Abstract
Semantic association search is to search an entity-relation graph for subgraphs called semantic associations that connect a set of entities specified in a user’s query. Recent research on this topic has concentrated on summarizing numerous search results by mining their important patterns to form an abstractive overview. However, top-ranked patterns may have redundancy, and their graph structure may not be comprehensible to non-expert users. To reduce redundancy, we present a novel framework featuring a combinatorial optimization model to select top-k diversified patterns. In particular, we devise a new similarity measure which jointly considers structural and semantic similarity to assess the overlap between patterns. To facilitate non-expert users’ comprehension of a pattern, we verbalize its graph structure, transforming it into compact and coherent English text based on a novel method for discourse planning. Extensive experiments demonstrate the effectiveness of our approach compared with existing methods.
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Acknowledgement
This work is supported in part by the NSFC under Grants 61572247 and 61772264, and in part by the Qing Lan and Six Talent Peaks Programs of Jiangsu Province.
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Gu, Y., Liang, Y., Cheng, G., Liu, D., Wei, R., Qu, Y. (2018). Diversified and Verbalized Result Summarization for Semantic Association Search. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11233. Springer, Cham. https://doi.org/10.1007/978-3-030-02922-7_26
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DOI: https://doi.org/10.1007/978-3-030-02922-7_26
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