Matches in ScholarlyData for { ?s ?p This paper presents an approach for the interactive discovery of relationships between selected elements via the Semantic Web. It fills the gap between algorithms that find relationships in datasets of the Semantic Web and their efficient usage in real-world contexts. Selected elements are first semi-automatically mapped to unique objects of Semantic Web datasets. These datasets are then crawled for relationships which are presented both, in detail and overview. Interactive features and visual clues allow for sophisticated exploration of the found relationships on different levels. The general process is described and the RelFinder tool as a concrete implementation and proof-of-concept is presented. The benefits and application potentials are illustrated by a scenario which uses the RelFinder and DBpedia to assist a business analyst in decision-making. Finally, the approach is evaluated in a user study, and discussed and compared with related work.. }
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- 31 abstract "This paper presents an approach for the interactive discovery of relationships between selected elements via the Semantic Web. It fills the gap between algorithms that find relationships in datasets of the Semantic Web and their efficient usage in real-world contexts. Selected elements are first semi-automatically mapped to unique objects of Semantic Web datasets. These datasets are then crawled for relationships which are presented both, in detail and overview. Interactive features and visual clues allow for sophisticated exploration of the found relationships on different levels. The general process is described and the RelFinder tool as a concrete implementation and proof-of-concept is presented. The benefits and application potentials are illustrated by a scenario which uses the RelFinder and DBpedia to assist a business analyst in decision-making. Finally, the approach is evaluated in a user study, and discussed and compared with related work.".