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Optimizing model parameter for entity summarization across knowledge graphs

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Abstract

Knowledge graphs, which belongs to the category of semantic networks, are considered as a new method of knowledge representation of health care data. It establishes a semantic explanation model for human perception and health care information processing. Each knowledge graph is composed of massive entities and relationships. However, it is an arduous work to search and visualize users’ interested entities and attributes since there are many attributes for an entity across different knowledge graphs. It is a natural problem how to summarize an entity based on multiple knowledge graphs. We propose a three-stage algorithm to solve the problem of entity summarization across knowledge graphs, including candidate generation, knowledge graph linkage, and entity summarization. We propose an unsupervised framework to link different knowledge graphs based on the semantic and structure information of entities. To further reduce the computational cost, we employ word embedding technique to find the similar entities in semantic, and filter some pairs of unmatched entities. Finally, we model entity summarization as personalized ranking problem in a knowledge graph. We conduct a set of experiments to evaluate our proposed method on four real datasets: historical data for user query, two English knowledge graphs (YAGO and DBpeida) and an English corpus. Experimental results demonstrate the effectiveness of our proposed method by comparing with baselines.

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Notes

  1. http://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/yagonaga/yago/.

  2. http://nlp.stanford.edu/software/segmenter.shtml.

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Acknowledgements

This work has been supported by the National Key Research and Development Program of China under Grant 2016YFB1000905, NSFC under Grant Nos. U1401256, 61672234, 61402180 and 61472321.The author would also like to thank Key Disciplines of Software Engineering of Shanghai Polytechnic University under Grant No. XXKZD1604.

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Authors and Affiliations

  1. Institute for Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Na Li, Ming Gao & Aoying Zhou

  2. College of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai, 201209, China

    Jihong Yan

  3. Information Center, Shanghai Agricultural Committee, Shanghai, 200335, China

    Chen Xu

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  1. Jihong Yan
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  2. Chen Xu
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  3. Na Li
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  4. Ming Gao
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  5. Aoying Zhou
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Correspondence to Ming Gao.

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Yan, J., Xu, C., Li, N. et al. Optimizing model parameter for entity summarization across knowledge graphs. J Comb Optim 37, 293–318 (2019). https://doi.org/10.1007/s10878-017-0225-y

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