There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which a...
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There are a lot of heterogeneous ontologies in semantic web, and the task of ontology mapping is to find their semantic relationship. There are integrated methods that only simply combine the similarity values which are used in current multi-strategy ontology mapping. The semantic information is not included in them and a lot of manual intervention is also needed, so it leads to that some factual mapping relations are missed. Addressing this issue, the work presented in this paper puts forward an ontology matching approach, which uses multi-strategy mapping technique to carry on similarity iterative computation and explores both linguistic and structural similarity. Our approach takes different similarities into one whole, as a similarity cube. By cutting operation, similarity vectors are obtained, which form the similarity space, and by this way, mapping discovery can be converted into binary classification. Support vector machine (SVM) has good generalization ability and can obtain best compromise between complexity of model and learning capability when solving small samples and the nonlinear problem. Because of the said reason, we employ SVM in our approach. For making full use of the information of ontology, our implementation and experimental results used a common dataset to demonstrate the effectiveness of the mapping approach. It ensures the recall ration while improving the quality of mapping results.
As the city population capacity and the construction scale unceasing expansion, City soil heavy metal pollution problems have become increasingly prominent. City hazards that it causes involve in all aspects of nation...
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ISBN:
(纸本)9781629939209
As the city population capacity and the construction scale unceasing expansion, City soil heavy metal pollution problems have become increasingly prominent. City hazards that it causes involve in all aspects of national economy, so city of heavy metal pollution analysis and assessment is to solve urgently. This paper uses a city provides 8 kinds of heavy metal elements(As, Cd, Cr, Cu, Hg, Ni, Pb, Zn) under different regions of the concentration content data. We construct quadratic trend surface model z = b0 + b 1x +2 +b2 y2 + b3xy + b4x + b5 y based on element concentration, by spatial distribution and variability research. We use the model to simulate the 8 heavy metal elements in the city 's spatial distribution and analysis city pollution degree of heavy metal in different regions based on single factor pollution index method;we construct factor analysis model according to the original data, so that approach the main reason of city heavy metal pollution.
Aiming at nonlinear decontrolled plants at large exist in industrial processes, this paper firstly introduces the support vector machine and least squares support vector machine briefly. On this basis, we propose a no...
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A novel improve algorithm TPDE is proposed in this paper, which combines differential evolution(DE). Each individual contains two states, the attractive state and the repulsive state. In order to refrain from the shor...
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Heuristic optimization is an efficient approach and robust. A novel hybrid algorithm DE-PSO is proposed in this paper, which combines differential evolution(DE) with the particle swarm optimization(PSO) algorithm. In ...
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The main challenge in the area of reinforcement learning is scaling up to larger and more complex problems. Aiming at the scaling problem of reinforcement learning, a scalable reinforcement learning method, DCS-SRL, i...
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The main challenge in the area of reinforcement learning is scaling up to larger and more complex problems. Aiming at the scaling problem of reinforcement learning, a scalable reinforcement learning method, DCS-SRL, is proposed on the basis of divide-and-conquer strategy, and its convergence is proved. In this method, the learning problem in large state space or continuous state space is decomposed into multiple smaller subproblems. Given a specific learning algorithm, each subproblem can be solved independently with limited available resources. In the end, component solutions can be recombined to obtain the desired result. To ad- dress the question of prioritizing subproblems in the scheduler, a weighted priority scheduling algorithm is proposed. This scheduling algorithm ensures that computation is focused on regions of the problem space which are expected to be maximally productive. To expedite the learning process, a new parallel method, called DCS-SPRL, is derived from combining DCS-SRL with a parallel scheduling architecture. In the DCS-SPRL method, the subproblems will be distributed among processors that have the capacity to work in parallel. The experimental results show that learning based on DCS-SPRL has fast convergence speed and good scalability.
Qos is a comprehensive indicator to measure satisfaction with a service, or quality of service. This paper analyzes the web site client multimedia Qos, Qos technical model and IPQos the implementation mechanism, so as...
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Model method metadata provided a new solution to the sharing of model method data in the spatial information system. Furthermore, it is crucial to research the basic model method metadata standard. With the analysis o...
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Unified Modeling Language (UML) provides various diagrams to depict system characteristics and complex environment from different viewpoints and different application layers. It also contains a lot of standard element...
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computer simulation has been widely used in scientific research. But the present simulation systems are mostly designed for a specific purpose, which can only be applied in specific research experiment, so the expansi...
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