Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Ontology Matching
Replica identification using genetic programming
Proceedings of the 2008 ACM symposium on Applied computing
On active learning of record matching packages
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Linked Data
A Genetic Programming Approach to Record Deduplication
IEEE Transactions on Knowledge and Data Engineering
Learning expressive linkage rules using genetic programming
Proceedings of the VLDB Endowment
Introduction to linked data and its lifecycle on the web
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
Active learning of expressive linkage rules using genetic programming
Web Semantics: Science, Services and Agents on the World Wide Web
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The amount of data that is available as Linked Data on the Web has grown rapidly over the last years. However, the linkage between data sources remains sparse as setting RDF links means effort for the data publishers. Many existing methods for generating these links rely on explicit linkage rules which specify the conditions which must hold true for two entities in order to be interlinked. As writing good linkage rules by hand is a non-trivial problem, the burden to generate links between data sources is still high. In order to reduce the effort and required expertise to write linkage rules, we present an approach which combines genetic programming and active learning for the interactive generation of expressive linkage rules. Our approach automates the generation of a linkage rule and only requires the user to confirm or decline a number of example links. The algorithm minimizes user involvement by selecting example links which yield a high information gain. The proposed approach has been implemented in the Silk Link Discovery Framework. Within our experiments, the algorithm was capable of finding linkage rules with a full F1-measure by asking the user to confirm or decline a maximum amount of 20 links.