Navigating the topical structure of academic search results via the Wikipedia category network.
CIKM'13: 22nd ACM International Conference on Information and Knowledge Management San Francisco California USA October, 2013(2013)
摘要
Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. We propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step we map the search results to articles and categories of Wikipedia, constructing a graph of relevant topics with hierarchical relations. In the second step we sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, our method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.
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