Ontologizing concept maps using graph theory
Proceedings of the 2011 ACM Symposium on Applied Computing
Towards open ontology learning and filtering
Information Systems
Generating concept map exercises from textbooks
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Extraction of concept maps from textbooks for domain modeling
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
Evaluation of concepts asset in topic maps ontology
AIC'10/BEBI'10 Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics
Voting theory for concept detection
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Concept map construction from text documents using affinity propagation
Journal of Information Science
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One of the goals of the Knowledge Puzzle Project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the Knowledge Puzzle Ontology Learning Tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a three-dimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.