A Service Annotation Quality Improvement Approach Based on Efficient Human Intervention

Abstract

Semantic Annotation plays an essential role in automatic service discovery and composition. However, existing approaches and tools cannot achieve high annotation quality to ensure the semantic service application. Meanwhile, the semi-automatic strategies for improving the annotation quality are time-consuming. To further improve the efficiency as well as the quality of the annotation, this paper presents an effective method involving human-computer interaction to further optimize the annotation procedure. Besides employing the feedback and propagation strategy to semi-automatically improve the annotation quality, the strategy to involve the manual annotation is developed when the efficiency of semi-automatically strategy is related low. To optimize the manual annotation procedure, a clustering based approach is presented to select the most impacted candidates to optimize the annotation improvement. In addition, to help the annotators to choose the correct annotation, the local ontology restriction based method is further designed to improve the recommendation performance. The experiments show that our approach effectively involving the human intervention can significantly improve the annotation quality, faster the quality improvement procedure and reduce the manual load by increasing the recommendation accuracy.