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Exploiting Relevance Feedback in Knowledge Graph Search

Published: 10 August 2015 Publication History

Abstract

The big data era is witnessing a prevalent shift of data from homogeneous to heterogeneous, from isolated to linked. Exemplar outcomes of this shift are a wide range of graph data such as information, social, and knowledge graphs. The unique characteristics of graph data are challenging traditional search techniques like SQL and keyword search. Graph query is emerging as a promising complementary search form. In this paper, we study how to improve graph query by relevance feedback. Specifically, we focus on knowledge graph query, and formulate the graph relevance feedback (GRF) problem. We propose a general GRF framework that is able to (1) tune the original ranking function based on user feedback and (2) further enrich the query itself by mining new features from user feedback. As a consequence, a query-specific ranking function is generated, which is better aligned with the user search intent. Given a newly learned ranking function based on user feedback, we further investigate whether we shall re-rank the existing answers, or choose to search from scratch. We propose a strategy to train a binary classifier to predict which action will be more beneficial for a given query. The GRF framework is applied to searching DBpedia with graph queries derived from YAGO and Wikipedia. Experiment results show that GRF can improve the mean average precision by 80% to 100%.

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    cover image ACM Conferences
    KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 2015
    2378 pages
    ISBN:9781450336642
    DOI:10.1145/2783258
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    Published: 10 August 2015

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    Author Tags

    1. graph query
    2. knowledge graph
    3. relevance feedback

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    KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;
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    • (2020)Knowledge graph explorationACM SIGWEB Newsletter10.1145/3409481.34094852020:Summer(1-8)Online publication date: 27-Jul-2020
    • (2020)BIBLIOGRAPHYModern Big Data Architectures10.1002/9781119597926.biblio(161-177)Online publication date: 7-Apr-2020
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