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LiU Semantic Web research in Semantic Web Special issue on ontology and linked data matching

Postad i kategorin: Journal den 18 January, 2017

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Two papers with co-authors from our group are published in a special issue on ontology and linked data matching of the Semantic Web journal:

Lambrix P, Kaliyaperumal R, A Session-based Ontology Alignment Approach enabling User Involvement, Semantic Web Journal, 8(2):225-251, 2017.

Zhang Z, Gentile A, Blomqvist E, Augenstein I, Ciravegna F, An unsupervised data-driven method to discover equivalent relations in large Linked Datasets, Semantic Web Journal, 8(2):197-223, 2017.


Special issue on Visualization and Interaction for Ontologies and Linked Data

Postad i kategorin: Journal den 7 November, 2016

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Valentina Ivanova and Patrick Lambrix, together with Steffen Lohmann and Catia Pesquita are guest editors for a special issue on Visualization and Interaction for Ontologies and Linked Data for the Journal of Web Semantics. Submissions are due latest on January 31, 2017.

The topics for the special issue are similar to the topics of the VOILA 2016 workshop.


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Semantic Web research at LiU

The Semantic Web is an extension of the traditional Web towards having machine processable content and interlinked data on the Web, with well-described semantics.

Ideally we would be able to view the whole Web as a large set of interconnected databases, which can be queried and browsed by software systems as well as human users. Semantic Web research at LiU includes areas such as ontologies and ontology engineering (ontology design patterns, ontology alignment and completion, ontology and alignment visualisation), graph databases and triple stores, querying the Web of data, RDF stream processing and complex event processing, as well as applications of these technologies in a number of fields, including open data, security, decision support systems, e-health and e-science, etc.

The aim of this blog is to report on the activities of our research group, and make results accessible to a wider audience.

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