规则半自动学习的概率软逻辑推理模型

Probabilistic soft logic reasoning model with semi-automatic rule learning

概率软逻辑(PSL)作为一种基于声明式规则的概率模型,具有极强的扩展性和多领域适应性,目前为止,它需要人为给出大量的常识和领域知识作为规则确立的先决条件,这些知识的获取往往非常昂贵并且其中包含的不正确的信息可能会影响推理的正确性。为了缓解这种困境,将C5.0算法和概率软逻辑相结合,让数据和知识共同驱动推理模型,提出了一种规则半自动学习方法。该方法利用C5.0算法提取规则,再辅以人工规则和优化调节后的规则作为改进的概率软逻辑输入。实验结果表明,在学生成绩预测问题上所提方法比C5.0算法和没有规则学习的概率软逻辑具有更高的精度;和纯手工定义规则的方法相比,所提方法能大幅降低人工成本;和贝叶斯网络(BN)、支持向量机(SVM)等算法相比,该方法也表现出不错的效果。

Probabilistic Soft Logic(PSL),as a kind of declarative rule-based probability model,has strong extensibility and multi-domain adaptability.So far,it requires a lot of common sense and domain knowledge as preconditions for rule establishment.The acquisition of these knowledge is often very expensive and the incorrect information contained therein may reduce the correctness of reasoning.In order to alleviate this dilemma,the C5.0 algorithm and probabilistic soft logic were combined to make the data and knowledge drive the reasoning model together,and a semi-automatic learning method was proposed.C5.0 algorithm was used to extract rules,and artificial rules and optimized adjusted rules were supplemented as improved probabilistic soft logic input.The experimental results show that the proposed method has higher accuracy than the C5.0 algorithm and the PSL without rule learning on student performance prediction.Compared with the past method with pure hand-defined rules,the proposed method can significantly reduce the manual costs.Compared with Bayesian Network(BN),Support Vector Machine(SVM)and other algorithms,the proposed method also shows good results.