A Pattern Based Approach for Re-engineering Non-Ontological Resources into Ontologies

With the goal of speeding up the ontology development process, ontology engineers are starting to reuse as much as possible available ontologies and non-ontological resources such as classification schemes, thesauri, lexicons and folksonomies, that already have some degree of consensus. The reuse of such non-ontological resources necessarily involves their re-engineering into ontologies. Non-ontological resources are highly heterogeneous in their data model and contents: they encode different types of knowledge, and they can be modeled and implemented in different ways. In this paper we present (1) a typology for non-ontological resources, (2) a pattern based approach for re-engineering non-ontological resources into ontologies, and (3) a use case of the proposed approach

Developing domain ontologies for course content

Ontologies have the potential to play an important role in instructional design and the development of course content. They can be used to represent knowledge about content, supporting instructors in creating content or learners in accessing content in a knowledge-guided way. While ontologies exist for many subject domains, their quality and suitability for the educational context might be unclear. For numerous subjects, ontologies do not exist. We present a method for domain experts rather than ontology engineers to develop ontologies for use in the delivery of courseware content. We will focus in particular on relationship types that allow us to model rich domains adequately

The use of ontologies for effective knowledge modelling and information retrieval

© 2017 The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management 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

Mapping relational data model to OWL ontology: knowledge conceptualization in OWL

In this paper, we introduce the issues and solutions of using OWL ontology to model extra restriction on 'Properties' of 'Classes' that are not provided by OWL specifications and to represent associations amongst 'Properties' other than 'Classes'. Two specific types of knowledge that cannot be modeled directly using OWL DL elements are identified and presented. Firstly the data value range constraint for a "DatatypeProperty"; secondly the calculation knowledge representation. Our approach to such issues is to conceptualize the knowledge in OWL and map the conceptualization in an implementation. Examples for each type of the knowledge and their OWL code are provided in detail to demonstrate our approach

Automatic Transformation of Relational Database Schema into OWL Ontologies

Ontology alignment, or ontology matching, is a technique to map different concepts between ontologies. For this purpose at least two ontologies are required. In certain scenarios, such as data integration, heterogeneous database integration and data model compatibility evaluation, a need to transform a relational database schema to an ontology can arise. To conduct a successful transformation it is necessary to identify the differences between relational database schema and ontology information representation methods, and then to define transformation rules. The most straight forward but time consuming way to carry out transformation is to do it manually. Often this is not an option due to the size of data to be transformed. For this reason there is a need for an automated solution.The automatic transformation of OWL ontology from relational database schema is presented in this paper; the data representation differences between relational database schema and OWL ontologies are described; the transformation rules are defined and the transformation tool’s prototype is developed to perform the described transformation