Federated Entity Search Using On-the-Fly Consolidation

Nowadays, search on the Web goes beyond the retrieval of textual Web sites and increasingly takes advantage of the growing amount of structured data. Of particular interest is entity search, where the units of retrieval are structured entities instead of textual documents. These entities reside in different sources, which may provide only limited information about their content and are therefore called “uncooperative”. Further, these sources capture complementary but also redundant information about entities. In this environment of uncooperative data sources, we study the problem of federated entity search, where redundant information about entities is reduced on-the-fly through entity consolidation performed at query time. We propose a novel method for entity consolidation that is based on using language models and completely unsupervised, hence more suitable for this on-the-fly uncooperative setting than state-of-the-art methods that require training data. Further, we apply the same language model technique to deal with the federated search problem of ranking results returned from different sources. Particular novel are the mechanisms we propose to incorporate consolidation results into this ranking. We perform experiments using real Web queries and data sources. Our experiments show that our approach for federated entity search with on-the-fly consolidation improves upon the performance of a state-of-the-art preference aggregation baseline and also benefits from consolidation.