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Summarizing Vocabularies in the Global Semantic Web

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Abstract

In the Semantic Web, vocabularies are defined and shared among knowledge workers to describe linked data for scientific, industrial or daily life usage. With the rapid growth of online vocabularies, there is an emergent need for approaches helping users understand vocabularies quickly. In this paper, we study the summarization of vocabularies to help users understand vocabularies. Vocabulary summarization is based on the structural analysis and pragmatics statistics in the global Semantic Web. Local Bipartite Model and Expanded Bipartite Model of a vocabulary are proposed to characterize the structure in a vocabulary and links between vocabularies. A structural importance for each RDF sentence in the vocabulary is assessed using link analysis. Meanwhile, pragmatics importance of each RDF sentence is assessed using the statistics of instantiation of its terms in the Semantic Web. Summaries are produced by extracting important RDF sentences in vocabularies under a re-ranking strategy. Preliminary experiments show that it is feasible to help users understand a vocabulary through its summary.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Xiang Zhang, Gong Cheng, Wei-Yi Ge & Yu-Zhong Qu

Authors
  1. Xiang Zhang
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  2. Gong Cheng
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  3. Wei-Yi Ge
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  4. Yu-Zhong Qu
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Corresponding author

Correspondence to Xiang Zhang.

Additional information

The work is supported in part by the National Basic Research 973 Program of China under Grant No. 2003CB317004 and the National Natural Science Foundation of China under Grant No. 60773106.

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Zhang, X., Cheng, G., Ge, WY. et al. Summarizing Vocabularies in the Global Semantic Web. J. Comput. Sci. Technol. 24, 165–174 (2009). https://doi.org/10.1007/s11390-009-9212-9

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  • DOI: https://doi.org/10.1007/s11390-009-9212-9

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