Information extraction and knowledge integration (Invited tutorial)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The future of computation will crucially depend on our ability to draw on the unprecedented availability of Big Data to produce more intelligent systems that make more informed decisions. There are two major strategies for turning Big Data into knowledge. The first is to extract information from very large amounts of text, relying on pattern-based extraction and mining algorithms. The second strategy is to merge and refine information, often from pre-existing structured sources. This comes with its own set of challenges, entailing a need for special knowledge integration algorithms. Applying these, we can obtain large multilingual databases such as Lexvo.org and UWN/MENTA.

Original languageEnglish (US)
Title of host publicationLinguistic Linked Open Data - 12th EUROLAN 2015 Summer School and RUMOUR 2015 Workshop, Revised Selected Papers
EditorsDiana Trandabăţ, Daniela Gîfu
PublisherSpringer Verlag
PagesXIII-XIV
ISBN (Print)9783319329413
StatePublished - 2016
Externally publishedYes
Event12th Summer School on Linguistic Linked Open Data, EUROLAN 2015 and Workshop on Social Media and the Web of Linked Data, RUMOUR-2015 - Sibiu, Romania
Duration: Jul 13 2015Jul 25 2015

Publication series

NameCommunications in Computer and Information Science
Volume588
ISSN (Print)1865-0929

Conference

Conference12th Summer School on Linguistic Linked Open Data, EUROLAN 2015 and Workshop on Social Media and the Web of Linked Data, RUMOUR-2015
Country/TerritoryRomania
CitySibiu
Period7/13/157/25/15

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Keywords

  • Data integration
  • Information extraction
  • Knowledge bases

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