Tue 9 Jan 2018 14:00 - 14:30 at Rose - Answer Set Programming

Semantic resources (WordNet, Wikidata, BabelNet, …) offer invaluable knowledge that can be exploited by humans and machines to solve a variety of tasks. Among these, we address here the one called entity set expansion: extend a given a set of words –called seeds– with new ones being of the same “sort”. Differently from classical approaches, we determine “optimal” common categories of the given seeds by analyzing the semantic relations among the objects these seeds refer to. In particular, we define the notion of entity network to integrate information from different semantic resources, and show how to use such networks to disambiguate word senses. Finally, we propose a proof-of-concept implementation in answer set programming with external predicates to query online semantic resources and perform optimization tasks.