Attribute extraction and scoring: A probabilistic approach

• Published in: 2013 IEEE 29th International Conference on Data Engineering (ICDE) pp. 194 - 205
• Main Authors: Taesung Lee, Zhongyuan Wang, Haixun Wang, Seung-won Hwang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2013
• Subjects: Companies; Data mining; Knowledge based systems; Probabilistic logic; Sociology; Statistics; Syntactics
• ISBN: 9781467349093; 1467349097
• ISSN: 1063-6382; 2375-026X
• DOI: 10.1109/ICDE.2013.6544825

Knowledge bases, which consist of concepts, entities, attributes and relations, are increasingly important in a wide range of applications. We argue that knowledge about attributes (of concepts or entities) plays a critical role in inferencing. In this paper, we propose methods to derive attributes for millions of concepts and we quantify the typicality of the attributes with regard to their corresponding concepts. We employ multiple data sources such as web documents, search logs, and existing knowledge bases, and we derive typicality scores for attributes by aggregating different distributions derived from different sources using different methods. To the best of our knowledge, ours is the first approach to integrate concept- and instance-based patterns into probabilistic typicality scores that scale to broad concept space. We have conducted extensive experiments to show the effectiveness of our approach.