Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Mining Web Data for Competency Management
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
ESTER: efficient search on text, entities, and relations
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
EntityRank: searching entities directly and holistically
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Query modeling for entity search based on terms, categories, and examples
ACM Transactions on Information Systems (TOIS)
A ranking framework for entity oriented search using Markov random fields
Proceedings of the 1st Joint International Workshop on Entity-Oriented and Semantic Search
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Entity ranking on Web scale datasets is still an open challenge. Several resources, as for example Wikipedia-based ontologies, can be used to improve the quality of the entity ranking produced by a system. In this paper we focus on the Wikipedia corpus and propose algorithms for finding entities based on query relaxation using category information. The main contribution is a methodology for expanding the user query by exploiting the semantic structure of the dataset. Our approach focuses on constructing queries using not only keywords from the topic, but also information about relevant categories. This is done leveraging on a highly accurate ontology which is matched to the character strings of the topic. The evaluation is performed using the INEX 2007 Wikipedia collection and entity ranking topics. The results show that our approach performs effectively, especially for early precision metrics.