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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