Matches in ScholarlyData for { ?s ?p It has been argued that linked open data is the major benefit of the use of semantic technologies on the web as it provides a huge amount of structured data that can be accessed in a more effective way than web pages. While linked open data avoids many problems connected with the use of expressive ontologies, e.g. the knowlegde acquisition bottleneck, data heterogeneity remains a problem. In particular, the same objects may be referred to using different URIs in different data sets. Identifying such representations of the same object is called object reconciliation. In this paper, we propose a novel object reconciliation method that is based on an existing semantic similarity measure for linked data. We adapt the measure to the object reconciliation problem, present complete and approximate algorithms for efficiently computing the methods and present a systematic evaluation of the method based on a benchmark dataset. As our main result, we show that the use of light-weight ontologies and schema information significantly improves object reconciliation in the context of linked open data.. }
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- 32 abstract "It has been argued that linked open data is the major benefit of the use of semantic technologies on the web as it provides a huge amount of structured data that can be accessed in a more effective way than web pages. While linked open data avoids many problems connected with the use of expressive ontologies, e.g. the knowlegde acquisition bottleneck, data heterogeneity remains a problem. In particular, the same objects may be referred to using different URIs in different data sets. Identifying such representations of the same object is called object reconciliation. In this paper, we propose a novel object reconciliation method that is based on an existing semantic similarity measure for linked data. We adapt the measure to the object reconciliation problem, present complete and approximate algorithms for efficiently computing the methods and present a systematic evaluation of the method based on a benchmark dataset. As our main result, we show that the use of light-weight ontologies and schema information significantly improves object reconciliation in the context of linked open data.".