Extended co-citation search: Graph-based document retrieval on a co-citation network containing citation context information

This study proposes a novel extended co-citation search technique, which is graph-based document retrieval on a co-citation network containing citation context information. The proposed search expands the scope of the target documents by repetitively spreading the relationship of co-citation in order to obtain relevant documents that are not identified by traditional co-citation searches. Specifically, this search technique is a combination of (a) applying a graph-based algorithm to compute the similarity score on a complicated network, and (b) incorporating co-citation contexts into the process of calculating similarity scores to reduce the negative effects of an increasing number of irrelevant documents. To evaluate the search performance of the proposed search, 10 proposed methods (five representative graph-based algorithms applied to co-citation networks weighted with/without contexts) are compared with two kinds of baselines (a traditional co-citation search with/without contexts) in information retrieval experiments based on two test collections (biomedicine and computer linguistic articles). The experiment results showed that the scores of the normalized discounted cumulative gain (nDCG@K) of the proposed methods using co-citation contexts tended to be higher than those of the baselines. In addition, the combination of the random walk with restart (RWR) algorithm and the network weighted with contexts achieved the best search performance among the 10 proposed methods. Thus, it is clarified that the combination of graph-based algorithms and co-citation contexts are effective in improving the performance of co-citation search techniques, and that sole use of a graph-based algorithm is not enough to enhance search performances from the baselines.