DBpediaNYD - A Silver Standard Benchmark Dataset for Semantic Relatedness in DBpedia

Determining the semantic relatedness (i.e., the strength of a relation) of two resources in DBpedia (or other linked data sources) is a problem addressed by quite a few approaches in the recent past. However, there are no large-scale benchmark datasets for comparing such approaches, and it is an open problem to determine which of the approaches work better than others. Furthermore, large-scale datasets for training machine learning based approaches are not available. DBpedia-NYD is a large-scale synthetic silver standard benchmark dataset which supports contains symmetric and asymmetric similarity values, obtained using a web search engine.

Provided by: University of Manitoba Topic: Data Management Date Added: Aug 2013 Format: PDF

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