关键词: 信息抽取, 最近公共祖先（LCA）, 基本语义块, 有效语义块, 关系映射

Abstract: Conventional information extraction methods of semi-structured pages usually assume that valid data have relatively strong structural similarity, divide the page into data records and data region with similar characteristics and then extract from them. However, faculty list pages of universities mostly are written artificially and filled by human beings instead of automatic generation by using templates, so their structure is not rigorous. This paper proposes a faculty information extraction method based on LCA (lowest common ancestor) segmentation algorithm, introduces the connection between LCA and semantic relation into Web segmentation, and presents the new concepts of basic semantic blocks and effective semantic blocks. After converting the page into a DOM (document object model) tree and the preprocessing, the page is divided into the basic semantic blocks with LCA algorithm firstly. Then the basic semantic blocks are merged into their corresponding effective semantic blocks with complete personnel information. Finally, according to the alignment of effective semantic blocks, all faculty information mapped by all relationships in current page is gotten. The experimental results show that the proposed method still has high precision and recall rates in the segmentation and extraction of quantities of real university research faculty list pages by compared with the MDR (mining data records) algorithm.

Key words: information extraction, lowest common ancestor (LCA), basic semantic block, effective semantic block, relational mapping