| Manual ontology construction is costly, time consuming, error-prone and inflexible. To address these problems, researchers hope that an automated process will result in faster and better ontology construction and enrichment. Ontology learning has recently become a major area of research whose goal is to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, most current approaches deal with some specific tasks that are merely a part of the ontology learning process rather than providing support to users throughout the complete process. There are few studies that attempt to automate the entire ontology learning process from a collection of domain-specific literature through the use of text mining and statistical natural language processing. In this paper, we present a complete framework for ontology learning that enables us to retrieve documents from the Web using focused crawling. We then use an SVM (Support Vector Machine) classifier to identify domain-specific documents and perform text mining in order to extract useful information for the ontology enrichment process. Our experimental results with an amphibian morphology ontology support our belief that we can use SVM and text mining approaches to improve the association of words with the correct concept in an ontology. This paper reports on the overall system architecture and our initial experiments on all phases in our ontology learning framework, i.e., document focused crawling, document classification, and information extraction using text mining techniques to enrich the domain ontology. |
