WordNet: a lexical database for English
Communications of the ACM
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Comparing corpora using frequency profiling
WCC '00 Proceedings of the workshop on Comparing corpora - Volume 9
Learning domain ontologies for semantic Web service descriptions
Web Semantics: Science, Services and Agents on the World Wide Web
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
Profiling and Tracing Stakeholder Needs
Innovations for Requirement Analysis. From Stakeholders' Needs to Formal Designs
Semantic information integration and question answering based on pervasive agent ontology
Expert Systems with Applications: An International Journal
Journal of Information Science
Research on domain ontology in different granulations based on concept lattice
Knowledge-Based Systems
Journal of Web Engineering
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
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
Hi-index | 0.01 |
Ontology learning refers to extracting conceptual knowledge from several sources and building an ontology from scratch, enriching, or adapting an existing ontology. It uses methods from a diverse spectrum of fields such as natural language processing, artificial intelligence and machine learning. However, a crucial challenging issue is to quantitatively evaluate the usefulness and accuracy of both techniques and combinations of techniques, when applied to ontology learning. It is an interesting problem because there are no published comparative studies. We are developing a flexible framework for ontology learning from text which provides a cyclical process that involves the successive application of various NLP techniques and learning algorithms for concept extraction and ontology modelling. The framework provides support to evaluate the usefulness and accuracy of different techniques and possible combinations of techniques into specific processes, to deal with the above challenge. We show our framework's efficacy as a workbench for testing and evaluating concept identification. Our initial experiment supports our assumption about the usefulness of our approach.