OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

Semantic model-driven development of web service architectures.

Building service-based architectures has become a major area of interest since the advent of Web services. Modelling these architectures is a central activity. Model-driven development is a recent approach to developing software systems based on the idea of making models the central artefacts for design representation, analysis, and code generation. We propose an ontology-based engineering methodology for semantic model-driven composition and transformation of Web service architectures. Ontology technology as a logic-based knowledge representation and reasoning framework can provide answers to the needs of sharable and reusable semantic models and descriptions needed for service engineering. Based on modelling, composition and code generation techniques for service architectures, our approach provides a methodological framework for ontology-based semantic service architecture

Engineering an Ontology for Autonomous Systems - The OASys Ontology

This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programe on autonomous systems’ engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems’ stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system’s conceptual models. The objective is to obtain and to use these models as main input for the autonomous system’s model-based control system

Model-based Engineering of Autonomous Systems using Ontologies and Metamodels

Our research focuses on engineering processes for autonomous intelligent systems construction with a life-cycle holistic view, by means of a model-based framework. The conceptual core of the framework is ontologically-driven. Our ontological approach consists of two elements. The first one is a domain Ontology for Autonomous Systems (OASys) to capture the autonomous system structure, function and behaviour. The second element is an Ontology-driven Engineering Methodology (ODEM) to develop the target autonomous system. This methodology is based on Model-based Systems Engineering and produces models of the system as core assets. These models are used through the whole system life-cycle, from implementation or validation to operation and maintenance. On the application side, the ontological framework has been used to develop a metacontrol engineering technology for autonomous systems, the OM Engineering Process (OMEP), to improve their runtime adaptivity and resilience. OMEP has been applied to a mobile robot in the form of a metacontroller built on top of the robot's control architecture. It exploits a functional model of the robot (TOMASys Model) to reconfigure its control if required by the situation at runtime. The functional model is based on a metamodel about controller function and structure using concepts form the ontology. The metacontroller was developed using the ontology-driven methodology and a robot control reference architecture

An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

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

Constructing an Office Domain Ontology using Knowledge Engineering Process

Knowledge based identification of human activities in systems depends primarily on rich contextual domain knowledge casing all of the information about the human, objects around human and also relations amongst them. Knowledge engineering plays an important role in building knowledge based expert systems, to solve complex problems such as human activity recognition. This requires formal representation of the knowledge which is based on the conceptualization of the domain. Ontology is awidely chosen representational model that depicts knowledge as a set of concepts. In this work, we have applied knowledge engineering process for constructingthe domain ontology of the officeenvironment in agreement with the ontology development life cycle

The design research pyramid: a three layer framework

To support knowledge-based design development, considerable research has been conducted from various perspectives at different levels. The research on knowledge-based design support systems, generic design artefact and design process modelling, and the inherent quality of design knowledge itself are some examples of these perspectives. The structure underneath the research is not a disparate one but ordered. This paper provides an overview of some ontologies of design knowledge and a layered research framework of knowledge-based engineering design support. Three layers of research are clarified in this pattern: knowledge ontology, design knowledge model, and application. Specifically, the paper highlights ontologies of design knowledge by giving a set of classifications of design knowledge from different points of view. Within the discussion of design knowledge content ontology, two topologies, i.e., teleology and evolutionary, are identified

Biomedical ontology alignment: An approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results