Data Cleaning publication categorizer

Schema-level heterogeneity represents an obstacle for auto-
mated discovery of coreference resolution links between in-
dividuals. Although there is a multitude of existing schema
matching solutions, the Linked Data environment diers
from the standard scenario assumed by these tools. In par-
ticular, large volumes of data are available, and repositories
are connected into a graph by instance-level mappings. In
this paper we describe how these features can be utilised to
produce schema-level mappings which facilitate the instance

Schema heterogeneity issues often represent an obstacle for discovering coreference links between individuals in semantic data repositories. In this paper we present an approach, which performs ontology schema matching in order to improve instance coreference resolution performance. A novel feature of the approach is its use of existing instance-level coreference links defined in third-party repositories as background knowledge for schema matching techniques.

Finding RDF individuals that refer to the same real-world entities but have different URIs is necessary for the efficient use of data across sources. The requirements for such instance-level integration of RDF data are different from both database record linkage and ontology schema matching scenarios. Flexible configuration and reuse of different methods is needed to achieve good performance.