基于WordNet的本体澄清

Ontology Clarification by Using WordNet

由于本体能够消除概念的混淆和重用知识,因此它的质量对于语义网技术的应用非常重要。为了提高本体的质量,很多的工作集中在概念建模,但是本体表示这个非常重要的方面一直被忽视。目前本体的表示使用的是词(term),但同一个词可能有很多不同的意思,这样在基于本体的应用时将导致不清楚或错误的理解。为了解决这个问题,使用定义在WordNet中的词义(sense)而不是词来作为本体的表示,其原因是词义只有唯一的意思。本体澄清的定义为利用目标词周围的本体元素和被它标注的文档附近的词,对目标词进行自动消歧的过程。通过计算目标词义和它的邻居词的语义相似度,语义相关度最大的词义将选为正确的词义。实验表明,我们的算法有很好的性能。与最好的消歧算法相比,概念(Concept)精度差不多是名词精度的2倍,关系(Property)精度差不多是动词精度的3倍。实验证明了我们的算法在半自动的本体净化过程中也是非常有效的。

Semantic Web technology highly depends on the quality of ontology as it reduces or eliminates conceptual confusion and reuses knowledge. In order to enhance quality of ontology, a vast amount of research has focused on concept modeling task, but there is one major problem with lexical representation of ontology. Current lexical representation is term which may have different meanings; this can result in frustrating misunderstanding and ambiguity during the management and application of ontology. To solve this problem, sense is used to replace term as the lexical representation of concepts and properties for its unique meaning. We call ontology clarification the process of automatically disambiguating terms in ontology by using its surrounding ontology elements and its nearby terms in annotated documents using this ontology. The right sense is assigned to a target term by maximizing the relatedness between the target and its neighbors for semantic relatedness between them. Experiments show our ontology clarification method has good performance. Comparing with the best word sense disambiguation method, the concept precision is almost 2 times than the precision of noun,and the property precision is almost 3 times than the precision of verb. The last experiment proves that our method is also effective in a semi-automatic ontology clarification process.