Exploiting Relevance Feedback in Knowledge Graph Search-Reference-Cited by-同舟云学术

Exploiting Relevance Feedback in Knowledge Graph Search

Published:2015-08-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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Author:

Su Yu¹,Yang Shengqi¹,Sun Huan¹,Srivatsa Mudhakar²,Kase Sue³,Vanni Michelle³,Yan Xifeng¹

Affiliation:

1. University of California, Santa Barbara, Santa Barbara, CA, USA

2. IBM Research, Yorktown Heights, NY, USA

3. U.S. Army Research Laboratory, Adelphi, MD, USA

Funder

National Science Foundation

Army Research Laboratory

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2783258.2783320

Reference42 articles.

1. Google Knowledge Graph. http://www.google.com/insidesearch/features/search/knowledge.html. Google Knowledge Graph. http://www.google.com/insidesearch/features/search/knowledge.html.

2. JOptimizer. http://www.joptimizer.com. JOptimizer. http://www.joptimizer.com.

3. SPARQL 1.1. http://www.w3.org/TR/sparql11-overview. SPARQL 1.1. http://www.w3.org/TR/sparql11-overview.

4. TREC : relevance feedback track. http://trec.nist.gov/data/relevance.feedback.html. TREC: relevance feedback track. http://trec.nist.gov/data/relevance.feedback.html.

5. Query Difficulty, Robustness, and Selective Application of Query Expansion

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