Combining Concept Annotation and Pattern Structures for Guiding Ontology Mapping

Pierre Monnin (1) , Amedeo Napoli (1) , Adrien Coulet (1, 2)

1 ORPAILLEUR - Knowledge representation, reasonning
2 BMIR - Stanford Center for BioMedical Informatics Research

Formal Concept Analysis (FCA) is a mathematical framework classifying in formal concepts a set of objects w.r.t. their common attributes. To this aim, FCA relies on a binary incidence relationship indicating whether an object has an attribute. On one hand, in order to consider more complex descriptions for objects (e.g., intervals, graphs), FCA has been extended with Pattern Structures. On the other hand, in a previous work, we introduced the notion of Concept Annotation, adding a third dimension to formal concepts, computed over the extent, without modifying the original classification. In this paper, we combine Concept Annotation with the formalism of Pattern Structures and we consider multiple annotation possibilities, i.e., multiple annotations for one concept and computing the annotation over the intent. We illustrate our approach and its interest with two use cases: (i) suggesting mappings between ontology classes and (ii) finding specific classes frequently associated as domain and range of a predicate.

Mots clés: Formal Concept Analysis; Pattern Structures; Concept Annotation

Domaines: Base de données [cs.DB]; Intelligence artificielle [cs.AI]

Pierre Monnin, Amedeo Napoli, Adrien Coulet. Combining Concept Annotation and Pattern Structures for Guiding Ontology Mapping. FCA4AI@IJCAI2018 - 6th International Workshop "What can FCA do for Artificial Intelligence?", Jul 2018, Stockholm, Sweden. ⟨hal-01858391⟩