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Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text

机译:维基百科结构中的特定领域语义相关性:以生物医学文本为例

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Wikipedia is becoming an important knowledge source in various domain specific applications based on concept representation. This introduces the need for concrete evaluation of Wikipedia as a foundation for computing semantic relatedness between concepts. While lexical resources like WordNet cover generic English well, they are weak in their coverage of domain specific terms and named entities, which is one of the strengths of Wikipedia. Furthermore, semantic relatedness methods that rely on the hierarchical structure of a lexical resource are not directly applicable to the Wikipedia link structure, which is not hierarchical and whose links do not capture well defined semantic relationships like hyponymy. In this paper we (1) Evaluate Wikipedia in a domain specific semantic relatedness task and demonstrate that Wikipedia based methods can be competitive with state of the art ontology based methods and distributional methods in the biomedical domain (2) Adapt and evaluate the effectiveness of bibliometric methods of various degrees of sophistication on Wikipedia (3) Propose a new graph-based method for calculating semantic relatedness that outperforms existing methods by considering some specific features of Wikipedia structure.
机译:Wikipedia正在成为基于概念表示的各种特定领域应用程序中的重要知识来源。这就需要对维基百科进行具体评估,以此作为计算概念之间语义相关性的基础。尽管诸如WordNet之类的词汇资源很好地覆盖了通用英语,但是它们对特定领域术语和命名实体的覆盖却很薄弱,这是Wikipedia的优势之一。此外,依赖于词汇资源的层次结构的语义相关性方法不能直接应用于Wikipedia链接结构,该链接结构不是层次结构,并且其链接无法捕获明确的语义关系,如下位音。在本文中,我们(1)在特定领域的语义相关性任务中评估Wikipedia,并证明基于Wikipedia的方法可以与生物医学领域中基于现有本体的方法和分布方法相竞争(2)适应并评估文献计量学的有效性Wikipedia上各种复杂程度的方法(3)提出了一种新的基于图的方法来计算语义相关性,该方法通过考虑Wikipedia结构的某些特定特征而优于现有方法。

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