Metamodels: definitions of structures or ontological commitments?

The concept of metamodel is central in Model Driven Engineering (MDE). It is used to define the conceptual foundation of modeling languages. There exist specialized languages for specifying metamodels known as metalanguages. The most popular of them are object-oriented and support defining structures of the metamodels without considering the semantical underpinnings of the structures. In this paper we study the nature of metamodels from philosophical perspective. We claim that a metamodel is something more than an abstract syntax definition: it is an ontological commitment that guides the modeler in his perception about the real world phenomenon. Therefore, metalanguages should derive their foundation from the study of Ontology. We employ an ontological theory based on the Four-category ontology and the principles of metaphysical realism. We propose a metalanguage called OGML (Ontology Grounded Metalanguage) built upon the basic concepts of this ontology

A Taxonomy of Metamodel Hierarchies

In the context of software engineering and model-driven development in particular, metamodeling gains more and more importance. So far, no classifying study of theoretical metamodeling concepts and hierarchy design options has been conducted in order to establish a comprehensive set of interrelated design variables, i.e. a coherent design space. A well-designed metamodeling hierarchy is essential to avoid problems not easily noticeable, like ambiguous classification and the replication of concepts. This study aims at exploring the theoretical foundation and providing a taxonomy or a design space for constructing tailor-made metamodel hierarchies for specific problems areas and domains

Analytical metadata modeling for next generation BI systems

Business Intelligence (BI) systems are extensively used as in-house solutions to support decision-making in organizations. Next generation BI 2.0 systems claim for expanding the use of BI solutions to external data sources and assisting the user in conducting data analysis. In this context, the Analytical Metadata (AM) framework defines the metadata artifacts (e.g., schema and queries) that are exploited for user assistance purposes. As such artifacts are typically handled in ad-hoc and system specific manners, BI 2.0 argues for a flexible solution supporting metadata exploration across different systems. In this paper, we focus on the AM modeling. We propose SM4AM, an RDF-based Semantic Metamodel for AM. On the one hand, we claim for ontological metamodeling as the proper solution, instead of a fixed universal model, due to (meta)data models heterogeneity in BI 2.0. On the other hand, RDF provides means for facilitating defining and sharing flexible metadata representations. Furthermore, we provide a method to instantiate our metamodel. Finally, we present a real-world case study and discuss how SM4AM, specially the schema and query artifacts, can help traversing different models instantiating our metamodel and enabling innovative means to explore external repositories in what we call metamodel-driven (meta)data exploration.Peer ReviewedPostprint (author's final draft

Metamodels of information technology best practices frameworks

This article deals with the generation and application of ontological metamodels of frameworks of best practices in IT. The ontological metamodels represent the logical structures and fundamental semantics of framework models and constitute adequate tools for the analysis, adaptation, comparison and integration of the frameworks of best practices in IT. The MetaFrame methodology for the construction of the metamodels, founded on the discipline of the conceptual metamodelling and on the extended Entity/Relationship methodology is described herein, as well as the metamodels of the best practices for the outsourcing of IT, the eSCM-SP v2.01 (eSourcing Capability Model for Service Providers) and the eSCM-CL v1.1 (eSourcing Capability Model for Client Organizations), constructed according to the MetaFrame methodology

Ontologies in domain specific languages : a systematic literature review

The systematic literature review conducted in this paper explores the current techniques employed to leverage the development of DSLs using ontologies. Similarities and differences between ontologies and DSLs, techniques to combine DSLs with ontologies, the rationale of these techniques and challenges in the DSL approaches addressed by the used techniques have been investigated. Details about these topics have been provided for each relevant research paper that we were able to investigate in the limited amount of time of one month. At the same time, a synthesis describing the main trends in all the topics mentioned above has been done

A Proposal for Deploying Hybrid Knowledge Bases: the ADOxx-to-GraphDB Interoperability Case

Graph Database Management Systems brought data model abstractions closer to how humans are used to handle knowledge - i.e., driven by inferences across complex relationship networks rather than by encapsulating tuples under rigid schemata. Another discipline that commonly employs graph-like structures is diagrammatic Conceptual Modeling, where intuitive, graphical means of explicating knowledge are systematically studied and formalized. Considering the common ground of graph databases, the paper proposes an integration of OWL ontologies with diagrammatic representations as enabled by the ADOxx metamodeling platform. The proposal is based on the RDF-semantics variant of OWL and leads to a particular type of hybrid knowledge bases hosted, for proof-of-concept purposes, by the GraphDB system due to its inferencing capabilities. The approach aims for complementarity and integration, providing agile diagrammatic means of creating semantic networks that are amenable to ontology-based reasoning