정보관리학회지 (Journal of the Korean Society for information Management)

  • 제26권2호
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  • Pages.127-147
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  • 2009
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  • 1013-0799(pISSN)
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  • 2586-2073(eISSN)
한국정보관리학회 (Korean Society for Information Management)
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딜리셔스에서 유사태그 추출에 관한 연구

Mining Semantically Similar Tags from Delicious

  • Yi, Kwan (School of Library and Information Science, University of Kentucky)
  • 발행 : 2009.06.30
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초록

자연언어에서 유사어의 처리는 사람과 컴퓨터간의 의사소통에 적지 않은 장애가 되어왔고, 이는 사용자의 임의적 단어사용에 기반을 두고 있는 웹 2.0 애플리케이션, 특히 소셜태깅 분야에 있어서 그 장애의 정도가 더 심각해질 수 있다. 본 연구는 한 대표적인 웹 2.0 애플리케이션에서 자동 유사어 추출에 관한 문제를 다루고 있다. 더 구체적으로, 가장 널리 사용되는 소셜북마킹 애플리케이션인 딜리셔스를 기반으로, 유사태그를 추출하는 방법(FolkSim)을 제시하고자 한다. 제시한 방법의 평가를 위하여, 문서유사도의 측정을 위해서 쓰여진 고전적 벡터모델에 의거한 유사태그를 추출하는 방법(CosSim)과 그 결과들을 서로 비교분석하여 보았다. 몇 가지 면에서 FolkSim가 더 나은 결과 산출해내는 증거들이 관찰되어졌다. 또한, FolkSim 방법에 의한 유사태그가 만들어지지 않는 경우에 대비하여, 그 대안 또한 제시하고 있다.

The synonym issue is an inherent barrier in human-computer communication, and it is more challenging in a Web 2.0 application, especially in social tagging applications. In an effort to resolve the issue, the goal of this study is to test the feasibility of a Web 2.0 application as a potential source for synonyms. This study investigates a way of identifying similar tags from a popular collaborative tagging application, Delicious. Specifically, we propose an algorithm (FolkSim) for measuring the similarity of social tags from Delicious. We compared FolkSim to a cosine-based similarity method and observed that the top-ranked tags on the similar list generated by FolkSim tend to be among the best possible similar tags in given choices. Also, the lists appear to be relatively better than the ones created by CosSim. We also observed that tag folksonomy and similar list resemble each other to a certain degree so that it possibly serves as an alternative outcome, especially in case the FolkSim-based list is unavailable or infeasible.

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피인용 문헌

  1. An empirical study on the automatic resolution of semantic ambiguity in social tags vol.48, pp.1, 2011, https://doi.org/10.1002/meet.2011.14504801175
  2. A Comparative Study on Clustering Methods for Grouping Related Tags vol.43, pp.3, 2009, https://doi.org/10.4275/KSLIS.2009.43.3.399