
Information Discovery from Semi-structured Record Sets on the Web
From Web Pages to Knowledge
LAP Lambert Academic Publishing
Published on 28. February 2014
Book
Paperback/Softback
124 pages
978-3-659-20611-5 (ISBN)
Description
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.
More details
Language
English
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 9 mm
Weight
203 gr
ISBN-13
978-3-659-20611-5 (9783659206115)
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Schweitzer Classification
Persons
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.