Ontology mapping is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. Many efforts have been conducted to automate the discovery of ontology mapping. However, some problems are still evident. In this paper, ontology mapping is formalized as a problem of decision making. In this way, discovery of optimal mapping is cast as finding the decision with minimal risk. An approach called RiMOM (Risk Minimization based Ontology Mapping) is proposed, which automates the process of discoveries on 1:1, n:1, 1:null and null:1 mappings. Based on the techniques of normalization and NLP, the problem of instance heterogeneity in ontology mapping is resolved to a certain extent. To deal with the problem of name conflict in mapping process, we use thesaurus and statistical technique. Experimental results indicate that the proposed method can significantly outperform the baseline methods, and also obtains improvement over the existing methods.

Theoretical analysis and experimental tests indicate that single-phase full-bridge Z-Source inverter, compared to its three-phase counterpart, suffers greatly from low-frequency output harmonic distortion due to the 2nd harmonic component of the current drawn by the inverter bridge from the DC side. Analytic relationship between low-frequency capacitor voltage and inductor current ripple factors and ZSource network parameters of single-phase Z-Source inverter under Simple Boost Control is derived in this paper. In addition, one novel Low-frequency Harmonics EliminationPulse Width Modulation technique is presented, which could greatly reduce low-frequency capacitor voltage ripple for given Z-Source network. The theoretical analyses and proposed modified pulse width modulation technique have been confirmed by computer simulation and laboratoryimplemented prototype.

Abstract. The large volume of web content needs to be annotated by ontologies (called Semantic Annotation), and our empirical study shows that strong dependencies exist across different types of information (it means that identification of one kind of information can be used for identifying the other kind of information). Conditional Random Fields (CRFs) are the state-of-the-art approaches for modeling the dependencies to do better annotation. However, as information on a Web page is not necessarily linearly laid-out, the previous linear-chain CRFs have their limitations in semantic annotation. This paper is concerned with semantic annotation on hierarchically dependent data (hierarchical semantic annotation). We propose a Tree-structured Conditional Random Field (TCRF) model to better incorporate dependencies across the hierarchically laid-out information. Methods for performing the tasks of model-parameter estimation and annotation in TCRFs have been proposed. Experimental results indicate that the proposed TCRFs for hierarchical semantic annotation can significantly outperform the existing linear-chain CRF model.

Vehicle to Grid technology allows the batteries of electric vehicles to operate as energy storage elements for renewable energy power systems. The Z-Source inverter is a new and attractive topology for the power electronics interface. In this paper, the equivalent DC-link voltage ripple of a single-phase Z-Source inverter for Vehicle to Grid applications is analyzed in this paper before deriving a general design approach for the Z-Source network. These theoretical findings, and design rule for a Z-Source network have been confirmed by computer simulations and a laboratory-implemented prototype.

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