Abstract. Óbuda University wanted to build a linked dataset describing their courses in the semester. The concepts to be covered included curricula, subjects, courses, semesters and educators. A particular use case needed the description of lecture rooms and events as well. Although there are several ontologies for the mentioned domains, selecting a set of ontologies fitting our use case was not an easy task. After realizing the problems, we created the Ontology for Linked Open University Data (OLOUD) to fill in the gaps between re-used ontologies. OLOUD acts as a glue for a selection of existing ontologies, and thus enables us to formulate SPARQL queries for a wide range of practical questions of university students. OLOUD integrates data from several sources and provides personal timetables, navigation and other types of help for students and lecturers.Keywords: Ontology · Linked Open Data · Linked Open University Data · SPARQL IntroductionIn this paper we focus on a special segment of open data at the university domain: university courses. We aim to facilitate the implementation of Smart Universities [1] by defining a common data model for course information. Ontological representation as the most modern description method for the problem domain was chosen. Originally our objective was to develop a generic data model for university course related data. During our work we noticed that though the Bologna Process ensures a certain level of compatibility for education systems in the EU, this does not reach deeper constructs regarding the educational model. We found that the meaning of the main concepts (like course, subject and study programme) is quite different in currently available educational models in Europe. Presenting course related information requires a lot of data originating from multiple information systems at a typical university. As these systems are usually not fully integrated and the access to the data is limited, significant effort is necessary to suc-

Abstract-Linked Data resources are identified by Uniform Resource Identifiers. It is an important step in any LinkedData project to define the conventions for URI assignments. In some cases resources already have their natural identifiers, or they can be inherited from previous databases. However, there are cases when frequent insertions of triple sets occur without any convenient way for identification and grouping of them. In this paper we elaborate on a mechanism that makes handling complex and frequent insertions easier, and also provides the benefits of simple authoring together with rich querying and reasoning on the data. We show how to eliminate some of the time consuming and error prone aspects of Linked Data authoring by introducing the self-unfolding URI concept. This solution generates RDF description to entities based on information encoded in their URIs. For the generation of these new RDF triples we propose templates that can be implemented by SPARQL Insert queries.

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Linked Data is a practical application of the Semantic Web technologies for connecting data worldwide. The Open Data pursuit has achieved remarkable progress in Europe as well, and studies have shown that it has a positive impact on the quality of education at university level too. Publishing information about university or college course, their corresponding places and related events, such as exams in Linked Data format allows the event information to be aggregated, filtered and delivered to potential participants: students and lecturers via multiple channels and devices. In the previously published version of our Linked Open University Data model organisational hierarchy, staff and publication data were modelled as Linked Data. With this model one can describe faculties, professors, their teaching and research activities, etc. as Linked Data giving a skeleton of human organisation of a university. In this paper we extend the model with temporal and location data including course schedule, event data and indoor location data. To this end we present the state of the current semantic representation formats for course data, event data and indoor location data and describe our approach for creating a unified schema for Linked Open University Data. We have reviewed several overlapping RDF schema and vocabulary, their potential combinations and usability in practice. Finally a formal representation of the model is presented as an OWL 2 ontology. We also provide some use cases where this machine readable data demonstrates its benefits, as well as some RDF data excerpt based on our model and some example SPARQL query