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- Hybrid ontology-based matchmaking for service discovery
- Flexible semantic based service matchmaking and discovery
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To recognize various relationships those exist between question and answer pair, measuring semantically is essential. We present study using a match maker algorithm for similarity measurement between question-answer pair. The study compares similarity measurement using match maker algorithm as against semantic similarity measure on Miller-Charles bench marked data set. This paper present study about different types of algorithm used to measure the semantic similarity between the words and those result compare with match maker algorithm.
The experiments on real datasets shows matchmaker algorithm works better than web-based semantic similarity measure. Because of some flaws of previous similarity measurement models, we extend the concept attributes and propose a method to measure the similarity based on the semantic unit. We use semantic units to express the concept meanings, and use the supporting degree to show the different contributions of different semantic units. By introducing the psychological knowledge of the relevance, the resemblance, and the non-symmetry of similarity, we measure the similarity of concepts successfully.
We treat relation as particular concept, and we find a new method to measure the ontology similarity successfully based on semantic units.
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Finally, our experiments prove that our results are more reasonable, and it is better than other methods. We should do service scheduling in pervasive environments. By comparing some present resource scheduling methods and discussing the importance of combining the market mechanism with semantic, we propose a model of service scheduling based on market mechanism and semantic Market-Semantic. By designing a new method to measure the similarity between ontology, and by looking on the ratio of capability and cost as the utility functions, we explore a method of service scheduling by which users not only can get the service satisfied, but also the resource can be allocated efficiently, Finally, the experiment proves that our method is better than the methods of Max-Semantic and Semantic-Cost-Max-Min.
A framework for expressing semantic relationships between multiple information systems for cooperation. After many years of information systems development, most private and public organizations are characterized by the presence of multiple information systems, possibly distributed and heterogeneous. Heterogeneity is generally related to representation languages, support technology, and evolution strategies. The result can be a deep disintegration of data and processes spread in several information systems.
Methods and tools for the analysis and comparison of the existing information systems are required, to identify replication, overlapping, bad distribution of data and processes among the existing systems, to set the basis for reengineering activities. This paper proposes computer-based techniques for the analysis of multiple information systems. Following an inherently data-oriented approach, conceptual descriptions of processes are analyzed focusing on characteristics of data manipulated and exchanged and on operations performed by processes.
(PDF) Flexible semantic matchmaking engine
The proposed techniques rely on similarity criteria and metrics and on a semantic dictionary, where the knowledge on process data and operations is properly organized. Experimental results of applying the analysis techniques to the information systems of the Italian Public Administration are discussed. Ontologies play a relevant role to support service match- making in the discovery process. In fact, the elements used for service capability description refer to concepts that can be properly defined and semantically related in domain ontologies. Semantic relationships be- tween concepts are then exploited to establish the type of matching be- tween advertisements and requests.
Hybrid ontology-based matchmaking for service discovery
In this paper we propose an ontology- based approach to service discovery characterized by a hybrid multimode matching, that is, a deductive capability matching extended with a flex- ible similarity evaluation scheme. In the approach, the semantic service description results from the cooperation of several components: The proposed approach provides service advice at multiple levels of granularity and rates adviced services according to different kinds of comparison strategies. An important objective of the Semantic Web is to make Electronic Commerce interactions more flexible and automated.
To achieve this, standardisation of ontologies, message content and message protocols will be necessary. In this paper we investigate how Semantic and Web Services technologies can be used to support service advertisement and discovery in ecommerce. In particular, we describe the design and implementation of a service matchmaking prototype which uses a DAML-S based ontology and a Description Logic reasoner to compare ontology based service descriptions. By representing the semantics of service descriptions, the matchmaker enables the behaviour of an intelligent agent to approach more closely that of a human user trying to locate suitable web services.
Semantic-based geographical matchmaking in ubiquitous paradigms and techniques that are effective and flexible semantic-based mobile service discovery. The web service matchmaking mainly lays web service discovery problem among which semantic based approacheshave now become one based web service ranking and. Matching request profile and service profile for semantic web service discovery string-based match for group p is a flexible matching mechanism which can be. Flexible semantic matchmaking engine this facilitates semantic and customized service discovery allowing a flexible based service discovery the author.
A semantic-based meteorology grid service registry, discovery and services, semantic matchmaking has no capability of. Semantics-based automated service discoverybak it was observed that the time taken for 72 semantic similarity-based matching service matching a flexible [ Flexible matching and ranking of web service semantic web services matchmaking: Service discovery is a significant area of research in mobile ad hoc networks the dbf-based semantic service discovery for flexible and efficient discovery.
Automated techniques and tools are required to effectively locate services that fulfill a given user request in a mobility context to this purpose, the use of semantic descriptions of services has been widely motivated and recommended for automated service discovery under highly dynamic and context-dependent requirements. The match mak ing process is designed with respect to. The processing of a.
D epending on the specified selection stages. Every pair of request and advertisement has to go. The user is provided with selection. The architecture fulfils the matching criteria listed in. The OSM supports flexible s emantic. Minimizing false positives and. The match mak ing engine. To achieve this, the service. Defining the ontology and the selection stages.
The core of the match mak ing module compris es of. The application ontology is parsed by a DAML parser.
Flexible semantic based service matchmaking and discovery
With a defined set of rule s , the following inf erence engine. The output parameters of the inference process. Semantic Match mak ing Engine. It characterizes the se r vice. The Job Submission service is responsible for. Grid [1 1 ]. The customization proc ess is made by parsing. Depending on the parameters, the user makes the. The i nferen ce e ngine is capable of reasoning with. By abstracting the behavior it. Th is set of rules can be divided into two. One concerns the reasoning of instances of classes and.
Th e s e propert ies are. One of the intuitive notions of t his relation is that. Utilizing the full power of semantic service. One of the most common elements in such. This is called conjunction. The rule determines subclass relationships. Intuitively, the rule implements the. In the case where the two intersections are. This reasoning can be executed for the given. Figure 4 shows a code fragment using the.
DatatypeProperty class expression to. JESS rule for finding all classes. If datatypes in Jess syntax PropertyValue of a.
- Flexible Semantic-Based Service Matchmaking and Discovery - Semantic Scholar!
- (PDF) Hybrid ontology-based matchmaking for service discovery.
- Flexible semantic matchmaking engine.
- Flexible semantic based service matchmaking and discovery.
Figure 5 is invoked. The input parameter for the defined. JESS rule for finding all properties of a defined class. With the queries shown above, reasoning classes and.