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experiment was made to test the effect of the system. The satisfying results were obtained which proved that the system could effectively improve the performance of the agricultural domain FAQ retrieval system comparing with the baseline keyword FAQ system.
Traditional agricultural knowledge service is based on keyword rather than content, the recall rate and precision rate cannot to be improved. The knowledge representation based on semantic web can truly achieve the semantic-level knowledge service. This paper analyzes the status of agricultural knowledge service
keywords. Ontology can play a very important role in the process of creating as well as managing the knowledge. This paper addresses the important issues in developing domain specific ontology for agriculture domain. We propose a generic approach for agriculture domain ontology representing entities and their relationships
domain ontology to reformulate the user query. We proposed to reformulate the query (initially in natural language) by replacing it with the terms retrieved from ontology. Then we improve the approach by combining the terms from ontology with keyword/s extracted from the initial user's natural language query. Precision
employed in query reformulation process. This work proposes the use of crop characteristic category element in reformulating query, as well as combination with ontology and query keyword elements. The experimental results show that the use of crop characteristic category in reformulating query has significantly improved the
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.