Interoperability has gained in importance and become an essential issue within the Semantic Web community. The more standardized and widespread the data manipulation tools are, the easier and more attractive using the Semantic Web approach has become. Though Semantic Web technologies can support the unambiguous identification of concepts and formally describe relationships between concepts, thereby allowing the representation of data in a more meaningful and more machine-understandable way, Web developers are still faced with the problem of semantic interoperability, which stands in the way of achieving the Web’s full potential. To attain semantic interoperability, systems must be capable of exchanging data in such a way that the precise meaning of the data is readily accessible, and the data itself can be translated by any system into a form that it understands. Hence, a central problem of interoperability and data integration issues in Semantic Web vision is schema or ontology matching and mapping. Considering this situation in SemanticWeb research, we wish to contribute to the enhancement of (semantic) interoperability by contributing to the ontology matching solution. The number of use cases for ontology matching justifies the great importanceof this topic in the Semantic Web. Furthermore, the development and existence of tried and tested ontology matching algorithms and support tools will be one of the crucial issues that may have a significant impact on future development. Therefore, we have developed a Metadata-based Ontology Matching (MOMA) Framework that addresses data integration and the interoperability issue by creating and maintaining awareness of the link between matching algorithms and various ontologies. Our approach allows for a more flexible manual and (semi-)automatic deployment of matching algorithms, depending on the specific requirements of the application (e.g. suitability to certain types of input) to which the matchers are to be utilized. Since it is difficult to theoretically compare the existing approaches due to the fact that they are based on different techniques, a matcher characteristic that describes the different approaches on various levels of detail is needed. We have hence developed a Multilevel Characteristic for Matching Approaches (MCMA), which forms part of the MOMA Framework and has been utilized for the matcher selection. Taking into account the requirements of the successful deployment of semantic technologies regarding off-the-shelf and easy to use tools, the MOMA Framework should be capable of meeting the demands of different users: humans (Semantic Web experts and ontology matching lay users) and machines (e.g. service/matching providers). For human users, the process of choosing the suitable approach can be carried out manually, while machines require at the very least a semi-automatic selection of appropriate matchers. In manual selection, since the decision depends on multiple criteria (MCMA) and scales are not consistent, we have applied a systematic approach that structures the expectation, intuition, and heuristic-based decision making into a well- defined methodology called Analytic Hierarchy Process (AHP). In order to (semi-) automatically determine which matchers are appropriate for a given application, the MOMA Framework uses additional information on the ontologies (ontology metadata) and available matchers (matcher metadata). The ontology metadata captures information about matching relevant ontology features while the matcher metadata, based on the MCMA, describes the most important characteristics of the matching services. Furthermore, since explicit knowledge about the dependencies between thematching algorithms and the structures on which they operate is needed, we have formalized it into dependency rules statements that, taking into account the characteristic of matching approaches and ontological sources to be matched, determine which elements (i.e. matchers) are to be used for a given set of ontologies. Since the evaluation aspects of the MOMA Framework are directly related to the usage of the framework in real-world situations, the evaluation of both the AHP and rule-based approaches has been conducted on real-world test cases defined by the Ontology Alignment Evaluation Initiative (OAEI) Campaign. The results of the evaluation process demonstrate the applicability of the MOMA Framework for matcher selection and the accuracy of its predictions. With theMOMAFramework, which allows for the selection of suitable matching approaches w.r.t the given application requirements, we intend to contribute to the tackling of real world challenges, which are commonly agreed testbeds and benchmarking, with the aim of ensuring seamless interoperability and integration of the various Semantic Web technologies.