Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia

Yingjie Hu, K. Janowicz, S. Prasad
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引用次数: 36

Abstract

Place name disambiguation is an important task for improving the accuracy of geographic information retrieval. This task becomes more challenging when the input texts are short. Wikipedia provides information about places and has often been employed for named entity recognition. However, the natural language representation of Wikipedia articles limits more effective use of this rich knowledge base. DBpedia is the Semantic Web version of Wikipedia, which provides structured and machine-understandable knowledge mined from Wikipedia articles. This paper presents an approach for combining Wikipedia and DBpedia to disambiguate place names in short texts. We discuss the pros and cons of the two knowledge bases, and argue that a combination of both performs better than each of them alone. We evaluate our proposed method by conducting experiments against baselines of three established methods. The result indicates that our method has a generally higher precision and recall. While our study employs DBpedia, the proposed method is generic and can be extended to other structured Linked Datasets such as Freebase or Wikidata.
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使用来自DBpedia的结构化数据改进短文本中基于维基百科的地名消歧
地名消歧是提高地理信息检索精度的一项重要任务。当输入文本较短时,这项任务变得更具挑战性。维基百科提供有关地点的信息,并经常用于命名实体识别。然而,维基百科文章的自然语言表示限制了对这个丰富知识库的更有效使用。DBpedia是维基百科的语义Web版本,它提供从维基百科文章中挖掘的结构化和机器可理解的知识。本文提出了一种结合Wikipedia和DBpedia来消除短文本中地名歧义的方法。我们讨论了这两个知识库的优点和缺点,并认为两者的组合比单独使用它们中的任何一个表现更好。我们通过对三种既定方法的基线进行实验来评估我们提出的方法。结果表明,该方法具有较高的查准率和查全率。虽然我们的研究使用了DBpedia,但所提出的方法是通用的,可以扩展到其他结构化的关联数据集,如Freebase或Wikidata。
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