Cluster-based patent retrieval

  • Authors:

  • In-Su Kang;Seung-Hoon Na;Jungi Kim;Jong-Hyeok Lee

  • Affiliations:

  • Korea Institute of Science and Technology Information, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc), Republic of Korea;Division of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc), Republic of Korea;Division of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc), Republic of Korea;Division of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH), Advanced Information Technology Research Center (AITrc), Republic of Korea

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2007

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Abstract

Through the recent NTCIR workshops, patent retrieval casts many challenging issues to information retrieval community. Unlike newspaper articles, patent documents are very long and well structured. These characteristics raise the necessity to reassess existing retrieval techniques that have been mainly developed for structure-less and short documents such as newspapers. This study investigates cluster-based retrieval in the context of invalidity search task of patent retrieval. Cluster-based retrieval assumes that clusters would provide additional evidence to match user's information need. Thus far, cluster-based retrieval approaches have relied on automatically-created clusters. Fortunately, all patents have manually-assigned cluster information, international patent classification codes. International patent classification is a standard taxonomy for classifying patents, and has currently about 69,000 nodes which are organized into a five-level hierarchical system. Thus, patent documents could provide the best test bed to develop and evaluate cluster-based retrieval techniques. Experiments using the NTCIR-4 patent collection showed that the cluster-based language model could be helpful to improving the cluster-less baseline language model.