Assessing the educational linked data landscape

d’Aquin, Mathieu; Adamou, Alessandro; Dietze, Stefan

doi:10.1145/2464464.2464487

Cited by 35 publications

(27 citation statements)

References 2 publications

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“…In this case no representations for course, semester or lecture room concepts were found. The general state of linked data for education is studied in [5] containing statistics on vocabulary re-use. We found another very useful review of ontologies for modelling course information in higher education [6].…”

Section: Related Workmentioning

confidence: 99%

A Case Study on Linked Data for University Courses

Szász

Fleiner

Micsik

2017

Lecture Notes in Computer Science

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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-

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Section: Related Workmentioning

confidence: 99%

A Case Study on Linked Data for University Courses

Szász

Fleiner

Micsik

2017

Lecture Notes in Computer Science

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“…In many cases classes and relations across such schemas are redundant and not aligned (with only 2% of schemas aligned [23]). As a consequence this leads to a disintegrated dataset landscape [8].…”

Section: Introductionmentioning

confidence: 99%

Online Relation Alignment for Linked Datasets

Koutraki¹,

Preda²,

Vodislav³

2017

The Semantic Web

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The large number of linked datasets in the Web, and their diversity in terms of schema representation has led to a fragmented dataset landscape. Querying and addressing information needs that span across disparate datasets requires the alignment of such schemas. Majority of schema and ontology alignment approaches focus exclusively on class alignment. Yet, relation alignment has not been fully addressed, and existing approaches fall short on addressing the dynamics of datasets and their size. In this work, we address the problem of relation alignment across disparate linked datasets. Our approach focuses on two main aspects. First, online relation alignment, where we do not require full access, and sample instead for a minimal subset of the data. Thus, we address the main limitation of existing work on dealing with the large scale of linked datasets, and in cases where the datasets provide only query access. Second, we learn supervised machine learning models for which we employ various features or matchers that account for the diversity of linked datasets at the instance level. We perform an experimental evaluation on real-world linked datasets, DBpedia, YAGO, and Freebase. The results show superior performance against state-of-the-art approaches in schema matching, with an average relation alignment accuracy of 84%. In addition, we show that relation alignment can be performed efficiently at scale.

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“…In addition, while sharing of vocabularies and vocabulary terms is a crucial requirement for enabling reuse, the Web of data still features a large amount of highly overlapping and often unlinked vocabularies [8].…”

Section: Introductionmentioning

confidence: 99%

Retrieval, Crawling and Fusion of Entity-centric Data on the Web

Dietze

2017

Semantic Keyword-Based Search on Structured Data Sources

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Abstract. While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs.

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Assessing the educational linked data landscape

Cited by 35 publications

References 2 publications

A Case Study on Linked Data for University Courses

A Case Study on Linked Data for University Courses

Online Relation Alignment for Linked Datasets

Retrieval, Crawling and Fusion of Entity-centric Data on the Web

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