Markov Logic Networks With Its Application In Web

Probabilistic graphical models enable us to efficiently handle uncertainty. First-order logic enables us to compactly represent a wide variety of knowledge. Combining the both in a single representation has been a longstanding goal of AI research. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula, and can be viewed as a template for constructing Markov networks.The traditional statistical methods have focused on data independent and identically distributed (IID), and assumed that data has the same structure. However, there is so much semi-structured relational data in the Web. The data itself not only has a complex internal structure, but also is related to each other via different kinds of relations. The relationship is an important source of semantic information, which is often ignored by the traditional statistical methods.Statistical Relational Learning (SRL), is also known as an relationship learning method, which combines relational/first-order logic representations, Probabilistic reasoning mechanisms, with machine learning/data mining principles together, so as to capture the likelihood model from the relational data. Markov Logic Networks is a new Statistical Relational Learning model in which Markov networks integrates with the first-order logic. It has become concerned in AI research field and has been used in many fields such as World Wide Web, social networks, computational biology and ubiquitous computing.This thesis focuses on applying the Markov logic network in the Web field. The main research work is summarized as following:Firstly, the thesis studies the related theory of Markov Logic Networks.It summarizes the theoretical basises of Markov logic networks, which include probability graph model, Markov random field, Markov network and first-order logic. Then, we study the concepts and features of Markov logic networks, including closed Markov logic networks, three assumptions and Markov logic network knowledge base, and we also discuss two Markov logic network algorithms: weight learning algorithm and reasoning algorithms.Secondly, the thesis studies text classification based on Markov logic networks.In Statistical Relational Learning, relationship enables us to compactly represent a wide variety of knowledge, so the Markov logic of text classification problem is also very concise. Using discriminative learning algorithm for Markov Logic Networks weights, MC-SAT, Gibbs sampling and simulated tempering algorithm for inference in experiments, it proves that the method based on Markov Logic Networks is better than conventional KNN method in text classification.Thirdly, the thesis studies De-duplication based on Markov logic networks.This thesis show how a small number of predicate rules in Markov logic capture the essential features of a problem in De-duplication and combine these rules together to compose all kinds of model. Using discriminative learning algorithm for Markov Logic Networks weights, MC-SAT algorithm for inference in experiments, it proves that the method based on Markov Logic Networks not only covers the original Fellegi-Sunter model, but also achieves a better result than the traditional methods based on Clustering Algorithms and Similarity Measures in De-duplication. The experimental result also shows that Markov logic networks can be used for constructing the common framework for such problems.Fourthly, the thesis summarizes other potential applications of Markov logic networks in Web field.For information extraction, this thesis gives the methods of creating an imperfect Markov logic network in terms of how to detect field boundaries, which achieves a slightly better result than the traditional Hidden Markov Model. As for the hypertext classification, information retrieval, we just give the approaches of creating a relatively model.To sum up, we can draw the following conclusions: Markov logic networks is a powerful Statistical Relational Learning methods, which not only brings various domain knowledge into it in the form of modulars, but also handle uncertainty and permit imperfect and contradictory konwledge. Many important tasks are naturally formulated as instances of Markov logic networks.