Abstract
Entity summarization aims at selecting a small subset of attribute-value pairs of an entity from a knowledge graph, which provides users with concrete information given an entity-related query. However, previous approaches focus on the “goodness” of the attribute-value pairs, paying little attention to user preference towards them.
In this paper, we formalize the task of context-aware entity summarization, and propose an algorithm to solve this problem. We model user interest by mining the latent topics in a query log dataset. A modified Personalized PageRank algorithm is utilized to rank attribute-value pairs by leveraging three elements: relevance, informativeness and topic coherence. We evaluate our approach on real-world datasets and show that it outperforms the state-of-the-art approaches.
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Acknowledgements
This work is supported by the National Basic Research Program (973) of China (No. 2012CB316203) and NSFC under Grant Nos. U1401256, 61402177, 61402180 and 61232002. This work is also supported by CCF-Tecent Research Program of China (No. AGR20150114) and NSF of Shanghai (No. 14ZR1412600).
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Yan, J., Wang, Y., Gao, M., Zhou, A. (2016). Context-Aware Entity Summarization. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9658. Springer, Cham. https://doi.org/10.1007/978-3-319-39937-9_40
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DOI: https://doi.org/10.1007/978-3-319-39937-9_40
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