In collaborative agile ontology development projects support for modular reuse of ontologies from large existing remote repositories, ontology project life cycle management, and transitive dependency management are important needs. The Apache Maven approach has proven its success in distributed collaborative Software Engineering by its widespread adoption. The contribution of this paper is a new design artifact called OntoMaven. OntoMaven adopts the Maven-based development methodology and adapts its concepts to knowledge engineering for Maven-based ontology development and management of ontology artifacts in distributed ontology repositories.
New standardization efforts such as OMG Application Programming Interfaces for Knowledge Bases (OMG API4KB) www.omgwiki.org API4KB and OMG OntoIOP aim at the accessability and interoperability of heterogenous ontologies via standardized interfaces and semantic transformations defined on the meta-level of the ontology models, e.g., by the Distributed Ontology Language (DOL) @cite_10 . These approaches do not address ontology engineering directly, but can provide a standardized repository back-end for OntoMaven ontology development projects.
@cite_10
2952247164 2553692388 2904842953 1660209954
The Distributed Ontology Language (DOL) is currently being standardized within the OntoIOp (Ontology Integration and Interoperability) activity of ISO TC 37 SC 3. It aims at providing a unified framework for (1) ontologies formalized in heterogeneous logics, (2) modular ontologies, (3) links between ontologies, and (4) annotation of ontologies. This paper focuses on an application of DOL's meta-theoretical features in mathematical formalization: validating relationships between ontological formalizations of mathematical concepts in COLORE (Common Logic Repository), which provide the foundation for formalizing real-world notions such as spatial and temporal relations. Ontologies are considered a key technology enabling semantic interoperability and integration of data and processes. We are now entering a phase of knowledge system development, in which ontologies are produced in larger numbers and exhibit greater complexity. Also we have witnessed an increasing need for technologies that enable the reuse of existing (Semantic) Web resources from within the ontology engineering environment itself. In the NeOn project we aim at advancing the state of the art in using ontologies for large-scale semantic applications. This is realized by providing an infrastructure for networked ontology management and engineering capable of suiting the community’s needs [1]. The heart of this infrastructure is the NeOn Toolkit for engineering contextualized networked ontologies and semantic applications. The shift from closed semantic applications, characterized by a monolithic corporate ontology, to open semantic applications, characterized by networks of ontologies, implies that a number of other aspects like dealing with context, collaboration or data and web integration become crucial. The NeOn Toolkit was built with the web-centric elements of semantic technologies in mind: much like Web 2.0 environments emphasize distributed content production, the NeOn Toolkit features methods and tools for managing knowledge that is distributed, heterogeneous, contextualized, and developed collaboratively. Technologies for Industry-4.0 are evolving rapidly, and the term semantics is widely used. Various standardization groups claim that they provide mechanisms to express semantics or allow the integration of semantic information. The emerging data format AutomationML (as IEC 62714) proposes a role-based approach to encode semantics in engineering models and has already standardized fundamental engineering concepts as role classes. However, for concrete data processing tasks the currently standardized role classes are not sufficient to unambiguously express the meaning of various vendor or application specific concepts, while user-defined role classes rely on hand wired semantics encoded in dedicated software, e.g. importer exporters. Yet AutomationML system unit classes represent reusable engineering objects as relational models of AutomationML roles, attributes, interfaces, internal elements and links, which can be used to describe complex user-specific concepts. To enable an automatic machine interpretation of these unstandardized relational models, we transform AutomationML data to a formal and declarative semantic representation using the Web Ontology Language (OWL), and propose a rule mining approach to learn the intended meaning of user selected system unit classes, i.e. to identify the common relational structure shared by the selected engineering objects. Ontology mapping is to find semantic correspondences between similar elements of different ontologies. It is critical to achieve semantic interoperability in the WWW. This paper proposes a new generic and scalable ontology mapping approach based on propagation theory, information retrieval technique and artificial intelligence model. The approach utilizes both linguistic and structural information, measures the similarity of different elements of ontologies in a vector space model, and deals with constraints using the interactive activation network. The results of pilot study, the PRIOR, are promising and scalable.