A concept-based model for enhancing text categorization
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Ontology-Based Natural Query Retrieval Using Conceptual Graphs
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Automatic categorization of questions for user-interactive question answering
Information Processing and Management: an International Journal
A three-phase method for patent classification
Information Processing and Management: an International Journal
| Hi-index | 0.00 |
Most of the data representation techniques are based on word and/or phrase analysis of the text. The statistical analysis of a term (word or phrase) frequency captures the importance of the term within a document. However, to achieve a more accurate analysis, the underlying data representation should indicate terms that capture the semantics of the text from which the importance of a term in a sentence and in the document can be derived. A new concept-based representation that relies on the analysis of the sentence semantics, rather than, the traditional analysis of the document dataset only is introduced. The proposed conceptual ontological graph representation denotes the terms which contribute to the sentence semantics. Then, each term is chosen based on its position in the proposed representation. Lastly, the selected terms are associated to their documents as features for the purpose of indexing in the text retrieval. Experiments using the proposed conceptual ontological graph representation in text retrieval are conducted. The evaluation of results is relied on two quality measures, the precision and the recall. Both of these quality measures improved when the newly developed representation is used to enhance the performance of the text retrieval.