A methodology to learn ontological attributes from the Web
Class descriptors such as attributes, features or meronyms are rarely considered when developing ontologies. Even WordNet only includes a reduced amount of part-of relationships. However, these data are crucial for defining concepts such as those considered in classical knowledge representation models. Some attempts have been made to extract those relations from text using general meronymy detection patterns; however, there has been very little work on learning expressive class attributes (including associated domain, range or data values) at an ontological level. In this paper we take this background into consideration when proposing and implementing an automatic, non-supervised and domain-independent methodology to extend ontological classes in terms of learning concept attributes, data-types, value ranges and measurement units. In order to present a general solution and minimize the data sparseness of pattern-based approaches, we use the Web as a massive learning corpus to retrieve data and to infer information distribution using highly contextualized queries aimed at improving the quality of the result. This corpus is also automatically updated in an adaptive manner according to the knowledge already acquired and the learning throughput. Results have been manually checked by means of an expert-based concept-per-concept evaluation for several well distinguished domains showing reliable results and a reasonable learning performance.
attributes features meronyms ontology learning web mining knowledge acquisition