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Latif A., Afzal M. T., Us Saeed A., Höfler Patrick

Harvesting Pertinent Resources from Linked Open Data

Journal of Digital Information Management, 2010

Linked Open Data (LOD) is becoming an essentialpart of the Semantic Web. Although LOD has amassed largequantities of structured data from diverse, openly availabledata sources, there is still a lack of user-friendly interfaces andmechanisms for exploring this huge resource. In this paper, wedescribe a methodology for harvesting relevant information fromthe gigantic LOD cloud. The methodology is based on combinationof information: identification, extraction, integration andpresentation. Relevant information is identified by using a setof heuristics. The identified information resource is extracted byemploying an intelligent URI discovery technique. The extractedinformation is further integrated with the help of a Concept AggregationFramework. Then the information is presented to endusers in logical informational aspects. Thereby, the proposedsystem is capable of hiding complex underlying semantic mechanicsfrom end users and reducing the users’ cognitive loadin locating relevant information. In this paper, we describe themethodology and its implementation in the CAF-SIAL system,and compare it with the state of the art
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