Semantic Service Matchmaking (SSM), random forest, logic-based service matchmaking, false-positive, false-negative
Semantic Service Matchmaking (SSM) can be leveraged for mining the most suitable service to accommodate a diversity of user demands. However, existing research on SSM mostly involves logical or non-logical matching, leading to unavoidable false-positive and false-negative problems. Combining different types of SSM methods is an effective way to improve this situation, but the adaptive combination of different service matching methods is still a difficult issue. To conquer this difficulty, a hybrid SSM method, which is based on a random forest and combines the advantages of existing SSM methods, is proposed in this paper. The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest, converting the service matching into a two classification problem. Therefore, our method avoids the flaws found in manual threshold setting. Experimental results show that the proposed method achieves an outstanding performance.
Tsinghua University Press
Wei Jiang, Junyu Lin, Huiqiang Wang et al. Hybrid Semantic Service Matchmaking Method Based on a Random Forest. Tsinghua Science and Technology 2020, 25(6): 798-812.