TY - GEN
T1 - Attribute extraction and scoring
T2 - 29th International Conference on Data Engineering, ICDE 2013
AU - Lee, Taesung
AU - Wang, Zhongyuan
AU - Wang, Haixun
AU - Hwang, Seung Won
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84881339771&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881339771&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2013.6544825
DO - 10.1109/ICDE.2013.6544825
M3 - Conference contribution
AN - SCOPUS:84881339771
SN - 9781467349086
T3 - Proceedings - International Conference on Data Engineering
SP - 194
EP - 205
BT - ICDE 2013 - 29th International Conference on Data Engineering
Y2 - 8 April 2013 through 11 April 2013
ER -