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Article

Yago: a core of semantic knowledge

Authors:

Fabian M. Suchanek,

Gjergji Kasneci,

Gerhard WeikumAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 697 - 706

https://doi.org/10.1145/1242572.1242667

Published: 08 May 2007 Publication History

Abstract

We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

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Index Terms

Yago: a core of semantic knowledge
1. Information systems

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Information & Contributors

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Published In

cover image ACM Conferences

WWW '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

Copyright © 2007 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 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]

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Published: 08 May 2007

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WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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https://doi.org/10.1109/TKDE.2024.3486915
Song JBai LAn XZhou L(2025)Unsupervised fuzzy temporal knowledge graph entity alignment via joint fuzzy semantics learning and global structure learningNeurocomputing10.1016/j.neucom.2024.129019617(129019)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.129019
Fang YLiu XLu WPedrycz WLang QYang J(2025)Knowledge graph completion with low-dimensional gated hierarchical hyperbolic embeddingKnowledge-Based Systems10.1016/j.knosys.2024.112804309(112804)Online publication date: Jan-2025
https://doi.org/10.1016/j.knosys.2024.112804
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https://doi.org/10.1016/j.ipm.2024.103920
Zhang GXiong YHu JXia C(2025)Triplet trustworthiness validation with knowledge graph reasoningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109813141(109813)Online publication date: Feb-2025
https://doi.org/10.1016/j.engappai.2024.109813
Liu YWang PYang DQiu N(2024)A knowledge graph embedding model based attention mechanism for enhanced node information integrationPeerJ Computer Science10.7717/peerj-cs.180810(e1808)Online publication date: 22-Jan-2024
https://doi.org/10.7717/peerj-cs.1808
Jung J(2024)CR‐M‐SpanBERT: Multiple embedding‐based DNN coreference resolution using self‐attention SpanBERTETRI Journal10.4218/etrij.2023-030846:1(35-47)Online publication date: 28-Feb-2024
https://doi.org/10.4218/etrij.2023-0308
Chen H(2024)Large Knowledge Model: Perspectives and ChallengesData Intelligence10.3724/2096-7004.di.2024.00016:3(587-620)Online publication date: 6-Sep-2024
https://doi.org/10.3724/2096-7004.di.2024.0001
Yang FZhang YZhao XPang S(2024)Unleashing the Power of Decoders: Temporal Knowledge Graph Extrapolation with Householder TransformationSymmetry10.3390/sym1609116616:9(1166)Online publication date: 6-Sep-2024
https://doi.org/10.3390/sym16091166
Shi LLiu WWu YDai CJi ZGanchev I(2024)Knowledge Graph Embedding Using a Multi-Channel Interactive Convolutional Neural Network with Triple AttentionMathematics10.3390/math1218282112:18(2821)Online publication date: 11-Sep-2024
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