Abstract
The object of our research is to develop an ontology-based agricultural knowledge fusion method that can be used as a comprehensive basis on which to solve agricultural information inconsistencies, analyze data, and discover new knowledge. A recent survey has provided a detailed comparison of various fusion methods used with Deep Web data (Li, 2013). In this paper, we propose an effective agricultural ontology-based knowledge fusion method by leveraging recent advances in data fusion, such as the semantic web and big data technologies, that will enhance the identification and fusion of new and existing data sets to make big data analytics more possible. We provide a detailed fusion method that includes agricultural ontology building, fusion rule construction, an evaluation module, etc. Empirical results show that this knowledge fusion method is useful for knowledge discovery.
Highlights
Most people use the Internet and the World-Wide-Web for browsing and getting information
Definition 2: Given primary knowledge fusion (PKF) = (AISS, M, Q), the agricultural knowledge fusion problem is defined as AKF = (PKF, f, FR), where:
The architecture consists of three main aspects: 1) agricultural ontology and fusion rules are the cornerstones of the convergence of agricultural knowledge; 2) agricultural ontology-based knowledge representation and matching, as well as mining and automatically selecting fusion rules based on the property of concept, are the key components in knowledge fusion; 3) in order to find more accurate knowledge to satisfy users’ queries, assessment of the fusion results is necessary to enhance knowledge fusion
Summary
Most people use the Internet and the World-Wide-Web for browsing and getting information. Information integration focuses on how to find relevant information, but in knowledge fusion this information is merged to create knowledge that is more complete, less uncertain, and less conflicting than the input (Hu, Hu, Sekhari, Peng, & Cao, 2011). This reduces the cost of data access and enhances the value of the discovered data. Hunter and Williams (2010) advocated a knowledge-based approach to merging semi-structured information. They used fusion rules to manage the semistructured information that was input for merging. The big data technologies, including data-processing models and emerging tools, are being developed for implementation of our fusion system
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