Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
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
Audio Structuring and Personalized Retrieval Using Ontologies
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Ontology Construction for Information Selection
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Ontology-based information selection
Ontology-based information selection
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
A Combined Approach of Formal Concept Analysis and Text Mining for Concept Based Document Clustering
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
TextOntoEx: Automatic ontology construction from natural English text
Expert Systems with Applications: An International Journal
Novel logistic regression models to aid the diagnosis of dementia
Expert Systems with Applications: An International Journal
Retrieval of semantic concepts based on analysis of texts for automatic construction of ontology
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
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In Information retrieval, Keyword based retrieval is unsatisfactory for user needs since it can't always retrieve relevant words according to the concept. Since different words can represent the same concept (polysemy) and one word can represent different concepts (homonymy), mapping problem will lead to word sense Disambiguation. Through the implementation of domain dependent ontology, concept based information retrieval (IR) can be achieved. Since Semantic concept extraction from keywords is the initial phase for automatic construction of ontology process, this paper propose an effective method for it. Reuters21578 is used as the input of this process, followed by indexing, training and clustering using self-Organizing Map. Based on the feature vector, the clustering of documents are formed using automatic concept selections, in order to make the hierarchy. Clusters are represented hierarchically based on the topics assigned .Ontology will be generated automatically for each cluster, based on the topic assigned.