PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Detecting and Exploiting Stability in Evolving Heterogeneous Information Spaces
George Papadakis, George Giannakopoulos, Claudia Niederée, Themis Palpanas and Wolfgang Nejdl
In: JCDL 2011, 13-17 June 2011, Ottawa, Canada.

Abstract

Individuals contribute content on the Web at an unprecedented rate, accumulating immense quantities of (semi-)structured data. Wisdom of the Crowds theory advocates that such information (or parts of it) is constantly overwritten, updated, or even deleted by other users, with the goal of rendering it more accurate, or up-to-date. This is particularly true for the collaboratively edited, semi-structured data of entity repositories, whose entity profiles are consistently kept fresh. Therefore, their core information that remain stable with the passage of time, despite being reviewed by numerous users, are particularly useful for the description of an entity. Based on the above hypothesis, we introduce a classification scheme that predicts, on the basis of statistical and content patterns, whether an attribute (i.e., name-value pair) is going to be modified in the future. We apply our scheme on a large, real-world, versioned dataset and verify its effectiveness. Our thorough experimental study also suggests that reducing entity profiles to their stable parts conveys significant benefits to two common tasks in computer science: information retrieval and information integration.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:8429
Deposited By:George Giannakopoulos
Deposited On:29 December 2011