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

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.