research-article

Linked Enterprise Data for Fine Grained Named Entity Linking and Web Intelligence

Authors:

Albert Weichselbraun,

Daniel Streiff,

Arno ScharlAuthors Info & Claims

WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)

Article No.: 13, Pages 1 - 11

https://doi.org/10.1145/2611040.2611052

Published: 02 June 2014 Publication History

Abstract

To identify trends and assign metadata elements such as location and sentiment to the correct entities, Web intelligence applications require methods for linking named entities and revealing relations between organizations, persons and products. For this purpose we introduce Recognyze, a named entity linking component that uses background knowledge obtained from linked data repositories. This paper outlines the underlying methods, provides insights into the migration of proprietary knowledge sources to linked enterprise data, and discusses the lessons learned from adapting linked data for named entity linking. A large dataset obtained from Orell Füssli, the largest Swiss business information provider, serves as the main showcase. This dataset includes more than nine million triples on companies, their contact information, management, products and brands. We identify major challenges towards applying this data for named entity linking and conduct a comprehensive evaluation based on several news corpora to illustrate how Recognyze helps address them, and how it improves the performance of named entity linking components drawing upon linked data rather than machine learning techniques.

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Cited By

MacLean CCavallucci D(2024)Information Extraction to Identify Novel Technologies and Trends in Renewable EnergyWorld Conference of AI-Powered Innovation and Inventive Design10.1007/978-3-031-75923-9_22(330-345)Online publication date: 29-Oct-2024
https://doi.org/10.1007/978-3-031-75923-9_22
Ehrmann MJacquet GSteinberger R(2017)JRC-NamesSemantic Web10.3233/SW-1602288:2(283-295)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/SW-160228
Wilder NSmith JMockus A(2016)Exploring a framework for identity and attribute linking across heterogeneous data systemsProceedings of the 2nd International Workshop on BIG Data Software Engineering10.1145/2896825.2896833(19-25)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2896825.2896833
Show More Cited By

Index Terms

Linked Enterprise Data for Fine Grained Named Entity Linking and Web Intelligence
1. Information systems
  1. Information systems applications
    1. Data mining

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WIMS '14: Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14)

June 2014

506 pages

ISBN:9781450325387

DOI:10.1145/2611040

Copyright © 2014 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].

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Aristotle University of Thessaloniki

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2014

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WIMS '14

WIMS '14: 4th International Conference on Web Intelligence, Mining and Semantics

June 2 - 4, 2014

Thessaloniki, Greece

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WIMS '14 Paper Acceptance Rate 41 of 90 submissions, 46%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

MacLean CCavallucci D(2024)Information Extraction to Identify Novel Technologies and Trends in Renewable EnergyWorld Conference of AI-Powered Innovation and Inventive Design10.1007/978-3-031-75923-9_22(330-345)Online publication date: 29-Oct-2024
https://doi.org/10.1007/978-3-031-75923-9_22
Ehrmann MJacquet GSteinberger R(2017)JRC-NamesSemantic Web10.3233/SW-1602288:2(283-295)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/SW-160228
Wilder NSmith JMockus A(2016)Exploring a framework for identity and attribute linking across heterogeneous data systemsProceedings of the 2nd International Workshop on BIG Data Software Engineering10.1145/2896825.2896833(19-25)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2896825.2896833
Scharl AWeichselbraun AGobel MRafelsberger WKamolov R(2016)Scalable Knowledge Extraction and Visualization for Web IntelligenceProceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS)10.1109/HICSS.2016.467(3749-3757)Online publication date: 5-Jan-2016
https://dl.acm.org/doi/10.1109/HICSS.2016.467
Scharl AHubmann-Haidvogel AJones AFischl DKamolov RWeichselbraun ARafelsberger W(2016)Analyzing the public discourse on works of fiction - Detection and visualization of emotion in online coverage about HBO's Game of ThronesInformation Processing and Management: an International Journal10.1016/j.ipm.2015.02.00352:1(129-138)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.ipm.2015.02.003
Scharl AKamolov RFischl DRafelsberger WJones A(2014)Visualizing Contextual Information in Aggregated Web Content RepositoriesProceedings of the 2014 9th Latin American Web Congress10.1109/LAWeb.2014.18(114-118)Online publication date: 22-Oct-2014
https://dl.acm.org/doi/10.1109/LAWeb.2014.18

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