Summarizing Entity Descriptions for Effective and Efficient Human-centered Entity Linking

Abstract

Entity linking connects the Web of documents with knowledge bases. It is the task of linking an entity mention in text to its corresponding entity in a knowledge base. Whereas a large body of work has been devoted to automatically generating candidate entities, or ranking and choosing from them, manual efforts are still needed, e.g., for defining gold-standard links for evaluating automatic approaches, and for improving the quality of links in crowdsourcing approaches. However, structured descriptions of entities in knowledge bases are sometimes very long. To avoid overloading human users with too much information and help them more efficiently choose an entity from candidates, we aim to substitute entire entity descriptions with compact, equally effective structured summaries that are automatically generated. To achieve it, our approach analyzes entity descriptions in the knowledge base and the context of entity mention from multiple perspectives, including characterizing and differentiating power, information overlap, and relevance to context. Extrinsic evaluation (where human users carry out entity linking tasks) and intrinsic evaluation (where human users rate summaries) demonstrate that summaries generated by our approach help human users carry out entity linking tasks more efficiently (22-23% faster), without significantly affecting the quality of links obtained; and our approach outperforms existing approaches to summarizing entity descriptions.