期刊
INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS
卷 8, 期 3, 页码 42-73出版社
IGI GLOBAL
DOI: 10.4018/jswis.2012070103
关键词
Information Extraction; Knowledge-Base Construction; Machine Learning; Machine Reading; Natural Language Understanding; Statistical Inference; Systems
资金
- Defense Advanced Research Projects Agency (DARPA) Machine Reading Program under Air Force Research Laboratory (AFRL) [FA8750-09-C-0181]
- NSF CAREER award [IIS-1054009]
- ONR award [N000141210041]
- Google Inc.
- Greenplum
- Johnson Controls Inc.
- LogicBlox Inc.
- Oracle Corporation
Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary's approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.
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