Do We Need Entity-Centric Knowledge Bases for Entity Disambiguation?

Entity Disambiguation has been studied extensively in the last 10 years with authors reporting increasingly well per- forming systems. However, most studies focused on gen- eral purpose knowledge bases like Wikipedia or DBPedia and left out the question how those results generalize to more specialized domains. This is especially important in the context of Linked Open Data which forms an enormous resource for disambiguation. However, the influence of do- main heterogeneity, size and quality of the knowledge base remains largely unanswered. In this paper we present an extensive set of experiments on special purpose knowledge bases from the biomedical domain where we evaluate the disambiguation performance along four variables: (i) the representation of the knowledge base as being either entity- centric or document-centric, (ii) the size of the knowledge base in terms of entities covered, (iii) the semantic het- erogeneity of a domain and (iv) the quality and complete- ness of a knowledge base. Our results show that for spe- cial purpose knowledge bases (i) document-centric disambig- uation significantly outperforms entity-centric disambigua- tion, (ii) document-centric disambiguation does not depend on the size of the knowledge-base, while entity-centric ap- proaches do, and (iii) disambiguation performance varies greatly across domains. These results suggest that domain- heterogeneity, size and knowledge base quality have to be carefully considered for the design of entity disambiguation systems and that for constructing knowledge bases user- annotated texts are preferable to carefully constructed know- ledge bases