Zhishi.me - Weaving Chinese Linking Open Data

  • Xing Niu
  • Xinruo Sun
  • Haofen Wang
  • Shu Rong
  • Guilin Qi
  • Yong Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7032)

Abstract

Linking Open Data (LOD) has become one of the most important community efforts to publish high-quality interconnected semantic data. Such data has been widely used in many applications to provide intelligent services like entity search, personalized recommendation and so on. While DBpedia, one of the LOD core data sources, contains resources described in multilingual versions and semantic data in English is proliferating, there is very few work on publishing Chinese semantic data. In this paper, we present Zhishi.me, the first effort to publish large scale Chinese semantic data and link them together as a Chinese LOD (CLOD). More precisely, we identify important structural features in three largest Chinese encyclopedia sites (i.e., Baidu Baike, Hudong Baike, and Chinese Wikipedia) for extraction and propose several data-level mapping strategies for automatic link discovery. As a result, the CLOD has more than 5 million distinct entities and we simply link CLOD with the existing LOD based on the multilingual characteristic of Wikipedia. Finally, we also introduce three Web access entries namely SPARQL endpoint, lookup interface and detailed data view, which conform to the principles of publishing data sources to LOD.

Keywords

Link Data Semantic Data Internal Link External Link Link Open Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xing Niu
    • 1
  • Xinruo Sun
    • 1
  • Haofen Wang
    • 1
  • Shu Rong
    • 1
  • Guilin Qi
    • 2
  • Yong Yu
    • 1
  1. 1.APEX Data & Knowledge Management LabShanghai Jiao Tong UniversityChina
  2. 2.Southeast UniversityChina

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