Taxonomy induction based on a collaboratively built knowledge repository
Taxonomy induction based on a collaboratively built knowledge repository
复制标题
DOI：
10.1016/j.artint.2011.01.003
复制
发表时间：
2011-06
期刊：
影响因子：
--
通讯作者：
Simone Paolo Ponzetto;M. Strube
中科院分区：
文献类型：
--
作者：
Simone Paolo Ponzetto;M. Strube
维基百科中的分类系统可以看作是一个概念网络。我们使用基于网络连通性和词汇句法匹配的方法来标注类别之间的语义关系。其结果是一个大规模的分类学。对于评估，我们提出了一种方法，该方法(1)手动确定分类的质量，(2)自动将其覆盖范围与最大的手动创建的本体之一ResearchCyc和词汇数据库WordNet进行比较。此外，我们通过计算基准数据集中单词之间的语义相似度来执行外部评估。结果表明，该分类法在质量和覆盖率上均优于人工创建的覆盖面广的资源。
The category system in Wikipedia can be taken as a conceptual network. We label the semantic relations between categories using methods based on connectivity in the network and lexico-syntactic matching. The result is a large scale taxonomy. For evaluation we propose a method which (1) manually determines the quality of our taxonomy, and (2) automatically compares its coverage with ResearchCyc, one of the largest manually created ontologies, and the lexical database WordNet. Additionally, we perform an extrinsic evaluation by computing semantic similarity between words in benchmarking datasets. The results show that the taxonomy compares favorably in quality and coverage with broad-coverage manually created resources.