Ontology-driven document enrichment: principles, tools and applications
International Journal of Human-Computer Studies
IEEE Intelligent Systems
IEEE Intelligent Systems
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Ontology Specification Languages for the Semantic Web
IEEE Intelligent Systems
Automatic Ontology-Based Knowledge Extraction from Web Documents
IEEE Intelligent Systems
IEEE Internet Computing
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
Ontology Construction for Information Selection
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Web Data Cleansing and Preparation for Ontology Extraction Using WordNet
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 2 - Volume 2
A Unified Probabilistic Framework for Web Page Scoring Systems
IEEE Transactions on Knowledge and Data Engineering
Learning ontologies from natural language texts
International Journal of Human-Computer Studies
Building and maintaining ontologies: a set of algorithms
Data & Knowledge Engineering - NLDB2002
Finding aliases on the web using latent semantic analysis
Data & Knowledge Engineering - Special issue: WIDM 2002
Extracting conceptual relationships from specialized documents
Data & Knowledge Engineering - Special issue: ER 2002
OntoTrack: A semantic approach for ontology authoring
Web Semantics: Science, Services and Agents on the World Wide Web
Automated Chinese domain ontology construction from text documents
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Constructing tree-based knowledge structures from text corpus
Applied Intelligence
Supporting small teams in cooperatively building application domain models
Expert Systems with Applications: An International Journal
Enhancement of domain ontology construction using a crystallizing approach
Expert Systems with Applications: An International Journal
GRAONTO: A graph-based approach for automatic construction of domain ontology
Expert Systems with Applications: An International Journal
A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection
Expert Systems with Applications: An International Journal
Research on domain ontology in different granulations based on concept lattice
Knowledge-Based Systems
Exploiting online social data in ontology learning for event tracking and emergency response
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Adapting domain ontology for personalized knowledge search and recommendation
Information and Management
Learning to create an extensible event ontology model from social-media streams
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
CFinder: An intelligent key concept finder from text for ontology development
Expert Systems with Applications: An International Journal
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Ontology describes data about data and offers a group of glossaries with a definition that encompasses them in their entire. It not only transfers syntax of words but also accurately transfers semantic data between human users and the network. Hence, the usefulness of the semantic web depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the domain ontology. First, we collected domain-related web pages from the Internet. Secondly, we use the HTML tag labels to choose meaningful terms from the web pages. Next, we use these terms to construct the domain ontology by calculating a TF-IDF to find the weight of terms, using a recursive ART network (Adaptive Resonance Theory Network) to cluster terms. Each group of terms will find a candidate keyword for ontology construction. Boolean operations locate individual keywords in a hierarchy. Finally, the system outputs an ontology in a Jena package using an RDF format. The primary experiment indicates that our method is useful for domain ontology creation.