ABSTRACT
Web search engines often retrieve answers for queries about popular entities from a growing knowledge base that is populated by a continuous information extraction process. However, less popular entities are not frequently mentioned on the web and are generally interesting to fewer users; these entities reside on the long tail of information. Traditional knowledge base construction techniques that rely on the high frequency of entity mentions to extract accurate facts about these mentions have little success with entities that have low textual support. We present Lonlies, a system for estimating property values of long tail entities by leveraging their relationships to head topics and entities. We demonstrate (1) how Lonlies builds communities of entities that are relevant to a long tail entity utilizing a text corpus and a knowledge base; (2) how Lonlies determines which communities to use in the estimation process; (3) how we aggregate estimates from community entities to produce final estimates, and (4) how users interact with Lonlies to provide feedback to improve the final estimation results.
- M. S. Bernstein, J. Teevan, S. Dumais, D. Liebling, and E. Horvitz. Direct Answers for Search Queries in the Long Tail. In SIGCHI, 2012. Google ScholarDigital Library
- K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD, 2008. Google ScholarDigital Library
- J. Callan, M. Hoy, C. Yoo, and L. Zhao. Clueweb09 data set, 2009.Google Scholar
- X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang. Knowledge Vault: A Web-scale Approach to Probabilistic Knowledge Fusion. In SIGKDD, 2014. Google ScholarDigital Library
- C. Manning and D. Klein. Optimization, maxent models, and conditional estimation without magic. In NAACL - Tutorials '03, Stroudsburg, PA, USA, 2003. Association for Computational Linguistics. Google ScholarDigital Library
- M. E. Newman and M. Girvan. Finding and evaluating community structure in networks. Physical review E, 69(2), 2004.Google Scholar
- F. Niu, C. Zhang, C. Ré, and J. W. Shavlik. Deepdive: Web-scale knowledge-base construction using statistical learning and inference. VLDS, 12:25--28, 2012.Google Scholar
- E. H. Simpson. Measurement of diversity. Nature, 1949.Google Scholar
Index Terms
- LONLIES: Estimating Property Values for Long Tail Entities
Recommendations
A search based approach to entity recognition: magnetic and IISAS team at ERD challenge
ERD '14: Proceedings of the first international workshop on Entity recognition & disambiguationERD 2014 was a research challenge focused on the task of recognition and disambiguation of knowledge base entities in short and long texts. This write-up describes Magnetic-IISAS team's approach to the entity recognition in search queries with which we ...
Two-stage approach to named entity recognition using Wikipedia and DBpedia
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and CommunicationIn natural language understanding, extraction of named entity (NE) mentions in given text and classification of the mentions into pre-defined NE types are important processes. Most NE recognition (NER) relies on resources such as a training corpus or NE ...
Concordance-based entity-oriented search
We consider the problem of finding relevant named entities in response to a search query over a given text corpus. Entity search can readily be used to augment conventional web search engines for a variety of applications. We use entity concordance ...
Comments