Context-aware Entity Linking with Attentive Neural Networks on Wikidata
Knowledge Graph
(2019)cite arxiv:1912.06214Comment: 16 pages.Abstract
The Entity Linking (EL) approaches have been a long-standing research field
and find applicability in various use cases such as semantic search, text
annotation, question answering, etc. Although effective and robust, current
approaches are still limited to particular knowledge repositories (e.g.
Wikipedia) or specific knowledge graphs (e.g. Freebase, DBpedia, and YAGO). The
collaborative knowledge graphs such as Wikidata excessively rely on the crowd
to author the information. Since the crowd is not bound to a standard protocol
for assigning entity titles, the knowledge graph is populated by non-standard,
noisy, long or even sometimes awkward titles. The issue of long, implicit, and
nonstandard entity representations is a challenge in EL approaches for gaining
high precision and recall. In this paper, we advance the state-of-the-art
approaches by developing a context-aware attentive neural network approach for
entity linking on Wikidata. Our approach contributes by exploiting the
sufficient context from a Knowledge Graph as a source of background knowledge,
which is then fed into the neural network. This approach demonstrates merit to
address challenges associated with entity titles (multi-word, long, implicit,
case-sensitive). Our experimental study shows $\approx$8\% improvements over
the baseline approach, and significantly outperform an end to end approach for
Wikidata entity linking. This work, first of its kind, opens a new direction
for the research community to pay attention to developing context-aware EL
approaches for collaborative knowledge graphs.
Links and resources
Tags
community
@ch's tags highlighted