A Cognitive Contribution to Entity Representation and Matching

The problem of Data Linkage in the Semantic Web can be divided in two lines of action: schema and ontology matching/mapping, which allows us to draw conclusions about sets of individuals through concept relations, and entity-level linkage, where more information can be reached from distributed sources because of the fact that the information is about the same entity.While the area of schema and ontology matching is traditionally much addressed, it appears that today the SemanticWeb looks very much like a collection of “information islands” that are very poorly integrated with each other, especially on the individual level; and when some of these islands are linked, this is often the result of a lot of hard and time-consuming manual work. The general problem we are working on is to provide a structured approach of how to improve the situation of data linkage at the level of individuals in the Web of Data. As a specific contribution, in this article we describe an empirical investigation about how humans describe individuals (or entities), by analyzing a feature-listing experiment performed by a large sample of participants. We propose a measure of relevance to analyze the results, and apply the findings to the specific problem of entity matching in a large entity repository, by proposing a novel approach for entity matching/alignment. We show in a first experimental evaluation that such an approach, which takes into account the cognitive point of view of entity representation by humans, can provide an improvement over other relevant approaches.