Keyword Query Routing

Описание: Title: Keyword Query Routing

Domain: Data Mining

Abstract:

Keyword search is an intuitive paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. We propose a novel method for computing top-k routing plans based on their potentials to contain results for a given keyword query. We employ a keyword-element relationship summary that compactly represents relationships between keywords and the data elements mentioning them. A multilevel scoring mechanism is proposed for computing the relevance of routing plans based on scores at the level of keywords, data elements, element sets, and subgraphs that connect these elements. Experiments carried out using 150 publicly available sources on the web showed that valid plans (precision@1 of 0.92) that are highly relevant (mean reciprocal rank of 0.89) can be computed in 1 second on average on a single PC. Further, we show routing greatly helps to improve the performance of keyword search, without compromising its result quality.

Key Features:

1. We propose to investigate the problem of keyword query routing for keyword search over a large number of structured and Linked Data sources. Routing keywords only to relevant sources can reduce the high cost of searching for structured results that span multiple sources. To the best of our knowledge, the work presented in this paper represents the first attempt to address this problem.

2. Existing work uses keyword relationships (KR) collected individually for single databases .We represent relationships between keywords as well as those between data elements. They are\constructed for the entire collection of linked sources, and then grouped as elements of a compact summary called the set-level keyword-element relationship graph (KERG). Summarizing relationships is essential for addressing the scalability requirement of the Linked Data web scenario.

3. IR-style ranking has been proposed to incorporate relevance at the level of keywords. To cope with the increased keyword ambiguity in the web setting, we employ a multilevel relevance model, where elements to be considered are keywords, entities mentioning these keywords, corresponding sets of entities, relationships between elements of the same level, and inter-relationships between elements of different levels.

4. We implemented the approach and evaluated it in a real-world setting using more than 150 publicly available data sets. The results show the applicability of this approach: valid plans (precision@1 ¼ 0.92) that are highly relevant to the user information need (mean reciprocal rank (RR) ¼ 0.86) can be computed in 1 second on average using a commodity PC. Further, we show that when routing is applied to an existing keyword search system to prune sources, substantial performance gain can be achieved.

5. The web is no longer only a collection of textual documents but also a web of interlinked data sources (e.g., Linked Data). One prominent project that largely contributes to this development is Linking Open Data.

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