ACM SIGIR Forum
Why finding entities in Wikipedia is difficult, sometimes
Information Retrieval
Query modeling for entity search based on terms, categories, and examples
ACM Transactions on Information Systems (TOIS)
Category-based query modeling for entity search
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Estimating query difficulty for news prediction retrieval
Proceedings of the 21st ACM international conference on Information and knowledge management
Example based entity search in the web of data
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
An exploration of ranking models and feedback method for related entity finding
Information Processing and Management: an International Journal
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Entity ranking has recently emerged as a research field that aims at retrieving entities as answers to a query. Unlike entity extraction where the goal is to tag the names of the entities in documents, entity ranking is primarily focused on returning a ranked list of relevant entity names for the query. Many approaches to entity ranking have been proposed, and most of them were evaluated on the INEX Wikipedia test collection. In this paper, we show that the knowledge of predicted classes of topic difficulty can be used to further improve the entity ranking performance. To predict the topic difficulty, we generate a classifier that uses features extracted from an INEX topic definition to classify the topic into an experimentally pre-determined class. This knowledge is then utilised to dynamically set the optimal values for the retrieval parameters of our entity ranking system. Our experiments suggest that topic difficulty prediction is a promising approach that could be exploited to improve the effectiveness of entity ranking.