The task of verifying the compatibility between interacting web services has traditionally been limited to checking the compatibility of the interaction protocol in terms of message sequences and the type of data being exchanged. Since web services are developed largely in an uncoordinated way, different services often use independently developed ontologies for the same domain instead of adhering to a single ontology as standard. In this work we investigate the approaches that can be taken by the server to verify the possibility to reach a state with semantically inconsistent results during the execution of a protocol with a client, if the client ontology is published. Often database is used to store the actual data along with the ontologies instead of storing the actual data as a part of the ontology description. It is important to observe that at the current state of the database the semantic conflict state may not be reached even if the verification done by the server indicates the possibility of reaching a conflict state. A relational algebra based decision procedure is also developed to incorporate the current state of the client and the server databases in the overall verification procedure.
Ontology plays an important role towards enhancing the integration and interoperability of the semantic web services. A significant amount of research has been done towards formalizing the notion of conflict between two ontologies. @cite_7 , authors present a detailed classification of conflicts by distinguishing between and mismatches. @cite_25 authors further generalize the notion of conflicts and classify semantic mismatches into language level mismatches and ontology level mismatches. Then ontology level mismatches are further classified into conceptualization mismatch and explication mismatch. Further research in the same direction @cite_3 adds few new types of conceptualization mismatches. Researchers in @cite_10 present alternative types of conflicts that are primarily relevant to OWL based ontologies. However primary focus of these works is towards the interoperability between two ontologies -- rather than the correctness of the protocol for information exchange with respect to the interpretation.
@cite_3 @cite_10 @cite_25 @cite_7
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Ontology, as the means for conceptualizing and structuring domain knowledge, has become the backbone to enable the fulfillment of the semantic Web vision. But as the number of different online ontologies is growing significantly, the problem of managing semantic heterogeneity is increasingly seen. This semantic heterogeneity originates as a mismatch in the way the domain is interpreted and modeled. Many researches identified the reasons of semantic mismatches and categorized them into three main classes' i.e. linguistic mismatch, conceptualization mismatch and explication mismatch. In this paper, we have identified three more types of conceptualization mismatches. We have demonstrated the importance of such mismatches by giving different scenarios where appropriate. These mismatches should be paid more attention during ontology integration for sound semantic Web environment, because if ignored then we cannot take combined use of independently developed ontologies effectively. Ontology provides sharing knowledge among different data sources which will help to clarify the semantics of information. OWL is being promoted as a standard for web ontology language, thus in the future a considerable number of ontologies will be created based on OWL. Therefore automatically detecting semantic conflicts based on OWL will greatly expedite the step to achieve semantic interoperability, and will greatly reduce the manual work to detect semantic conflicts. In this paper, we summarize seven cases based on OWL in which semantic conflicts are easily to be encountered, and for each case, we give a rule to resolve the conflict. Via a series of examples, we show how this method works. Based on the seven cases, we propose a semantic conflicts detection and resolution algorithm. This aims at providing a semi-automatic method for semantic uniform data interoperability and integration. With the grown availability of large and specialized online ontologies, the questions about the combined use of independently developed ontologies have become even more important. Although there is already a lot of research done in this area, there are still many open questions. In this paper we try to classify the problems that may arise into a common framework. We then use that framework to examine several projects that aim at some ontology combination task, thus sketching the state of the art. We conclude with an overview of the different approaches and some recommandations for future research. The growth of the Internet has revitalised research on the integration of heterogeneous information sources. Integration efforts face a trade off between interoperability and heterogeneity. Important integration obstacles arise from the differences in the underlying ontologies of the various sources. In this paper we investigate the impediments to integration, focussing on ontologies. In particular, we present a classification of ontology mismatches (distinguishing conceptualisation mismatches and explication mismatches as its main categories), and discuss how each of the mismatch types can be dealt with. The idea is that knowing which ontology mismatches are difficult to deal with may contribute to finding a balance between heterogeneity and interoperability.