Database principles in Information Extraction

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

Abstract

Information Extraction commonly refers to the task of populating a relational schema, having predefined underlying semantics, from textual content. This task is pervasive in contemporary computational challenges associated with Big Data. This tutorial gives an overview of the algorithmic concepts and techniques used for performing Information Extraction tasks, and describes some of the declarative frameworks that provide abstractions and infrastructure for programming extractors. In addition, the tutorial highlights opportunities for research impact through principles of data management, illustrates these opportunities through recent work, and proposes directions for future research.

Original languageEnglish
Title of host publicationPODS 2014 - Proceedings of the 33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems
Pages156-163
Number of pages8
DOIs
StatePublished - 2014
Externally publishedYes
Event33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2014 - Snowbird, UT, United States
Duration: 22 Jun 201427 Jun 2014

Publication series

NameProceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

Conference

Conference33rd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2014
Country/TerritoryUnited States
CitySnowbird, UT
Period22/06/1427/06/14

Keywords

  • Database inconsistency
  • Database repairs
  • Document spanners
  • Finite-state transducers
  • Information extraction
  • Prioritized repairs
  • Regular expressions

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture

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