Technological Spaces: An Initial Appraisal

In this paper, we propose a high level view of technological spaces (TS) and relations among these spaces. A technological space is a working context with a set of associated concepts, body of knowledge, tools, required skills, and possibilities. It is often associated to a given user community with shared know-how, educational support, common literature and even workshop and conference regular meetings. Although it is difficult to give a precise definition, some TSs can be easily identified, e.g. the XML TS, the DBMS TS, the abstract syntax TS, the meta-model (OMG/MDA) TS, etc. The purpose of our work is not to define an abstract theory of technological spaces, but to figure out how to work more efficiently by using the best possibilities of each technology. To do so, we need a basic understanding of the similarities and differences between various TSs, and also of the possible operational bridges that will allow transferring the results obtained in one TS to other TS. We hope that the presented industrial vision may help us putting forward the idea that there could be more cooperation than competition among alternative technologies. Furthermore, as the spectrum of such available technologies is rapidly broadening, the necessity to offer clear guidelines when choosing practical solutions to engineering problems is becoming a must, not only for teachers but for project leaders as well

The MOUSE approach: Mapping Ontologies using UML for System Engineers

To address the problem of semantic heterogeneity, there has been a large body of research directed toward the study of semantic mapping technologies. Although various semantic mapping technologies have been investigated,  facilitating the process for domain experts to perform a semantic data integration task is still not easy. This is because one is required not only to possess domain expertise but also to have a good understanding of knowledge engineering. This paper proposes an approach that automatically transforms an abstract semantic mapping syntax into a concrete executable mapping syntax, we call this approach MOUSE (Mapping Ontologies using UML for System Engineers). In order to evaluate MOUSE, an implementation of this approach for a semantic data integration use case has been developed (called SDI, Semantic Data Integration). The aim is to enable domain experts, particularly system engineers, to undertake mappings using a technology that they are familiar with (UML), while ensuring the created mappings are accurate and the approach is easy to use. The proposed UML-based abstract mapping syntax is evaluated through usability experiments conducted in a lab environment by participants who have skills equivalent to real life system engineers using the SDI tool. Results from the evaluations show that the participants could correctly undertake the semantic data integration task using the MOUSE approach while maintaining accuracy and usability (in terms of ease of use)

A Detailed Comparison of UML and OWL

As models and ontologies assume an increasingly central role in software and information systems engineering, the question of how exactly they compare and how they can sensibly be used together assumes growing importance. However, no study to date has systematically and comprehensively compared the two technology spaces, and a large variety of different bridging and integration ideas have been proposed in recent years without any detailed analysis of whether they are sound or useful. In this paper, we address this problem by providing a detailed and comprehensive comparison of the two technology spaces in terms of their flagship languages – UML and OWL – each a de facto and de jure standard in its respective space. To fully analyze the end user experience, we perform the comparison at two levels – one considering the underlying boundary assumptions and philosophy adopted by each language and the other considering their detailed features. We also consider all relevant auxiliary languages such as OCL. The resulting comparison clarifies the relationship between the two technologies and provides a solid foundation for deciding how to use them together or integrate them

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems

Model-based Approach for Product Requirement Representation and Generation in Product Lifecycle Management

The requirement specification is an official documentation activity, which is a collection of certain information to specify the product and its life-cycle activities in terms of functions, features, performance, constraints, production, maintenance, disposal process, etc. It contains mainly two phases; product requirement generation and representation. Appropriate criteria for the product design and further life-cycle activities are determined based on the requirement specification as well as the interrelations of product requirements with other life-cycle information such as; materials, manufacturing, working environments, finance, and regulations. The determination of these criteria is normally error-prone. It is difficult to identify and maintain the completeness and consistency of the requirement information across the product life-cycle. Product requirements are normally expressed in abstract and conceptual terms with document base representation which yields unstructured and heterogeneous information base and it is unsuitable for intelligent machine interpretations. Most of the time determination of the requirements and development of the requirement specification documents are performed by the designers/engineers based on their own experiences that might lead to incompleteness and inconsistency. This research work proposes a unique model-based product requirement representation and generation architecture to aid designers/engineers to specify product requirements across the product life-cycle. A requirement knowledge management architecture is developed to enhance the capabilities of the current Product Life-cycle Management (PLM) platforms in terms of product requirement representation and generation. After a systematic study on the categorization of product requirements, an ontological framework is developed for the specification of the requirements and related product life-cycle domain information. The ontological framework is embedded in an existing PLM system. A computational platform is developed and integrated into the PLM system for the intelligent machine processing of the product requirements and related information. This architecture supports product requirement representation in terms of the ontological framework and further information retrieval, inference, and requirement text generation activities

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Industrial Ecolog

Building product suggestions for a BIM model based on rule sets and a semantic reasoning engine

The architecture, engineering and construction (AEC) industry today relies on different information systems and computational tools built to support and assist in the building design and construction. However, these systems and tools typically provide this support in isolation from each other. A good combination of these systems and tools is beneficial for a better coordination and information management. Semantic web technologies and a Linked Data approach can be used to fulfil this aim. In this paper, we indicate how these technologies can be applied for one particular objective, namely to check a building information model (BIM) and make suggestions for that model regarding the building elements. These suggestions are based on information obtained from different data sources, including a BIM model, regulations and catalogues of locally available building components. In this paper, we briefly discuss the results obtained in the application of this approach in a case study based on structural safety requirements