Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Toward semantic understanding: an approach based on information extraction ontologies
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Question answering passage retrieval using dependency relations
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
An inference model for semantic entailment in natural language
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Employing a Domain Specific Ontology to Perform Semantic Search
ICCS '08 Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning
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For many years, information retrieval tools have been used to try to solve the information overload problem which was accentuated by the coming of age of the World Wide Web. Some tools used Boolean search, others, natural language based processing (NLP). Ontology-based techniques were proposed to improve the quality of the search but none were widely adopted since they did not statistically enhance either the recall or the precision of the search. However, when it comes to information extraction, they may be of significant help. Their integration in professional search engines has been rather slow, partially due to the fact that the ontology building process is time consuming. In this paper, we describe the SeseiOnto software, which uses simple artificial intelligence techniques to improve information extraction and retrieval. To assist the NLP-based information retrieval on a corpus of documents, SeseiOnto employs an automatically generated ontology. Under our experiments, we found that SeseiOnto obtained results comparable to a traditional search engine, while providing a natural language interface to its user.