HLT '02 Proceedings of the second international conference on Human Language Technology Research
Ad-hoc object retrieval in the web of data
Proceedings of the 19th international conference on World wide web
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
On the modeling of entities for ad-hoc entity search in the web of data
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
Combining inverted indices and structured search for ad-hoc object retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Entity ranking has become increasingly important, both for retrieving structured entities and for use in general web search applications. The most common format for linked data, RDF graphs, provide extensive semantic structure via predicate links. While the semantic information is potentially valuable for effective search, the resulting adjacency matrices are often sparse, which introduces challenges for representation and ranking. In this paper, we propose a principled and scalable approach for integrating of latent semantic information into a learning-to-rank model, by combining compact representation of semantic similarity, achieved by using a modified algorithm for tensor factorization, with explicit entity information. Our experiments show that the resulting ranking model scales well to the graphs with millions of entities, and outperforms the state-of-the-art baseline on realistic Yahoo! SemSearch Challenge data sets.