Detecting and exploiting stability in evolving heterogeneous information spaces

Papadakis, G; Giannakopoulos, G; Niederee, C; Palpanas, T; Nejdl, W

dc.contributor.author	Papadakis, G	en
dc.contributor.author	Giannakopoulos, G	en
dc.contributor.author	Niederee, C	en
dc.contributor.author	Palpanas, T	en
dc.contributor.author	Nejdl, W	en
dc.date.accessioned	2014-03-01T02:53:06Z
dc.date.available	2014-03-01T02:53:06Z
dc.date.issued	2011	en
dc.identifier.issn	15525996	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36181
dc.subject	entity evolution	en
dc.subject	n-gram graphs	en
dc.subject	stability detection	en
dc.subject.other	Classification scheme	en
dc.subject.other	Data sets	en
dc.subject.other	entity evolution	en
dc.subject.other	Experimental studies	en
dc.subject.other	Heterogeneous information	en
dc.subject.other	Information integration	en
dc.subject.other	n-gram graphs	en
dc.subject.other	Semi structured data	en
dc.subject.other	Stable parts	en
dc.subject.other	Structured data	en
dc.subject.other	Information retrieval	en
dc.subject.other	User interfaces	en
dc.subject.other	Digital libraries	en
dc.title	Detecting and exploiting stability in evolving heterogeneous information spaces	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1145/1998076.1998094	en
heal.identifier.secondary	http://dx.doi.org/10.1145/1998076.1998094	en
heal.publicationDate	2011	en
heal.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. © 2011 ACM.	en
heal.journalName	Proceedings of the ACM/IEEE Joint Conference on Digital Libraries	en
dc.identifier.doi	10.1145/1998076.1998094	en
dc.identifier.spage	95	en
dc.identifier.epage	104	en