the objective of the ANR ELKER project. Accordingly, one purpose is to extend the initial proposals based on FCA and to provide adapted implementations. This is part of the thesis work of Nacira Abbas initiated at the end of 2018 [26]. Moreover, we are currently investigating possible connections with Relational Concept Analysis and redescription mining. We would like to study the formulation of the discovery of link keys in reusing and extending some construction heuristics that were developed in redescription mining. Actually, redescription mining is a data mining technique which aims at constructing pairs of descriptions, i.e., pairs of logical statements, one for each of two datasets, such that their support sets, i.e., the sets of objects that satisfy each statements of a pair, respectively, are most similar, as measured for example by their Jaccard index.