A high-level electrical energy ontology with weighted attributes |
| |
Affiliation: | 1. Department of Building, Civil & Environmental Engineering, Concordia University, 1515 Ste-Catherine Street West, Montréal, Quebec, H3G 2W1, Canada;2. Concordia Institute for Information Systems Engineering, Concordia University, 1515 Ste-Catherine Street West, Montréal, Quebec H3G 2W1, Canada;1. Colorado State University, Department of Microbiology, Immunology & Pathology, 1682 Campus Delivery, Fort Collins, CO, 80523, USA;2. University of Queensland, Australian Infectious Diseases Research Centre, St. Lucia, Qld, 4072 & School of Veterinary Science, Gatton Campus, Qld, 4343, Australia;3. University of Padova, Department Molecular Medicine, Via Gabelli 63, 35121, Padova, Italy |
| |
Abstract: | One of the significant application areas of domain ontologies is known to be text analysis applications like information extraction and text classification systems, and semantic portals. In this paper, we present a high-level ontology for the electrical energy domain. This domain ontology has weighted attributes to cover the inherent fuzziness in the textual representations of its concepts. Additionally, we have included in the ontology the necessary attributes to align the ontology concepts to on-line collaborative knowledge bases like Wikipedia and linked open data sources like DBpedia, other attributes to facilitate its use in multilingual applications, and concepts to hold the named entities in the domain. The ultimate ontology is aligned with the previously proposed ontologies for the energy-related subdomains after extending the latter ones with weighted attributes. We make the ultimate form of the electrical energy ontology, as well as the extended versions of the domain ontologies for the subdomains, available for research purposes. Also included in the paper are sample text analysis applications which mainly exploit the weighted attributes within the ontology. |
| |
Keywords: | Domain ontology Electrical energy Weighted attributes Ontology learning Wikipedia Text analysis applications |
本文献已被 ScienceDirect 等数据库收录! |
|