Searching Web 2.0 data through entity-based aggregation

Ekaterini Ioannou*, Yannis Velegrakis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Entity-based searching has been introduced as a way of allowing users and applications to retrieve information about a specific real world object such as a person, an event, or a location. Recent advances in crawling, information extraction, and data exchange technologies have brought a new era in data management, typically referred to through the term Web 2.0. Entity searching over Web 2.0 data facilitates the retrieval of relevant information from the plethora of data available in semantic and social web applications. Effective entity searching over a variety of sources requires the integration of the different pieces of information that refer to the same real world entity. Entity-based aggregation of Web 2.0 data is an effective mechanism towards this direction. Adopting the suggestions of the Linked Data movement, aggregators are able to efficiently match and merge the data that refer to the same real world object.

Original languageEnglish
Title of host publicationTransactions on Computational Collective Intelligence XXI - Special Issue on Keyword Search and Big Data
EditorsNgoc Thanh Nguyen, Paulo Rupino da Cunha, Ryszard Kowalczyk
PublisherSpringer
Pages159-174
Number of pages16
ISBN (Print)9783662495209
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event8th International Conference on Computational Collective Intelligence, ICCCI 2016 - Halkidiki, Greece
Duration: 28 Sept 201630 Sept 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9630
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Computational Collective Intelligence, ICCCI 2016
Country/TerritoryGreece
CityHalkidiki
Period28/09/1630/09/16

Keywords

  • Data integration
  • Semantic data management
  • Semantic web

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