TY - GEN
T1 - Automatic acquisition of attributes for ontology construction
AU - Cui, Gaoying
AU - Lu, Qin
AU - Li, Wenjie
AU - Chen, Yirong
PY - 2009/11/9
Y1 - 2009/11/9
N2 - An ontology can be seen as an organized structure of concepts according to their relations. A concept is associated with a set of attributes that themselves are also concepts in the ontology. Consequently, ontology construction is the acquisition of concepts and their associated attributes through relations. Manual ontology construction is time-consuming and difficult to maintain. Corpus-based ontology construction methods must be able to distinguish concepts themselves from concept instances. In this paper, a novel and simple method is proposed for automatically identifying concept attributes through the use of Wikipedia as the corpus. The built-in {{Infobox}} in Wiki is used to acquire concept attributes and identify semantic types of the attributes. Two simple induction rules are applied to improve the performance. Experimental results show precisions of 92.5% for attribute acquisition and 80% for attribute type identification. This is a very promising result for automatic ontology construction.
AB - An ontology can be seen as an organized structure of concepts according to their relations. A concept is associated with a set of attributes that themselves are also concepts in the ontology. Consequently, ontology construction is the acquisition of concepts and their associated attributes through relations. Manual ontology construction is time-consuming and difficult to maintain. Corpus-based ontology construction methods must be able to distinguish concepts themselves from concept instances. In this paper, a novel and simple method is proposed for automatically identifying concept attributes through the use of Wikipedia as the corpus. The built-in {{Infobox}} in Wiki is used to acquire concept attributes and identify semantic types of the attributes. Two simple induction rules are applied to improve the performance. Experimental results show precisions of 92.5% for attribute acquisition and 80% for attribute type identification. This is a very promising result for automatic ontology construction.
KW - Attribute acquisition
KW - Ontology construction
KW - Wikipedia as resource source
UR - http://www.scopus.com/inward/record.url?scp=70350639609&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-00831-3_23
DO - 10.1007/978-3-642-00831-3_23
M3 - Conference article published in proceeding or book
SN - 3642008305
SN - 9783642008306
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 248
EP - 259
BT - Computer Processing of Oriental Languages
T2 - 22nd International Conference on Computer Processing of Oriental Languages, ICCPOL 2009
Y2 - 26 March 2009 through 27 March 2009
ER -