Knowledge Base Population: Successful Approaches and Challenges

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1148–1158, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Knowledge Base Population: Successful Approaches and Challenges Heng Ji Ralph Grishman Computer Science Department Computer Science Department Queens College and Graduate Center City University of New York New York University New York, NY 11367, USA New York, NY 10003, USA hengji@cs.qc.cuny.edu grishman@cs.nyu.edu Abstract In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discov- ering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking – link- ing names in context to entities in the KB – and Slot Filling – adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover informa- tion. Attributes (“slots”) derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with tra- ditional Information Extraction (IE) and Ques- tion Answering (QA) tasks, and provide some suggestions to address these challenges. 1 Introduction Traditional information extraction (IE) evaluations, such as the Message Understanding Conferences (MUC) and Automatic Content Extraction (ACE), assess the ability to extract information from indi- vidual documents in isolation. In practice, how- ever, we may need to gather information about a person or organization that is scattered among the documents of a large collection. This requires the ability to identify the relevant documents and to integrate facts, possibly redundant, possibly com- plementary, possibly in conflict, coming from these documents. Furthermore, we may want to use the extracted information to augment an existing data base. This requires the ability to link indi- viduals mentioned in a document, and information about these individuals, to entries in the data base. On the other hand, traditional Question Answering (QA) evaluations made limited efforts at disam- biguating entities in queries (e.g. Pizzato et al., 2006), and limited use of relation/event extraction in answer search (e.g. McNamee et al., 2008). The Knowledge Base Population (KBP) shared task, conducted as part of the NIST Text Analysis Conference, aims to address and evaluate these capabilities, and bridge the IE and QA communi- ties to promote research in discovering facts about entities and expanding a knowledge base with these facts. KBP is done through two separate sub- tasks, Entity Linking and Slot Filling; in 2010, 23 teams submitted results for one or both sub-tasks. A variety of approaches have been proposed to address both tasks with considerable success; nev- ertheless, there are many aspects of the task that remain unclear. What are the fundamental tech- niques used to achieve reasonable performance? What is the impact of each novel method? What types of problems are …

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