基于Web弱指导的本体概念实例及属性的同步提取

Weakly-Supervised Extraction of Ontology Concept Instances and Concept Attributes from the Web

该文提出了一种基于Web弱指导的本体概念实例和属性的同步提取方法,利用小规模的种子实例和属性集,该文从Web上自动获取实例和属性共现的上下文模式,并利用种子实例和属性的关联性来评价这些模式。进一步,根据上下文模式提取候选概念实例和属性后,该文提出两种方法来评价提取的候选实例和属性。第一,利用概念实例和属性的关联性来互相评价对方的准确度;第二,利用候选实例或候选属性与种子实例或属性在上下文模式分布上的相似度来评价准确度。在疾病类实验结果表明,人工确认候选实例的准确率在前500个结果达到94%,前1 000个结果的准确率也高达93%。

In this paper, we propose a weakly-supervised method of extracting Ontology concept instances and attributes from the Web. We automatically acquire the co-occurrence patterns of the concept instances and attributes from the Web, and we evaluate these patterns based on the assumption that concept instances are relevant to their attributes. Furthermore, we extract the candidate concept instances and attributes. This paper proposes two ways to evaluate the accuracy of the candidate instances and attributes： the first measure is based on the correlation between concept instances and attributes, and the second one is based on the distribution similarity on the context patterns between the candidate instances （or attributes） and the seed instances （or attributes）. Experiments on disease domain show that the precision of the top 500 and 1 000 results reaches 94% and 93%, respectively.