Services discovery based on syntactic matching cannot adapt to the open and dynamic environment of the web. To select the proper one from the web services candidate set provided by syntactic matching, a service select...Services discovery based on syntactic matching cannot adapt to the open and dynamic environment of the web. To select the proper one from the web services candidate set provided by syntactic matching, a service selection method based on semantic similarity is proposed. First, this method defines a web services ontology including QoS and context as semantic supporting, which also provides a set of terms to describe the interfaces of web services. Secondly, the similarity degree of two web services is evaluated by computing the semantic distances of those terms used to describe interfaces. Compared with existing methods, interfaces of web services can be interpreted under ontology, because it provides a formal and semantic specification of conceptualization. Meanwhile, efficiency and accuracy of services selection are improved.展开更多
In order to achieve adaptive and efficient service composition, a task-oriented algorithm for discovering services is proposed. The traditional process of service composition is divided into semantic discovery and fun...In order to achieve adaptive and efficient service composition, a task-oriented algorithm for discovering services is proposed. The traditional process of service composition is divided into semantic discovery and functional matching and makes tasks be operation objects. Semantic similarity is used to discover services matching a specific task and then generate a corresponding task-oriented web service composition （TWC） graph. Moreover, an algorithm for the new service is designed to update the TWC. The approach is applied to the composition model, in which the TWC is searched to obtain an optimal path and the final service composition is output. Also, the model can implement realtime updating with changing environments. Experimental results demonstrate the feasibility and effectiveness of the algorithm and indicate that the maximum searching radius can be set to 2 to achieve an equilibrium point of quality and quantity.展开更多
An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic rela...An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.展开更多
To properly compute the ontological similarity, an ontological similarity network-based reasoning framework is proposed. It structurally integrates extension-based approach, intension-based approach, the similarity ne...To properly compute the ontological similarity, an ontological similarity network-based reasoning framework is proposed. It structurally integrates extension-based approach, intension-based approach, the similarity network-based reasoning to exploit the implicit similarity, and the feedback from the context to validate the similarity measures. A new similarity measure is also presented to construct concept similarity network, which scales the similarity using the relative depth of the least common super-concept between any two concepts. Subsequently, the graph theory, instead of predefined knowledge rules, is applied to perform the similarity network-based reasoning such that the knowledge acquisition can be avoided. The framework has been applied to text categorization and visualization of high dimensional data. Theory analysis and the experimental results validate the proposed framework.展开更多
基金The National Natural Science Foundation of China(No.70471090,70472005),the Natural Science Foundation of Jiangsu Province(No.BK2004052,BK2005046).
文摘Services discovery based on syntactic matching cannot adapt to the open and dynamic environment of the web. To select the proper one from the web services candidate set provided by syntactic matching, a service selection method based on semantic similarity is proposed. First, this method defines a web services ontology including QoS and context as semantic supporting, which also provides a set of terms to describe the interfaces of web services. Secondly, the similarity degree of two web services is evaluated by computing the semantic distances of those terms used to describe interfaces. Compared with existing methods, interfaces of web services can be interpreted under ontology, because it provides a formal and semantic specification of conceptualization. Meanwhile, efficiency and accuracy of services selection are improved.
基金The National Key Technology R&D Program of Chinaduring the 11th Five-Year Plan Period(No2007BAF23B0302)the Major Research Plan of the National Natural Science Foundation of China(No90818028)
文摘In order to achieve adaptive and efficient service composition, a task-oriented algorithm for discovering services is proposed. The traditional process of service composition is divided into semantic discovery and functional matching and makes tasks be operation objects. Semantic similarity is used to discover services matching a specific task and then generate a corresponding task-oriented web service composition （TWC） graph. Moreover, an algorithm for the new service is designed to update the TWC. The approach is applied to the composition model, in which the TWC is searched to obtain an optimal path and the final service composition is output. Also, the model can implement realtime updating with changing environments. Experimental results demonstrate the feasibility and effectiveness of the algorithm and indicate that the maximum searching radius can be set to 2 to achieve an equilibrium point of quality and quantity.
基金The National Natural Science Foundation of China(No.50674086)Specialized Research Fund for the Doctoral Program of Higher Education(No.20060290508)the Science and Technology Fund of China University of Mining and Technology(No.2007B016)
文摘An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.
基金The National Natural Science Foundation of China(No.60003019).
文摘To properly compute the ontological similarity, an ontological similarity network-based reasoning framework is proposed. It structurally integrates extension-based approach, intension-based approach, the similarity network-based reasoning to exploit the implicit similarity, and the feedback from the context to validate the similarity measures. A new similarity measure is also presented to construct concept similarity network, which scales the similarity using the relative depth of the least common super-concept between any two concepts. Subsequently, the graph theory, instead of predefined knowledge rules, is applied to perform the similarity network-based reasoning such that the knowledge acquisition can be avoided. The framework has been applied to text categorization and visualization of high dimensional data. Theory analysis and the experimental results validate the proposed framework.
