A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
WordNet: a lexical database for English
Communications of the ACM
An algorithm for suffix stripping
Readings in information retrieval
Modern Information Retrieval
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
The VLDB Journal — The International Journal on Very Large Data Bases
Audio Structuring and Personalized Retrieval Using Ontologies
ADL '00 Proceedings of the IEEE Advances in Digital Libraries 2000
Speech recognition in the Informedia Digital Video Library: uses and limitations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Ontology-based information selection
Ontology-based information selection
Retrieval effectiveness of an ontology-based model for information selection
The VLDB Journal — The International Journal on Very Large Data Bases
Conceptual Indexing: A Better Way to Organize Knowledge
Conceptual Indexing: A Better Way to Organize Knowledge
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To improve the accuracy in terms of precision and recall of an audio information retrieval system we have created a domain-specific ontology (a collection of key concepts and their interrelationships), as well as a novel, pruning algorithm. Given the shortcomings of keyword-based techniques, we have opted to employ a concept-based technique utilizing this ontology. Achieving high precision and high recall is the key problem in the retrieval of audio information. In traditional approaches, high recall is typically achieved at the expense of low precision, and vice versa. Through the use of a domain-specific ontology appropriate concepts can be identified during metadata generation (description of audio) or query generation, thus improving precision.When irrelevant concepts are associated with queries or documents there is a loss of precision. On the other side of the coin, if relevant concepts are discarded, a loss of recall will ensue. In conjunction with the use of a domain specific ontology we have thus proposed a novel, automatic pruning algorithm which prunes as many irrelevant concepts as possible during any case of description and identification of documents, and query generation. To improve recall, A controlled and correct query expansion mechanism is proposed for the improvement of recall, thus guaranteeing that precision will not be lost.We have constructed a demonstration prototype, and experimentally and analytically we have shown that our model, compared to keyword search, achieves a significantly higher degree of precision and recall.