The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Applications of Ontology-Based Opinion Mining
|
|
Author(s): Razia Sulthana (SRM Institute of Science and Technology, India)and Subburaj Ramasamy (SRM Institute of Science and Technology, India)
Copyright: 2019
Pages: 29
Source title:
Extracting Knowledge From Opinion Mining
Source Author(s)/Editor(s): Rashmi Agrawal (Manav Rachna International Institute of Research and Studies, India)and Neha Gupta (Manav Rachna International Institute of Research and Studies, India)
DOI: 10.4018/978-1-5225-6117-0.ch008
Purchase
|
Abstract
Ontology provides a technique to formulate and present queries to databases either stand-alone or web-based. Ontology has been conceived to produce reusable queries to extract rules matching them, and hence, it saves time and effort in creating new ontology-based queries. Ontology can be incorporated in the machine learning process, which hierarchically defines the relationship between concepts, axioms, and terms in the domain. Ontology rule mining has been found to be efficient as compared to other well-known rule mining methods like taxonomy and decision trees. In this chapter, the authors carry out a detailed survey about ontology-related information comprising classification, creation, learning, reuse, and application. The authors also discuss the reusability and the tools used for reusing ontology. Ontology has a life cycle of its own similar to the software development life cycle. The classification-supervised machine learning technique and clustering and the unsupervised machine learning are supported by the ontology. The authors also discuss some of the open issues in creation and application of ontology.
Related Content
|
.
© 2023.
34 pages.
|
|
.
© 2023.
15 pages.
|
|
.
© 2023.
15 pages.
|
|
.
© 2023.
18 pages.
|
|
.
© 2023.
24 pages.
|
|
.
© 2023.
32 pages.
|
|
.
© 2023.
21 pages.
|
|
|