SASL: A Semantic Annotation System for Literature

  • Authors:

  • Pingpeng Yuan;Guoyin Wang;Qin Zhang;Hai Jin

  • Affiliations:

  • Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074

  • Venue:
  • WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
  • Year:
  • 2009

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Abstract

Due to ambiguity, search engines for scientific literatures may not return right search results. One efficient solution to the problems is to automatically annotate literatures and attach the semantic information to them. Generally, semantic annotation requires identifying entities before attaching semantic information to them. However, due to abbreviation and other reasons, it is very difficult to identify entities correctly. The paper presents a Semantic Annotation System for Literature (SASL), which utilizes Wikipedia as knowledge base to annotate literatures. SASL mainly attaches semantic to terminology, academic institutions, conferences, and journals etc. Many of them are usually abbreviations, which induces ambiguity. Here, SASL uses regular expressions to extract the mapping between full name of entities and their abbreviation. Since full names of several entities may map to a single abbreviation, SASL introduces Hidden Markov Model to implement name disambiguation. Finally, the paper presents the experimental results, which confirm SASL a good performance.