Document retrieval method using random walk with restart on weighted co-citation network
Document retrieval method using random walk with restart on weighted co-citation network
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DOI：
10.1002/meet.2014.14505101126
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Masaki Eto
中科院分区：
文献类型：
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作者：
Masaki Eto
本文提出了一种基于图的加权共引网络检索技术，使用户能够更容易地从共引网络中找到更多相关文献。更具体地说，带重启的随机游走技术被应用于文献的加权图，其中每个边权重的程度由共引文献的数量和共引上下文的强度来衡量；两者都是通过分析引用文献的全文而获得的。为了经验性地评估其有效性，从PubMed Central的开放获取子集创建了一个特殊的测试集，并将该方法的搜索性能与传统的共引搜索进行了比较，结果表明，该方法的搜索精度为k。实验结果表明，该方法在不降低查准率的情况下，能够检索到更多的相关文档。
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 co-citation 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 co-citation searching by “precision at k.” The experimental results indicate that the proposed method tends to retrieve much more relevant documents without reducing precision.