Ontology applies commonly to solve the problem of heterogeneity of data in the Semantic Web, but the heterogeneity problem between two ontologies seriously affects their communication. As an effective method, ontology matching can address the problem above, whose core technique is the similarity measure. A single similarity measure calculates the similarity value about a feature between two concepts, but none of the similarity measures can ensure their effectiveness in all context due to the diverse heterogeneous features between two ontologies. Therefore, multiple similarity measures are usually aggregated to improve the result's confidence. The problem that how to determine the optimal aggregating weights for the different similarity measures to obtain a high-quality alignment is called the meta-matching problem of ontology, which is modeled as a nonlinear problem with many local optimal solutions. Evolutionary Algorithm (EA) can represent an efficient methodology to address the ontology meta-matching problem, but EA-based ontology matching techniques suffer from the premature convergence and the requirement of a reference alignment to evaluate the solutions. To overcome the defects mentioned above, in this work, an improved EA-based matching approach is proposed, where two approximate evaluation indicators, i.e. pseudo-recall and pseudo-precision, are presented to evaluate the solution's quality, and an adaptive selection pressure is utilized to overcome the premature convergence. The experiment utilizes the Ontology Alignment Evaluation Initiative (OAEI)'s benchmark, and the experimental results will prove the effectiveness of our proposed method.
Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm (D$$^{2}$$
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CMOPSO). The sophistication of the D$$^{2}$$
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CMOPSO based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.
Support Vector Machine (SVM) based on Structural Risk Minimization (SRM) of Statistical Learning Theory has excellent performance in fault diagnosis. However, its training speed and diagnosis speed are relatively slow. Signed Directed Graph (SDG) based on deep knowledge model has better completeness that is knowledge representation ability. However, much quantitative information is not utilized in qualitative SDG model which often produces a false solution. In order to speed up the training and diagnosis of SVM and improve the diagnostic resolution of SDG, SDG and SVM are combined in this paper. Training samples' dimension of SVM is reduced to improve training speed and diagnosis speed by the consistent path of SDG; the resolution of SDG is improved by good classification performance of SVM. The Matlab simulation by Tennessee-Eastman Process (TEP) simulation system demonstrates the feasibility of the fault diagnosis algorithm proposed in this paper.
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