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research-article

Building event-centric knowledge graphs from news

Authors:

Marco Rospocher,

Marieke van Erp,

Antske Fokkens,

Thomas Ploeger,

Tessel BogaardAuthors Info & Claims

Web Semantics: Science, Services and Agents on the World Wide Web, Volume 37, Issue C

Pages 132 - 151

https://doi.org/10.1016/j.websem.2015.12.004

Published: 01 March 2016 Publication History

Abstract

Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations-e.g. a celebrity's birth date, occupation and family members-obtained from large repositories such as Freebase or Wikipedia.In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs.We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user's understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts.

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Cited By

Huang JZhang XHu XWu HZhang W(2024)Construction and Teaching Application of Knowledge Graph in the Field of International TradeProceedings of the International Conference on Decision Science & Management10.1145/3686081.3686127(270-275)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3686081.3686127
Peng JYang WWei FHe L(2024)Prompt for extractionKnowledge-Based Systems10.1016/j.knosys.2024.111544289:COnline publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111544
Liao FHuang JLiu QPeng XLiu BWu XQian J(2024)TCEKG: A Temporal and Causal Event Knowledge Graph for Power Distribution Network Fault DiagnosisAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5615-5_41(505-517)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5615-5_41
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Building event-centric knowledge graphs from news
1. Computing methodologies
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cover image Web Semantics: Science, Services and Agents on the World Wide Web

Web Semantics: Science, Services and Agents on the World Wide Web Volume 37, Issue C

March 2016

207 pages

ISSN:1570-8268

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

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Published: 01 March 2016

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Huang JZhang XHu XWu HZhang W(2024)Construction and Teaching Application of Knowledge Graph in the Field of International TradeProceedings of the International Conference on Decision Science & Management10.1145/3686081.3686127(270-275)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3686081.3686127
Peng JYang WWei FHe L(2024)Prompt for extractionKnowledge-Based Systems10.1016/j.knosys.2024.111544289:COnline publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111544
Liao FHuang JLiu QPeng XLiu BWu XQian J(2024)TCEKG: A Temporal and Causal Event Knowledge Graph for Power Distribution Network Fault DiagnosisAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5615-5_41(505-517)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5615-5_41
Yang JHuang SHuang M(2024)Event Evolution Analysis of Network Text Based on Pre-trained Language Model and Event GraphCooperative Design, Visualization, and Engineering10.1007/978-3-031-71315-6_6(52-62)Online publication date: 15-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-71315-6_6
Wang KWang TLu Z(2023)Construction of Gesar Epic Event Graph Based on Event ExtractionProceedings of the 2023 4th International Conference on Computer Science and Management Technology10.1145/3644523.3644601(431-436)Online publication date: 13-Oct-2023
https://dl.acm.org/doi/10.1145/3644523.3644601
Blin IStork LSpillner LSantagiustina C(2023)OKG: A Knowledge Graph for Fine-grained Understanding of Social Media Discourse on InequalityProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627557(166-174)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627557
Bonisoli GDi Buono MPo LRollo FChen HDuh WHuang HKato MMothe JPoblete B(2023)DICE: a Dataset of Italian Crime Event newsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591904(2985-2995)Online publication date: 19-Jul-2023
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Bhardwaj AYang JCudré-Mauroux P(2023)Human-in-the-Loop Rule Discovery for Micropost Event DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.320834535:8(8100-8111)Online publication date: 1-Aug-2023
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Gallofré Ocaña MOpdahl A(2023)A Software Reference Architecture for Journalistic Knowledge PlatformsKnowledge-Based Systems10.1016/j.knosys.2023.110750276:COnline publication date: 27-Sep-2023
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Plötzky FBritz KBalke W(2023)Shards of Knowledge – Modeling Attributions for Event-Centric Knowledge GraphsConceptual Modeling10.1007/978-3-031-47262-6_14(259-276)Online publication date: 6-Nov-2023
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