Finding Relevant Relations in Relevant Documents

Abstract

Abstract. This work studies the combination of a document retrieval and a relation extraction system for the purpose of identifying query-relevant relational facts. On the TREC Web collection, we assess extracted facts separately for correctness and relevance. Despite some TREC topics not being covered by the relation schema, we find that this approach reveals relevant facts, and in particular those not yet known in the knowledge base DBpedia. The study confirms that mention frequency, document relevance, and entity relevance are useful indicators for fact relevance. Still, the task remains an open research problem.

A Relation Extraction Approach for Clinical Decision Support

“…Regarding relations in IR, [18] study the problem of finding human readable descriptions of a given relationship in a knowledge graph. [16] apply supervised relation extraction to documents that are relevant for an information need Q and study how many of the extracted relations are indeed relevant for Q. [8] explores current state of the art in unsupervised relation extraction (OpenIE) for the task of finding support passages to complement an entity ranking with human-readable explanations of how those retrieved entities are connected to the information need.…”