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based on keyword indexing, there are many records in their result lists that are irrelevant to the user's information needs. It is shown that for retrieving more relevant and precise results, the following two points should be concerned: First of all, the query (either it is generated by a human or an intelligent agent
Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can't guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize
In recent years, the application of ontology has been already toward the diversification under the development of the semantic Web technology. The main application of ontology is information retrieval. With the utilization of ontology, we expect to offer more correct information for users. Although, most of the applications of ontology are information retrieval but they lacks of the interaction with...
In this paper, we will propose a domain ontology extensible method which can insert new keywords into the corresponding constructed domain ontology. The novel method uses TF-IDF (Term Frequency - Inverse Document Frequency) and LSA (Latent Semantic Analysis) to strengthen the semantic characteristic of keywords and
domain ontology by calculating a TF–IDF to find the weight of terms, using a recursive ART network (Adaptive Resonance Theory Network) to cluster terms. Each group of terms will find a candidate keyword for ontology construction. Boolean operations locate individual keywords in a hierarchy. Finally, the system outputs an
to determine the forms' relevance to the domain. In this work scientific research publications domain has been considered. Experimental results show that proposed approach is better as compared to keyword based crawlers in terms of both relevancy and completeness.
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