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 SDType, 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. © 2013 Springer-Verlag.
CITATION STYLE
Paulheim, H., & Bizer, C. (2013). Type inference on noisy RDF data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8218 LNCS, pp. 510–525). https://doi.org/10.1007/978-3-642-41335-3_32
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