Generating and evaluating domain-oriented multi-word terms from texts
Information Processing and Management: an International Journal
TERMINAE: A Linguistic-Based Tool for the Building of a Domain Ontology
EKAW '99 Proceedings of the 11th European Workshop on Knowledge Acquisition, Modeling and Management
The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Domain-Specific Knowledge Acquisition and Classification Using WordNet
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Conceptual clustering of documents for automatic ontology generation
BICS'13 Proceedings of the 6th international conference on Advances in Brain Inspired Cognitive Systems
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Ontology together with Semantic Web has a vital role in knowledge management on a global scale. Since manual construction of ontology leads to complex, time consuming and inconsistent results, automatic construction of ontology is more preferred. This consists of two phases, such as concept based retrieval and the generation of ontology. The extraction of the semantic concept from unstructured input document is focused in this paper. Semantic concepts can be extracted based on the analysis of a set of texts and using WordNet. Challenges facing are finding of semantic relationships among concepts and elimination of irrelevant documents by identifying conceptual mismatches. For each word in the text document, corresponding synonym, hyponym, and hypernym will be extracted from the WordNet. These concepts and their relationships can be used to make the taxonomy for the automatic construction of ontology. JDK and Net Beans IDE are used with WordNet for the implementation.