Foundations of statistical natural language processing
Foundations of statistical natural language processing
What Are Ontologies, and Why Do We Need Them?
IEEE Intelligent Systems
Text Mining Techniques to Automatically Enrich a Domain Ontology
Applied Intelligence
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
A simple but powerful automatic term extraction method
COMPUTERM '02 COLING-02 on COMPUTERM 2002: second international workshop on computational terminology - Volume 14
Thesaurus based automatic keyphrase indexing
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
User performance versus precision measures for simple search tasks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying important concepts from medical documents
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
TextOntoEx: Automatic ontology construction from natural English text
Expert Systems with Applications: An International Journal
Text-based domain ontology building using tf-idf and metric clusters techniques
The Knowledge Engineering Review
KP-Miner: A keyphrase extraction system for English and Arabic documents
Information Systems
Supporting the discovery and labeling of non-taxonomic relationships in ontology learning
Expert Systems with Applications: An International Journal
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
CRCTOL: A semantic-based domain ontology learning system
Journal of the American Society for Information Science and Technology
Probabilistic Topic Models for Learning Terminological Ontologies
IEEE Transactions on Knowledge and Data Engineering
KX: A flexible system for keyphrase extraction
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Enhancement of domain ontology construction using a crystallizing approach
Expert Systems with Applications: An International Journal
Boosting Collaborative Ontology Building with Key-Concept Extraction
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
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
Ontology learning from text: A look back and into the future
ACM Computing Surveys (CSUR)
The semantic GrowBag algorithm: automatically deriving categorization systems
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
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Key concept extraction is a major step for ontology learning that aims to build an ontology by identifying relevant domain concepts and their semantic relationships from a text corpus. The success of ontology development using key concept extraction strongly relies on the degree of relevance of the key concepts identified. If the identified key concepts are not closely relevant to the domain, the constructed ontology will not be able to correctly and fully represent the domain knowledge. In this paper, we propose a novel method, named CFinder, for key concept extraction. Given a text corpus in the target domain, CFinder first extracts noun phrases using their linguistic patterns based on Part-Of-Speech (POS) tags as candidates for key concepts. To calculate the weights (or importance) of these candidates within the domain, CFinder combines their statistical knowledge and domain-specific knowledge indicating their relative importance within the domain. The calculated weights are further enhanced by considering an inner structural pattern of the candidates. The effectiveness of CFinder is evaluated with a recently developed ontology for the domain of 'emergency management for mass gatherings' against the state-of-the-art methods for key concept extraction including-Text2Onto, KP-Miner and Moki. The comparative evaluation results show that CFinder statistically significantly outperforms all the three methods in terms of F-measure and average precision.