关键词: 知识学习, 马尔可夫逻辑网, 分类, 支持向量机

Abstract: It has been the focus of academia and application field since knowledge discovery in data is proposed, but facing the increasingly complex application environment, most of the existing research methods are based on statistical learning, which often neglect the relationship between knowledge and the knowledge change over time, leading to that the effect of application is often just passable. How to grasp the statistical relationship between knowledge accurately to make the correct knowledge learning, have become a problem in knowledge research. Recently, with the development of research on statistical relational learning, Markov logic networks, which combines the probability graph and first-order logic theory, is applied successfully to natural language processing, machine learning, social network analysis and so on. In this paper, a Markov Logic Networks is presented for knowledge learning, which brings in first-order logic to learn the relationship between knowledge, and finish logical reasoning. Compared with SVM, this proposal method gets about 10 percent higher effect by classification knowledge learning in the text categorization fields.

Key words: knowledge learning, Markov logic networks, classification, Support Vector Machine（SVM）