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Background: The Entity Linking (EL) task links entity mentions from an unstructured document to entities in a knowledge base. Although this problem is well-studied in news and social media, this problem has not received much attention in the life science domain. One outcome of tackling the EL problem in the life sciences domain is to enable scientists to build computational models of biological processes with more efficiency. However, simply applying a news-trained entity linker produces inadequate results.

Linking named mentions detected in a source document to an existing knowledge base provides disambiguated entity referents for the mentions.This allows better document analysis, knowledge extraction and knowledge base population. Most of the previous research extensively exploited the linguistic features of the source documents in a supervised or semi-supervised way. These systems therefore cannot be easily applied to a new language or domain. In this paper, we present a novel unsupervised algorithm named Quantified Collective Validation that avoids excessive linguistic analysis on the source documents and fully leverages the knowledge base structure for the entity linking task. We show our approach achieves stateof-the-art English entity linking performance and demonstrate successful deployment in a new language (Chinese) and two new domains (Biomedical and Earth Science).Experiment datasets and system demonstration are available at http://tw.rpi.edu/web/doc/ hanwang_emnlp_2015 for research purpose.

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