Evolutionary fine-tuning of automated semantic annotation systems

作者：

Highlights：

• A method to find “best” configuration parameters for semantic annotators is proposed.

• We explore the challenges of supervised training specific to semantic annotators.

• We test our method on four popular semantic annotators.

• Semantic annotation performance improved over their default configuration.

摘要

•A method to find “best” configuration parameters for semantic annotators is proposed.•We explore the challenges of supervised training specific to semantic annotators.•We test our method on four popular semantic annotators.•Semantic annotation performance improved over their default configuration.

论文关键词：Semantic annotation,Automated configuration,Genetic algorithm,Parameter learning

论文评审过程：Available online 9 May 2015, Version of Record 30 May 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.054