poster

Extracting structured information from Wikipedia articles to populate infoboxes

Authors:

Dustin Lange,

Christoph Böhm,

Felix NaumannAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1661 - 1664

https://doi.org/10.1145/1871437.1871698

Published: 26 October 2010 Publication History

Get Access

Abstract

Roughly every third Wikipedia article contains an infobox - a table that displays important facts about the subject in attribute-value form. The schema of an infobox, i.e., the attributes that can be expressed for a concept, is defined by an infobox template. Often, authors do not specify all template attributes, resulting in incomplete infoboxes.

With iPopulator, we introduce a system that automatically populates infoboxes of Wikipedia articles by extracting attribute values from the article's text. In contrast to prior work, iPopulator detects and exploits the structure of attribute values to independently extract value parts. We have tested iPopulator on the entire set of infobox templates and provide a detailed analysis of its effectiveness. For instance, we achieve an average extraction precision of 91% for 1,727 distinct infobox template attributes.

References

[1]

J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. of the 18th Intl. Conf. on Machine Learning, pages 282--289, 2001.

Digital Library

Google Scholar

[2]

D. Lange, C. Böhm, and F. Naumann. Extracting Structured Information from Wikipedia Articles to Populate Infoboxes. Technical Report 38, Hasso Plattner Institute, Potsdam, 2010. ISBN 978-3-86956-081-6.

Digital Library

Google Scholar

[3]

N. Okazaki. CRFsuite: a fast implementation of Conditional Random Fields (CRFs), 2007. http://www.chokkan.org/software/crfsuite/.

Google Scholar

[4]

S. Sarawagi. Information Extraction. Foundations and Trends in Databases, 1(3), 2008.

Digital Library

Google Scholar

[5]

F. Wu, R. Hoffmann, and D. S. Weld. Information Extraction from Wikipedia: Moving Down the Long tail. In Proc. of the 14th Intl. Conf. on Knowledge Discovery and Data Mining, pages 731--739, 2008.

Digital Library

Google Scholar

[6]

F. Wu and D. S. Weld. Autonomously Semantifying Wikipedia. In Proc. of the 16th Conf. on Information and Knowledge Management, pages 41--50, 2007.

Digital Library

Google Scholar

Cited By

View all

Nikiforos MDeliveri KKermanidis KPateli A(2023)Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class BalancingComputers10.3390/computers1206011112:6(111)Online publication date: 24-May-2023
https://doi.org/10.3390/computers12060111
Sakota MPeyrard MWest R(2023)Descartes: Generating Short Descriptions of Wikipedia ArticlesProceedings of the ACM Web Conference 202310.1145/3543507.3583220(1446-1456)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583220
Demir S(2022)Turkish Data-to-Text Generation Using Sequence-to-Sequence Neural NetworksACM Transactions on Asian and Low-Resource Language Information Processing10.1145/354382622:2(1-27)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3543826
Show More Cited By

Index Terms

Extracting structured information from Wikipedia articles to populate infoboxes
1. Information systems
  1. Information systems applications

Recommendations

Two-stage approach to named entity recognition using Wikipedia and DBpedia
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication

In natural language understanding, extraction of named entity (NE) mentions in given text and classification of the mentions into pre-defined NE types are important processes. Most NE recognition (NER) relies on resources such as a training corpus or NE ...
Learning multilingual named entity recognition from Wikipedia

We automatically create enormous, free and multilingual silver-standard training annotations for named entity recognition (ner) by exploiting the text and structure of Wikipedia. Most ner systems rely on statistical models of annotated data to identify ...
Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia
GIR '14: Proceedings of the 8th Workshop on Geographic Information Retrieval

Place name disambiguation is an important task for improving the accuracy of geographic information retrieval. This task becomes more challenging when the input texts are short. Wikipedia provides information about places and has often been employed for ...

Comments

Information & Contributors

Information

Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

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 ACM 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM '10

Sponsor:

CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
474
Total Downloads

Downloads (Last 12 months)12
Downloads (Last 6 weeks)2

Reflects downloads up to 14 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Nikiforos MDeliveri KKermanidis KPateli A(2023)Vocational Domain Identification with Machine Learning and Natural Language Processing on Wikipedia Text: Error Analysis and Class BalancingComputers10.3390/computers1206011112:6(111)Online publication date: 24-May-2023
https://doi.org/10.3390/computers12060111
Sakota MPeyrard MWest R(2023)Descartes: Generating Short Descriptions of Wikipedia ArticlesProceedings of the ACM Web Conference 202310.1145/3543507.3583220(1446-1456)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583220
Demir S(2022)Turkish Data-to-Text Generation Using Sequence-to-Sequence Neural NetworksACM Transactions on Asian and Low-Resource Language Information Processing10.1145/354382622:2(1-27)Online publication date: 8-Jul-2022
https://dl.acm.org/doi/10.1145/3543826
Nikiforos MDeliveri KKermanidis KPateli A(2022)Machine Learning on Wikipedia Text for the Automatic Identification of Vocational Domains of Significance for Displaced Communities2022 17th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)10.1109/SMAP56125.2022.9941803(1-6)Online publication date: 3-Nov-2022
https://doi.org/10.1109/SMAP56125.2022.9941803
Jeansoulin R(2021)A Century of French Railways: The Value of Remote Sensing and VGI in the Fusion of Historical DataISPRS International Journal of Geo-Information10.3390/ijgi1003015410:3(154)Online publication date: 10-Mar-2021
https://doi.org/10.3390/ijgi10030154
Samuel J(2021)WDProp: Web Application to Analyse Multilingual Aspects of Wikidata PropertiesProceedings of the 17th International Symposium on Open Collaboration10.1145/3479986.3479996(1-12)Online publication date: 15-Sep-2021
https://dl.acm.org/doi/10.1145/3479986.3479996
Samuel J(2021)ShExStatements: Simplifying Shape Expressions for WikidataCompanion Proceedings of the Web Conference 202110.1145/3442442.3452349(610-615)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3452349
Moreira JBarbosa L(2021)DeepEx: A Robust Weak Supervision System for Knowledge Base AugmentationJournal on Data Semantics10.1007/s13740-021-00134-xOnline publication date: 6-Jul-2021
https://doi.org/10.1007/s13740-021-00134-x
Privatto PGuilherme I(2021)When External Knowledge Does Not Aggregate in Named Entity RecognitionIntelligent Systems10.1007/978-3-030-91699-2_42(616-627)Online publication date: 28-Nov-2021
https://doi.org/10.1007/978-3-030-91699-2_42
Hong XJin PMu LZhao JWan S(2020)Competitor Mining from Web Encyclopedia: A Graph Embedding ApproachWeb Information Systems Engineering – WISE 202010.1007/978-3-030-62005-9_5(56-68)Online publication date: 18-Oct-2020
https://doi.org/10.1007/978-3-030-62005-9_5
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Two-stage approach to named entity recognition using Wikipedia and DBpedia

Learning multilingual named entity recognition from Wikipedia

Improving wikipedia-based place name disambiguation in short texts using structured data from DBpedia