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KEIPD: Knowledge Extraction and Inference System for Personal Documents

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9932))

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Abstract

Public personal documents on the Internet, such as resumes and personal homepages, may imply social relationships among people, which is of great value in various applications. This paper presents KEIPD, a system to extract and infer knowledge from personal documents. KEIPD employs a tree-similarity based approach to extract information from personal documents to obtain a relational network of entities. Then the inference of social relationships can be transformed into a link prediction problem. KEIPD implements some popular unsupervised predictors for link prediction and prune the candidate entity pairs based on the domain-dependent constraint.

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Notes

  1. 1.

    http://baike.baidu.com.

  2. 2.

    http://www.people.com.cn.

References

  1. Che, W., Li, Z., Liu, T.: LTP: a Chinese language technology platform. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations. pp. 13–16. Association for Computational Linguistics (2010)

    Google Scholar 

  2. Davis, D., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks. In: 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 281–288. IEEE (2011)

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  3. Zhang, M., Su, J., Wang, D., Zhou, G., Tan, C.-L.: Discovering relations between named entities from a large raw corpus using tree similarity-based clustering. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 378–389. Springer, Heidelberg (2005)

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, China

    Zhaoyang Lv, Yuanyuan Liu & Xiaohui Yu

  2. School of Information Technology, York University, Toronto, ON, Canada

    Xiaohui Yu

Authors
  1. Zhaoyang Lv
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  2. Yuanyuan Liu
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  3. Xiaohui Yu
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Corresponding author

Correspondence to Xiaohui Yu .

Editor information

Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

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© 2016 Springer International Publishing Switzerland

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Cite this paper

Lv, Z., Liu, Y., Yu, X. (2016). KEIPD: Knowledge Extraction and Inference System for Personal Documents. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_72

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  • DOI: https://doi.org/10.1007/978-3-319-45817-5_72

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45816-8

  • Online ISBN: 978-3-319-45817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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