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Topological Features Based Entity Disambiguation

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Abstract

This work proposes an unsupervised topological features based entity disambiguation solution. Most existing studies leverage semantic information to resolve ambiguous references. However, the semantic information is not always accessible because of privacy or is too expensive to access. We consider the problem in a setting that only relationships between references are available. A structure similarity algorithm via random walk with restarts is proposed to measure the similarity of references. The disambiguation is regarded as a clustering problem and a family of graph walk based clustering algorithms are brought to group ambiguous references. We evaluate our solution extensively on two real datasets and show its advantage over two state-of-the-art approaches in accuracy.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Chen-Chen Sun, De-Rong Shen, Yue Kou, Tie-Zheng Nie & Ge Yu

Authors
  1. Chen-Chen Sun
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  2. De-Rong Shen
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  3. Yue Kou
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  4. Tie-Zheng Nie
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  5. Ge Yu
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Corresponding author

Correspondence to Chen-Chen Sun.

Additional information

This work is supported by the National Basic Research 973 Program of China under Grant No. 2012CB316201, the Fundamental Research Funds for the Central Universities of China under Grant No. N120816001, and the National Natural Science Foundation of China under Grant Nos. 61472070 and 61402213.

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Sun, CC., Shen, DR., Kou, Y. et al. Topological Features Based Entity Disambiguation. J. Comput. Sci. Technol. 31, 1053–1068 (2016). https://doi.org/10.1007/s11390-016-1679-6

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  • DOI: https://doi.org/10.1007/s11390-016-1679-6

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