Academic Conference Homepage Understanding Using Constrained Hierarchical Conditional Random Fields

We address the problem of academic conference homepage understanding for the Semantic Web. This problem consists of three labeling tasks- labeling conference function pages, function blocks, and attributes. Different from traditional information extraction tasks, the data in academic conference homepages has complex structural dependencies across multiple Web pages. In addition, there are logical constraints in the data. In this paper, we propose a unified approach, Constrained Hierarchical Conditional Random Fields, to accomplish the three labeling tasks simultaneously. In this approach, complex structural dependencies can be well described. Also, the constrained Viterbi algorithm in the inference process can avoid logical errors. Experimental results on real world conference data have demonstrated that this approach performs better than cascaded labeling methods by 3.6 % in F1-measure and that the constrained inference process can improve the accuracy by 14.3%. Based on the proposed approach, we develop a prototype system of useoriented semantic academic conference calendar. The user simply needs to specify what conferences he/she is interested in. Subsequently, the system finds, extracts, and updates the semantic information from the Web, and then builds a calendar automatically for the user. The semantic conference data can be used in other applications, such as finding sponsors and finding experts. The proposed approach can be used in other information extraction tasks as well

A social recommendation framework based on multi-scale continuous conditional random fields

This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social rec-ommendation emphasizes utilizing various attributes infor-mation and relations in social networks to assist recom-mender systems. Although recommendation techniques have obtained distinct developments over the decades, traditional CF algorithms still have these following two limitations: (1) relational dependency within predictions, an important fac-tor especially when the data is sparse, is not being uti-lized effectively; and (2) straightforward methods for com-bining features like linear integration suffer from high com-puting complexity in learning the weights by enumerating the whole value space, making it difficult to combine var-ious information into an unified approach. In this paper, we propose a novel model, Multi-scale Continuous Condi-tional Random Fields (MCCRF), as a framework to solve above problems for social recommendations. In MCCRF, relational dependency within predictions is modeled by the Markov property, thus predictions are generated simultane-ously and can help each other. This strategy has never been employed previously. Besides, diverse information and rela-tions in social network can be modeled by state and edge feature functions in MCCRF, whose weights can be opti-mized globally. Thus both problems can be solved under this framework. In addition, We propose to utilize Markov chain Monte Carlo (MCMC) estimation methods to solve the difficulties in training and inference processes of MCCRF. Experimental results conducted on two real world data have demonstrated that our approach outperforms traditional CF algorithms. Additional experiments also show the improve-ments from the two factors of relational dependency and feature combination, respectively