Academic conference homepage understanding using constrained hierarchical conditional random fields

ABSTRACT

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 use-oriented 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.

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