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Mining and Leveraging Background Knowledge for Improving Named Entity Linking

Authors:

Albert Weichselbraun,

Philipp Kuntschik,

Adrian M.P. BraşoveanuAuthors Info & Claims

WIMS '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

Article No.: 27, Pages 1 - 11

https://doi.org/10.1145/3227609.3227670

Published: 25 June 2018 Publication History

Abstract

Knowledge-rich Information Extraction (IE) methods aspire towards combining classical IE with background knowledge obtained from third-party resources. Linked Open Data repositories that encode billions of machine readable facts from sources such as Wikipedia play a pivotal role in this development.

The recent growth of Linked Data adoption for Information Extraction tasks has shed light on many data quality issues in these data sources that seriously challenge their usefulness such as completeness, timeliness and semantic correctness. Information Extraction methods are, therefore, faced with problems such as name variance and type confusability. If multiple linked data sources are used in parallel, additional concerns regarding link stability and entity mappings emerge.

This paper develops methods for integrating Linked Data into Named Entity Linking methods and addresses challenges in regard to mining knowledge from Linked Data, mitigating data quality issues, and adapting algorithms to leverage this knowledge.

Finally, we apply these methods to Recognyze, a graph-based Named Entity Linking (NEL) system, and provide a comprehensive evaluation which compares its performance to other well-known NEL systems, demonstrating the impact of the suggested methods on its own entity linking performance.

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Cited By

Weichselbraun AWaldvogel RFraefel Avan Schie AKuntschik P(2022)Building Knowledge Graphs and Recommender Systems for Suggesting Reskilling and Upskilling Options from the WebInformation10.3390/info1311051013:11(510)Online publication date: 25-Oct-2022
https://doi.org/10.3390/info13110510
Weichselbraun AWaldvogel RFraefel Avan Schie AKuntschik P(2022)Slot Filling for Extracting Reskilling and Upskilling Options from the WebNatural Language Processing and Information Systems10.1007/978-3-031-08473-7_25(279-290)Online publication date: 13-Jun-2022
https://doi.org/10.1007/978-3-031-08473-7_25
Vachkova SKupriyanov RSuleymanov RPetryaeva E(2021)The Application of Text Mining Algorithms to Discover One Topic Objects in Digital Learning Repositories2021 28th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT50888.2021.9347611(502-509)Online publication date: 27-Jan-2021
https://doi.org/10.23919/FRUCT50888.2021.9347611
Show More Cited By

Index Terms

Mining and Leveraging Background Knowledge for Improving Named Entity Linking
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 '18: Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics

June 2018

398 pages

ISBN:9781450354899

DOI:10.1145/3227609

Copyright © 2018 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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Publication History

Published: 25 June 2018

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WIMS '18

WIMS '18: 8th International Conference on Web Intelligence, Mining and Semantics

June 25 - 27, 2018

Novi Sad, Serbia

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Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Weichselbraun AWaldvogel RFraefel Avan Schie AKuntschik P(2022)Building Knowledge Graphs and Recommender Systems for Suggesting Reskilling and Upskilling Options from the WebInformation10.3390/info1311051013:11(510)Online publication date: 25-Oct-2022
https://doi.org/10.3390/info13110510
Weichselbraun AWaldvogel RFraefel Avan Schie AKuntschik P(2022)Slot Filling for Extracting Reskilling and Upskilling Options from the WebNatural Language Processing and Information Systems10.1007/978-3-031-08473-7_25(279-290)Online publication date: 13-Jun-2022
https://doi.org/10.1007/978-3-031-08473-7_25
Vachkova SKupriyanov RSuleymanov RPetryaeva E(2021)The Application of Text Mining Algorithms to Discover One Topic Objects in Digital Learning Repositories2021 28th Conference of Open Innovations Association (FRUCT)10.23919/FRUCT50888.2021.9347611(502-509)Online publication date: 27-Jan-2021
https://doi.org/10.23919/FRUCT50888.2021.9347611
Bastos ANadgeri ASingh KMulang IShekarpour SHoffart JKaul M(2021)RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural NetworkProceedings of the Web Conference 202110.1145/3442381.3449917(1673-1685)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449917
Al-Moslmi TGallofre Ocana ML. Opdahl AVeres C(2020)Named Entity Extraction for Knowledge Graphs: A Literature OverviewIEEE Access10.1109/ACCESS.2020.29739288(32862-32881)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973928
Papadopoulou OZampoglou MPapadopoulos SKompatsiaris I(2019)Verification of Web Videos Through Analysis of Their Online ContextVideo Verification in the Fake News Era10.1007/978-3-030-26752-0_7(191-221)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-26752-0_7
Nixon LFischl DScharl A(2019)Real-Time Story Detection and Video Retrieval from Social Media StreamsVideo Verification in the Fake News Era10.1007/978-3-030-26752-0_2(17-52)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-26752-0_2
Scharl AHubmann-Haidvogel AGöbel MSchäfer TFischl DNixon L(2019)Multimodal Analytics Dashboard for Story Detection and VisualizationVideo Verification in the Fake News Era10.1007/978-3-030-26752-0_10(281-299)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-26752-0_10
Odoni FKuntschik PBraşoveanu AWeichselbraun A(2018)On the Importance of Drill-Down Analysis for Assessing Gold Standards and Named Entity Linking PerformanceProcedia Computer Science10.1016/j.procs.2018.09.004137(33-42)Online publication date: 2018
https://doi.org/10.1016/j.procs.2018.09.004

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