Welcome to the InfoSci Platform
Reference Hub57
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference

Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference

Feng Niu, Ce Zhang, Christopher Ré, Jude Shavlik
Copyright: © 2012 |Volume: 8 |Issue: 3 |Pages: 32
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466614871|DOI: 10.4018/jswis.2012070103
Cite Article Cite Article

MLA

Niu, Feng, et al. "Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference." IJSWIS vol.8, no.3 2012: pp.42-73. http://doi.org/10.4018/jswis.2012070103

APA

Niu, F., Zhang, C., Ré, C., & Shavlik, J. (2012). Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference. International Journal on Semantic Web and Information Systems (IJSWIS), 8(3), 42-73. http://doi.org/10.4018/jswis.2012070103

Chicago

Niu, Feng, et al. "Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference," International Journal on Semantic Web and Information Systems (IJSWIS) 8, no.3: 42-73. http://doi.org/10.4018/jswis.2012070103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.