A Bootstrapping Approach to Entity Linkage on the Semantic Web

Wei Hu, Cunxin Jia


In the Big Data era, ever-increasing RDF data have reached a scale in billions
of entities and brought challenges to the problem of entity linkage on the Semantic
Web. Although millions of entities, typically denoted by URIs, have been explicitly linked with owl:sameAs, potentially coreferent ones are still numerous. Existing automatic approaches address this problem mainly from two perspectives: one is via equivalence reasoning, which infers semantically coreferent entities but probably misses many potentials; the other is by similarity computation between property-values of entities, which is not always accurate and do not scale well. In this paper, we introduce a bootstrapping approach by leveraging these two kinds of methods for entity linkage. Given an entity, our approach firstly infers a set of semantically coreferent entities. Then, it iteratively expands this entity set using discriminative propertyvalue pairs. The discriminability is learned with a statistical measure, which does not only identify important property-values in the entity set, but also takes matched properties into account. Frequent property combinations are also mined to improve linkage accuracy. We develop an online entity linkage
search engine, and show its superior precision and recall by comparing with representative approaches on a large-scale and two benchmark datasets.

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Type of Paper: Research Paper
Keywords: Entity linkage, Bootstrapping, Discriminative property, Linked Data, Semantic Web
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