Information Discovery from Semi-Structured Record Sets on the Web

In this book, we develop two frameworks to tackle the task of semi-structured Web data record extraction. We first present a record segmentation search tree framework in which a new search structure, named Record Segmentation Tree (RST), is designed and several efficient search pruning strategies on the RST structure are proposed to identify the records in a given Web page. We also present another DOM Structure Knowledge Oriented Global Analysis (Skoga) framework which can perform robust detection of different kinds of data records and record regions. Skoga can conduct a global analysis on the DOM structure to achieve effective detection. Finally, we present a framework that can make use of the detected data records to automatically populate existing Wikipedia categories. This framework takes a few existing entities that are automatically collected from a particular Wikipedia category as seed input and explores their attribute infoboxes to obtain clues for the discovery of more entities for this category and the attribute content of the newly discovered entities.

He is currently a postdoc fellow in The Chinese University of Hong Kong, where he received his PhD in 2012. Before that, he obtained his MPhil and BSc degrees from Peking University and Northeast Normal University respectively. He has research interests in Information Extraction, Information Retrieval, Web Mining, Natural Language Processing, etc.