Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1464-1469.
Abstract
Building ontologies is a difficult task requiring skills in logics and ontological analysis. Domain experts usually reach as far as organizing a set of concepts into a hierarchy in which the semantics of the relations is under-specified. The categorization of Wikipedia is a huge concept hierarchy of this form, covering a broad range of areas. We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia. The method first selects a subset of concepts that are relevant for a given domain. The relevant concepts are subsequently split into classes and individuals, and, finally, the relations between the concepts are classified into subclass_of, instance_of, part_of, and generic related_to. We evaluate our method by generating ontology skeletons for the domains of Computing and Music. The quality of the generated ontologies has been measured against manually built ground truth datasets of several hundred nodes.
@inproceedings{ijcai2015p210,
title = {Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach},
author = {Mirylenka, Daniil and Passerini, Andrea and Serafini, Luciano},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1464--1469},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/210},
}
TY - CPAPER TI - Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach AU - Mirylenka, Daniil AU - Passerini, Andrea AU - Serafini, Luciano T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1464 EP - 1469 UR - https://ijcai.org/proceedings/2015/210 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: