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2012 IEEE 12th International Conference on Data Mining Workshops
Learning to Extract Entity Uniqueness from Web for Helping User Decision Making
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
Web entities are the building blocks of human knowledge and users are making decisions among vast varieties of entities. For example, recommendation systems generate lists of entities to users, but seldom show the reasons of recommendation such as the uniqueness of each item to assist user decision making. In this paper, we mathematically define Web entity uniqueness and uniqueness patterns, based on which we propose a novel unsupervised natural language learning algorithm for entity uniqueness extraction. We leverage the bootstrapping strategy to recognize uniqueness from the free-text Web corpus with assistance from semi-structured Web such as lists, tables and query logs. To avoid extracting the subjective entity uniqueness, which may bias user decision making, we propose the probabilistic likelihood of a uniqueness property using bipartite graph models over entities and properties. Experiments verify that our algorithms have higher accuracy and coverage of entity uniqueness extraction technique compared to other related algorithms. We also show by conducting a user study survey that entity uniqueness information indeed positively supports user decision making.
Index Terms:
Natural languages,Decision making,Humans,Bipartite graph,Algorithm design and analysis,Measurement,Airports,information extraction,Web entity,entity uniqueness,decision making
Citation:
Wenhan Wang, Ning Liu, Yiran Xie, "Learning to Extract Entity Uniqueness from Web for Helping User Decision Making," icdmw, pp.850-857, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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