research-article

Mitigating linked data quality issues in knowledge-intense information extraction methods

Authors:

Albert Weichselbraun,

Philipp KuntschikAuthors Info & Claims

WIMS '17: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics

Article No.: 17, Pages 1 - 12

https://doi.org/10.1145/3102254.3102272

Published: 19 June 2017 Publication History

Abstract

Advances in research areas such as named entity linking and sentiment analysis have triggered the emergence of knowledge-intensive information extraction methods that combine classical information extraction with background knowledge from the Web. Despite data quality concerns, linked data sources such as DBpedia, GeoNames and Wikidata which encode facts in a standardized structured format are particularly attractive for such applications.

This paper addresses the problem of data quality by introducing a framework that elaborates on linked data quality issues relevant to different stages of the background knowledge acquisition process, their impact on information extraction performance and applicable mitigation strategies. Applying this framework to named entity linking and data enrichment demonstrates the potential of the introduced mitigation strategies to lessen the impact of different kinds of data quality problems. An industrial use case that aims at the automatic generation of image metadata from image descriptions illustrates the successful deployment of knowledge-intensive information extraction in real-world applications and constraints introduced by data quality concerns.

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Cited By

Weichselbraun AHörler SHauser CHavelka AChbeir RManolopoulos YAkerkar RMizera-Pietraszko J(2020)Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web IntelligenceProceedings of the 10th International Conference on Web Intelligence, Mining and Semantics10.1145/3405962.3405988(54-62)Online publication date: 30-Jun-2020
https://dl.acm.org/doi/10.1145/3405962.3405988
Weichselbraun AKuntschik PBraşoveanu A(2018)Mining and Leveraging Background Knowledge for Improving Named Entity LinkingProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227670(1-11)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3227609.3227670

Index Terms

Mitigating linked data quality issues in knowledge-intense information extraction methods
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Incomplete data
        Inconsistent data
    2. Information integration

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cover image ACM Other conferences

WIMS '17: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics

June 2017

268 pages

ISBN:9781450352253

DOI:10.1145/3102254

Conference Chair:
Rajendra Akerkar
Western Norway Research Institute, Norway
,
General Chair:
Alfredo Cuzzocrea
University of Trieste and ICAR-CNR, Italy
,
Program Chairs:
Jannong Cao
Hong Kong Polytechnic University, Hong Kong
,
Mohand-Said Hacid
University of Claude Bernard Lyon 1, France

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

COSPECS: Dipartimento di Scienze Cognitive Psicologiche Pedagogiche e degli Studi Culturali, Universita degli Studi di Messina
Arescon: Advanced Research & Solution Consulting
DISA-MIS: Dipartimento di Scienze Aziendali - Management e Innovation Systems, Universita Degli Studi di Salerno
Xenia Progetti: Xenia Progetti

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2017

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COSPECS
Arescon
DISA-MIS
Xenia Progetti

WIMS '17: 7th International Conference on Web Intelligence, Mining and Semantics

June 19 - 22, 2017

Amantea, Italy

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Cited By

Weichselbraun AHörler SHauser CHavelka AChbeir RManolopoulos YAkerkar RMizera-Pietraszko J(2020)Classifying News Media Coverage for Corruption Risks Management with Deep Learning and Web IntelligenceProceedings of the 10th International Conference on Web Intelligence, Mining and Semantics10.1145/3405962.3405988(54-62)Online publication date: 30-Jun-2020
https://dl.acm.org/doi/10.1145/3405962.3405988
Weichselbraun AKuntschik PBraşoveanu A(2018)Mining and Leveraging Background Knowledge for Improving Named Entity LinkingProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227670(1-11)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3227609.3227670

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