CAJ | 학술논문

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1000个结果的准确率也高达93%.
해문제출료일충기우Web약지도적본체개념실례화속성적동보제취방법,이용소규모적충자실례화속성집,해문종Web상자동획취실례화속성공현적상하문모식,병이용충자실례화속성적관련성래평개저사모식.진일보,근거상하문모식제취후선개념실례화속성후,해문제출량충방법래평개제취적후선실례화속성.제일,이용개념실례화속성적관련성래호상평개대방적준학도;제이,이용후선실례혹후선속성여충자실례혹속성재상하문모식분포상적상사도래평개준학도.재질병류실험결과표명,인공학인후선실례적준학솔재전500개결과체도94%,전1000개결과적준학솔야고체93%.
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 1000 results reaches 94% and 93%, respectively.