OntoAQ: Ontology-Based Flexible Querying System for Farmers

Abstract

Queries are expressed by farmers related to their crop in natural language, which are usually answered by human experts. Knowledge required to answer such queries can be modeled as ontologies that include agricultural practices, data related to the farm being queried about, recent weather conditions, and other contextual information. Farmers depend on trends followed by other farmers, vendor recommendations, and government advisories, which form a part of the spatiotemporal context. OntoAQ is an ontology-based knowledge system that is designed for answering questions based on a fact database. We have implemented OntoAQ using a cotton crop database and provide a keyword-based query interface to enable farmers to ask specific questions about the problem they face. Questions are filtered using context parameters such as the crop and activities that the farmer is engaged in. Context parameters are inferred from the ontological information. Based on the users’ input, answers are provided using a graph-matching algorithm that locates the set of best matches. Answers are ranked based on relevance of the keywords. We discuss the relevance ontologies of farming practices based on spatial and temporal contexts of each activity and provide recommendations to further improve the system.