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Mining RDF from Tables in Chinese Encyclopedias

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Natural Language Processing and Chinese Computing (NLPCC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9362))

Abstract

Web tables understanding has recently attracted a number of studies. However, many works focus on the tables in English, because they usually need the help of knowledge bases, while the existing knowledge bases such as DBpedia, YAGO, Freebase and Probase mainly contain knowledge in English.

In this paper, we focus on the RDF triples extraction from tables in Chinese encyclopedias. Firstly, we constructed a Chinese knowledge base through taxonomy mining and class attribute mining. Then, with the help of our knowledge base, we extracted triples from tables through column scoring, table classification and RDF extraction. In our experiments, we practically implemented our approach in 6,618,544 articles from Hudong Baike with 764,292 tables, and extracted about 1,053,407 unique and new RDF triples with an estimated accuracy of \(90.2\%\), which outperforms other similar works.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Weiming Lu, Zhenyu Zhang, Renjie Lou, Hao Dai, Shansong Yang & Baogang Wei

Authors
  1. Weiming Lu
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  2. Zhenyu Zhang
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  3. Renjie Lou
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  4. Hao Dai
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  5. Shansong Yang
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  6. Baogang Wei
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Corresponding author

Correspondence to Weiming Lu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Rensselaer Polytechnic Institute, Troy, NY, USA

    Heng Ji

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Yansong Feng

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Lu, W., Zhang, Z., Lou, R., Dai, H., Yang, S., Wei, B. (2015). Mining RDF from Tables in Chinese Encyclopedias. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-25207-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25206-3

  • Online ISBN: 978-3-319-25207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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