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This paper proposes a measure that uses a spread co-citation relationship for document retrieval. To clarify whether this proposed measure has potential for enhancing the search performance of co-citation searching, two retrieval methods are evaluated: one uses the relationship directly; the other incorporates the co-citation context. Experiments with a special test collection comprising about 152,000 documents are conducted. Results indicate that this relationship tends to be able to detect relevant documents which are undetectable using a traditional co-citation relationship, and that using context has a positive effect to reduce the number of noise documents.

This paper proposes a graph-based retrieval technique on a weighted co-citation network, which allows users to find more relevant documents easily from the co-citation network. More specifically, the random walk with restart technique is applied to a weighted graph of documents, in which the degree of each edge weight is measured by the number of co-citation documents and the strength of the cocitation context; both obtained by parsing the full text of the citing documents. To evaluate its effectiveness empirically, a special test collection was created from the Open Access Subset of PubMed Central, and the search performance of the proposed method was compared with traditional cocitation searching by "precision at k." The experimental results indicate that the proposed method tends to retrieve much more relevant documents without reducing precision.

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In the field of academic document search, citations are often used for measuring implicit relationships between documents. Recently, some studies have attempted to extend co-citation searching. However, these studies mainly focus on comparisons of traditional co-citation and extended co-citation search methods; combination effects of word-based and extended co-citation search algorithms have not yet been sufficiently evaluated. This paper empirically evaluates the search performance of the combination search by using a test collection comprising about 152,000 documents and a metric 'precision at k.' The experimental results indicate that the combination search outperforms two baseline methods: a wordbased search and a combination search of word-based and traditional co-citation search algorithms.