Library and Information Science

Library and Information Science ISSN: 2435-8495
三田図書館・情報学会 Mita Society for Library and Information Science
〒108‒8345 東京都港区三田2‒15‒45 慶應義塾大学文学部図書館・情報学専攻内 c/o Keio University, 2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
Library and Information Science 58: 49-67 (2007)

原著論文Original Article

引用箇所間の意味的な近さに基づく共引用の多値化列挙形式の引用を例としてMultivalued co-citation measure based on semantic distance between co-cited papers in a citing paper: A case study focused on enumeration of citations

慶應義塾大学大学院文学研究科Graduate School of Library and Information Science, Keio University ◇ 〒108-8345 東京都港区三田二丁目15番45号 ◇ Mita 2-15-45, Minato-ku, Tokyo 108-8345, Japan

受付日:2007年5月19日Received: May 19, 2007
受理日:2007年6月21日Accepted: June 21, 2007
発行日:2007年12月31日Published: December 31, 2007




Purpose: One typical document retrieval method is to use co-citation. The method is based on the premise that the degree of similarity among co-cited papers is equal in a particular paper. The degree is calculated with binary values: “co-cited” or “not co-cited”. To improve upon this method, the author proposes a multivalued co-citation measure based on semantic distance between co-cited papers.

Methods: To determine the distance between citations, the author measured two machine parseable relationships (location and citing words) between places where papers are cited. In order to evaluate the proposed method, we identified two categories of co-citation: a group with strong relationships indicating “enumerated co-citation” (papers cited within one statement) and a group with weak relationships showing “non enumerated co-citation”. Similarities within each group were calculated and compared using the CiteSeer dataset and 6 major similarity indicators.

Results: All of the similarity indicators showed that the degree of “enumerated co-citation” is higher than “non enumerated co-citation”. Consequently, it became clear that the proposed co-citation measure can be used to distinguish the strength of co-citation more precisely and that it can be applied to large-scale document collections.

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