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LOD Link Discovery

A commonly used representation of semantic enriched data is Linked Open Data (LOD) - data should be structured and made available in an open and non-proprietary format. Furthermore, resources are described by dereferencable HTTP URI’s and interlinked with other URI’s. Within the LOD Link Discovery project, we investigate techniques to improve the quality of the LOD data sets - either by creating new links or by improving the quality of already existing links. Context and relations between entities are exploited as well as holistic approaches for Link Discovery, e.g., employing multiple data sources. These techniques are complemented by methods improving the scalability of Link Discovery - parallelization via Hadoop cluster enables the use of MapReduce, In-Memory computation and graph processing systems.

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Nentwig, Markus; Hartung, Michael; Ngonga Ngomo, Axel-Cyrille; Rahm, Erhard
A Survey of Current Link Discovery Frameworks
Semantic Web Journal
2017-01
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Nentwig, Markus; Groß, Anika; Rahm, Erhard
Holistic Entity Clustering for Linked Data
IEEE International Conference on Data Mining Workshop, ICDMW 2016, Barcelona, Catalonia, Spain, December 12-15, 2016
2016-12
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publication iconNentwig, Markus; Groß, Anika; Rahm, Erhard
A Clustering Approach for Holistic Link Discovery (Project overview)
Proc. Lernen. Wissen. Daten. Analysen. (LWDA), Potsdam, September 2016, CEUR
2016-09
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Rahm, Erhard
The Case for Holistic Data Integration
Proc. ADBIS, Invited keynote paper, Springer LNCS 9809
2016-09
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Nentwig, Markus; Soru, Tommaso; Ngonga Ngomo, Axel-Cyrille; Rahm, Erhard
LinkLion: A Link Repository for the Web of Data
The Semantic Web: ESWC 2014 Satellite Events. LNCS 8798, pp. 439-443
2014