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

Developing domain ontologies for courseware content

Ontologies have the potential to play an important role in educational technology. They can be used to represent knowledge about educational 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. Our investigation will be supported by a case study

Expressing the tacit knowledge of a digital library system as linked data

Library organizations have enthusiastically undertaken semantic web initiatives and in particular the data publishing as linked data. Nevertheless, different surveys report the experimental nature of initiatives and the consumer difficulty in re-using data. These barriers are a hindrance for using linked datasets, as an infrastructure that enhances the library and related information services. This paper presents an approach for encoding, as a Linked Vocabulary, the "tacit" knowledge of the information system that manages the data source. The objective is the improvement of the interpretation process of the linked data meaning of published datasets. We analyzed a digital library system, as a case study, for prototyping the "semantic data management" method, where data and its knowledge are natively managed, taking into account the linked data pillars. The ultimate objective of the semantic data management is to curate the correct consumers' interpretation of data, and to facilitate the proper re-use. The prototype defines the ontological entities representing the knowledge, of the digital library system, that is not stored in the data source, nor in the existing ontologies related to the system's semantics. Thus we present the local ontology and its matching with existing ontologies, Preservation Metadata Implementation Strategies (PREMIS) and Metadata Objects Description Schema (MODS), and we discuss linked data triples prototyped from the legacy relational database, by using the local ontology. We show how the semantic data management, can deal with the inconsistency of system data, and we conclude that a specific change in the system developer mindset, it is necessary for extracting and "codifying" the tacit knowledge, which is necessary to improve the data interpretation process

Ontology-assisted database integration to support natural language processing and biomedical data-mining

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the purposes of making computers understand medical natural language

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