Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
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Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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Entity search has emerged as an important research topic over the past years, but so far has only been addressed in a centralized setting. In this paper we present an attempt to solve the task of ad-hoc entity retrieval in a cooperative distributed environment. We propose a new collection ranking and selection method for entity search, called AENN. The key underlying idea is that a lean, name-based representation of entities can efficiently be stored at the central broker, which, therefore, does not have to rely on sampling. This representation can then be utilized for collection ranking and selection in a way that the number of collections selected and the number of results requested from each collection is dynamically adjusted on a per-query basis. Using a collection of structured datasets in RDF and a sample of real web search queries targeting entities, we demonstrate that our approach outperforms state-of-the-art distributed document retrieval methods in terms of both effectiveness and efficiency.