Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
The Usable Ontology: An Environment for Building and Assessing a Domain Ontology
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Text Mining Techniques to Automatically Enrich a Domain Ontology
Applied Intelligence
Mining Semantic Networks for Knowledge Discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Small Worlds: The Dynamics of Networks between Order and Randomness
Small Worlds: The Dynamics of Networks between Order and Randomness
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Web-scale information extraction in knowitall: (preliminary results)
Proceedings of the 13th international conference on World Wide Web
Lexical disambiguation using simulated annealing
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
The computation of word associations: comparing syntagmatic and paradigmatic approaches
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Mining Ontological Knowledge from Domain-Specific Text Documents
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Explorations in the use of semantic web technologies for product information management
Proceedings of the 16th international conference on World Wide Web
OntoSearch: a full-text search engine for the semantic web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Unsupervised learning of semantic relations between concepts of a molecular biology ontology
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Towards open ontology learning and filtering
Information Systems
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
ArabOnto: experimenting a new distributional approach for building Arabic ontological resources
International Journal of Metadata, Semantics and Ontologies
A semantic role labelling-based framework for learning ontologies from Spanish documents
Expert Systems with Applications: An International Journal
Agent-Based Virtual Humans in Co-Space: An Evaluative Study
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning concept hierarchies from textual resources for ontologies construction
Expert Systems with Applications: An International Journal
Concept map construction from text documents using affinity propagation
Journal of Information Science
CFinder: An intelligent key concept finder from text for ontology development
Expert Systems with Applications: An International Journal
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Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc.