Institutionalising Ontology-Based Semantic Integration

We address what is still a scarcity of general mathematical foundations for ontology-based semantic integration underlying current knowledge engineering methodologies in decentralised and distributed environments. After recalling the first-order ontology-based approach to semantic integration and a formalisation of ontological commitment, we propose a general theory that uses a syntax-and interpretation-independent formulation of language, ontology, and ontological commitment in terms of institutions. We claim that our formalisation generalises the intuitive notion of ontology-based semantic integration while retaining its basic insight, and we apply it for eliciting and hence comparing various increasingly complex notions of semantic integration and ontological commitment based on differing understandings of semantics

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding how these stakeholders collaborate will enable us to improve editing environments that support such collaborations. We uncover how large ontology-engineering projects, such as the ICD in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users subsequently change) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic

a survey

Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising field

Collaborative ontology engineering: a survey

Building ontologies in a collaborative and increasingly community-driven fashion has become a central paradigm of modern ontology engineering. This understanding of ontologies and ontology engineering processes is the result of intensive theoretical and empirical research within the Semantic Web community, supported by technology developments such as Web 2.0. Over 6 years after the publication of the first methodology for collaborative ontology engineering, it is generally acknowledged that, in order to be useful, but also economically feasible, ontologies should be developed and maintained in a community-driven manner, with the help of fully-fledged environments providing dedicated support for collaboration and user participation. Wikis, and similar communication and collaboration platforms enabling ontology stakeholders to exchange ideas and discuss modeling decisions are probably the most important technological components of such environments. In addition, process-driven methodologies assist the ontology engineering team throughout the ontology life cycle, and provide empirically grounded best practices and guidelines for optimizing ontology development results in real-world projects. The goal of this article is to analyze the state of the art in the field of collaborative ontology engineering. We will survey several of the most outstanding methodologies, methods and techniques that have emerged in the last years, and present the most popular development environments, which can be utilized to carry out, or facilitate specific activities within the methodologies. A discussion of the open issues identified concludes the survey and provides a roadmap for future research and development in this lively and promising fiel

Drawing OWL 2 ontologies with Eddy the editor

In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments. This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification

An Ontology-based approach to integrating life cycle analysis and computer aided design

Ponencia presentada en el XII Congreso Internacional de Ingeniería de Proyectos celebrado en Zaragoza en el año 2008One of the principal problems faced by engineering design today is the exchange of product information across different applications and environments. Ontological engineering systems, an evolution of KBE (Knowledge-Based Engineering) systems, seek to facilitate this integration while incorporating additional design information. An ontology, in the engineering domain, can be defined as an explicit specification of a shared conceptualization. This paper proposes the integration of an ontology with a Computer Aided Design (CAD) program, while also accessing a database of information on environmental impact. The proposed ontology is based on the AsD (Assembly Design) formalism, which describes spatial relationships and features of CAD models. The use of OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) ensures machine interpretability and exchange across different environments. Ultimately, the ontology will be used to represent a CAD model and related information (such as joining methods, materials, tolerances) in formal terms. Concurrently, a database of information on environmental impact of the materials, processes and transport involved will be accessed to evaluate the model on an environmental level. As a practical illustration, the evaluation of an underwater camera is used as an example

Semantic-based policy engineering for autonomic systems

This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

[EN] Knowledge engineering relies on ontologies, since they provide formal descriptions of real¿world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi¿automatically or automatically from scratch. It not only improves the efficiency of the ontology development pro¿ cess but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great poten¿ tial of ontology learning, we present an automatic ontology¿based model evolution approach to ac¿ count for highly dynamic environments at runtime. This approach can extend initial models ex¿ pressed as ontologies to cope with rapid changes encountered in surrounding dynamic environ¿ ments at runtime. The main contribution of our presented approach is that it analyzes heterogene¿ ous semi¿structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology¿based model. Within this approach, we aim to automatically evolve an initial ontology¿based model through the ontology learning approach. Therefore, this approach is illustrated using a proof¿of¿concept implementation that demonstrates the ontology¿based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology¿based models. First, we consider a feature¿based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria¿based eval¿ uation to assess the content of the evolved models. Finally, we perform an expert¿based evaluation to assess an initial and evolved models¿ coverage from an expert¿s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.Jabla, R.; Khemaja, M.; Buendía García, F.; Faiz, S. (2021). Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments. Applied Sciences. 11(22):1-30. https://doi.org/10.3390/app112210770130112

Building ontologies in a domain oriented software engineering environment

Ontologies can be used in Domain Oriented Software Engineering Environments (DOSEEs) to organize and describe knowledge and to support management, acquisition and sharing of knowledge regarding some domain. However, ontology construction is not a simple task. Thus, it is necessary to provide tools that support ontology development. This paper discusses the use of ontologies to support domain-oriented software development in ODE, an Ontology-based software Development Environment, and presents ODEd, an ontology editor developed to satisfy the requirements for an ontology editor in a DOSEE. These requirements include the definition of concepts and relations using graphic representations, automatic generation of some classes of axioms, derivation of object frameworks from ontologies, and ontology instantiation and browsing.Eje: Ingeniería de Software y Bases de Datos (ISBD)Red de Universidades con Carreras en Informática (RedUNCI