Affiliations: University of Ljubljana, Faculty of Computer and Information Science, Tržaska cesta 25, SI-1000 Ljubljana, e-mail: {slavko.zitnik, lovro.subelj, dejan.lavbic}@fri.uni-lj.si, [email protected] | Information Systems Research Laboratory, Vilnius Gediminas Technical University, Saulėtekio 11, LT-10223 Vilnius, Lithuania, e-mail: [email protected]
Abstract: Due to numerous public information sources and services, many methods to combine heterogeneous data were proposed recently. However, general end-to-end solutions are still rare, especially systems taking into account different context dimensions. Therefore, the techniques often prove insufficient or are limited to a certain domain. In this paper we briefly review and rigorously evaluate a general framework for data matching and merging. The framework employs collective entity resolution and redundancy elimination using three dimensions of context types. In order to achieve domain independent results, data is enriched with semantics and trust. However, the main contribution of the paper is evaluation on five public domain-incompatible datasets. Furthermore, we introduce additional attribute, relationship, semantic and trust metrics, which allow complete framework management. Besides overall results improvement within the framework, metrics could be of independent interest.
