Learning-based relevance feedback for web-based relation completion

ABSTRACT

In a pilot application based on web search engine called Web-based Relation Completion (WebRC), we propose to join two columns of entities linked by a predefined relation by mining knowledge from the web through a web search engine. To achieve this, a novel retrieval task Relation Query Expansion (RelQE) is modelled: given an entity (query), the task is to retrieve documents containing entities in predefined relation to the given one. Solving this problem entails expanding the query before submitting it to a web search engine to ensure that mostly documents containing the linked entity are returned in the top K search results. In this paper, we propose a novel Learning-based Relevance Feedback (LRF) approach to solve this retrieval task. Expansion terms are learned from training pairs of entities linked by the predefined relation and applied to new entity-queries to find entities linked by the same relation. After describing the approach, we present experimental results on real-world web data collections, which show that the LRF approach always improves the precision of top-ranked search results to up to 8.6 times the baseline. Using LRF, WebRC also shows performances way above the baseline.

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Index Terms

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

      • Relevance assessment

    • Retrieval models and ranking

• Security and privacy

  • Database and storage security

• Theory of computation

  • Theory and algorithms for application domains

    • Database theory

      • Theory of database privacy and security