A logic-based approach to named-entity disambiguation in the web of data

Semantic annotation aims at linking parts of rough data (e.g., text, video, or image) to known entities in the Linked Open Data (LOD) space. When several entities could be linked to a given object, a Named-Entity Disambiguation (NED) problem must be solved. While disambiguation has been extensively studied in Natural Language Understanding (NLU), NED is less ambitious—it does not aim to the meaning of a whole phrase, just to correctly link objects to entities—and at the same time more peculiar since the target must be LOD-entities. Inspired by semantic similarity in NLU, this paper illustrates a way to solve disambiguation based on Common Subsumers of pairs of RDF resources related to entities recognized in the text. The inference process proposed for resolving ambiguities leverages on the DBpedia structured semantics. We apply it to a TV-program description enrichment use case, illustrating its potential in correcting errors produced by automatic text annotators (such as errors in assigning entity types and entity URIs), and in extracting a description of the main topics of a text in form of commonalities shared by its entities.