Storing and Querying Ontologies in Logic Databases

The intersection of Description Logic inspired ontology languages with Logic Programs has been recently analyzed in [GHVD03]. The resulting language, called Description Logic Programs, covers RDF Schema and a notable portion of OWL Lite. However, the proposed mapping in [GHVD03] from the corresponding OWL fragment into Logic Programs has shown scalability as well as representational de�cits within our experiments and analysis. In this paper we propose an alternative mapping resulting in lower computational complexity and more representational exibility. We also present benchmarking results for both mappings with ontologies of di�erent size and complexity

Semantic Storage: Overview and Assessment

The Semantic Web has a great deal of momentum behind it. The promise of a ‘better web’, where information is given well defined meaning and computers are better able to work with it has captured the imagination of a significant number of people, particularly in academia. Language standards such as RDF and OWL have appeared with remarkable speed, and development continues apace. To back up this development, there is a requirement for ‘semantic databases’, where this data can be conveniently stored, operated upon, and retrieved. These already exist in the form of triple stores, but do not yet fulfil all the requirements that may be made of them, particularly in the area of performing inference using OWL. This paper analyses the current stores along with forthcoming technology, and finds that it is unlikely that a combination of speed, scalability, and complex inferencing will be practical in the immediate future. It concludes by suggesting alternative development routes

Semantic Data Storage in Information Systems

The storage and retrieval of information are important functions of information systems (IS). These IS functions have been realized for decades, due to the maturity of the relational database technology. In recent years, the concept of Semantic Information System (SIS) has emerged as IS in which information is represented with explicit semantic based on its meaning rather than its syntax to enable its automatic and intelligent processing by computers. At present, there is a shortage of discussions on the topic of semantic data storage in IS as compared to the relational database storage counterpart. This study uses a combination of qualitative and quantitative methods to discuss semantic data storage in IS. The qualitative method is by means of literature review to learn the existing techniques for representing and storing semantic data. The quantitative method is done with experiments to empirically discuss these techniques. The empirical findings of the study shed light on the technologies and approaches utilised to store semantic data in relational databases. This may contribute to the understanding of semantic technologies in IS and foster the development of semantic information systems

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

AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure