Type Inference on Noisy RDF Data

Presented at: The 12th International Semantic Web Conference (ISWC2013)

by Heiko Paulheim, Christian Bizer

Webpage: http://www.springer.com/computer/ai/book/978-3-642-41334-6
Webpage: https://github.com/lidingpku/iswc-archive/raw/master/paper/iswc-2013/82180497-type-inference-on-noisy-rdf-data.pdf

Type information is very valuable in knowledge bases. However, most large open knowledge bases are incomplete with respect to type information, and, at the same time, contain noisy and incorrect data. That makes classic type inference by reasoning difficult. In this paper, we propose the heuristic link-based type inference mechanism SD-Type, which can handle noisy and incorrect data. Instead of leveraging T-box information from the schema, SDType takes the actual use of a schema into account and thus is also robust to misused schema elements.

Type Inference on Noisy RDF Data was presented at this event.


Resource URI on the dog food server: http://data.semanticweb.org/conference/iswc/2013/proceedings-1/paper-32


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