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摘要
As academic disciplines are segmented and specialized, it becomes more difficult to capture relevant research areas precisely by common retrieval strategies using either keywords or journal categories. This paper proposes a method of measuring the relatedness among sets of academic papers in order to detect unrelated communities which are not related to target topic. A citation network, extracted by given keywords, is divided into communities based on the density of links. We measured and compared four measures of relatedness between two communities in a citation network for three large-scale citation datasets. We used both link and semantic similarities. The topological distance from the center in a citation network is a more efficient measure for removing the unrelated communities than the other three measures: the ratio of the number of intercluster links over the all links, the ratio of the number of common terms over all terms, cosine similarity of tf-idf vectors.
- 出版日期2011-7
