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Predicting RDF triples in incomplete knowledge bases with tensor factorization

Authors:

Steffen Rendle,

Lars Schmidt-ThiemeAuthors Info & Claims

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

Pages 326 - 331

https://doi.org/10.1145/2245276.2245341

Published: 26 March 2012 Publication History

Abstract

On RDF datasets, the truth values of triples are known when they are either explicitly stated or can be inferred using logical entailment. Due to the open world semantics of RDF, nothing can be said about the truth values of triples that are neither in the dataset nor can be logically inferred. By estimating the truth values of such triples, one could discover new information from the database thus enabling to broaden the scope of queries to an RDF base that can be answered, support knowledge engineers in maintaining such knowledge bases or recommend users resources worth looking into for instance. In this paper, we present a new approach to predict the truth values of any RDF triple. Our approach uses a 3-dimensional tensor representation of the RDF knowledge base and applies tensor factorization techniques that take open world semantics into account to predict new true triples given already observed ones. We report results of experiments on real world datasets comparing different tensor factorization models. Our empirical results indicate that our approach is highly successful in estimating triple truth values on incomplete RDF datasets.

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Cited By

Wang ZHan FZhao S(2024)A Survey on Knowledge Graph Related Research in Smart City DomainACM Transactions on Knowledge Discovery from Data10.1145/367261518:9(1-31)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3672615
Chen QZhang ZWang WZhang Z(2023)Learning-efficient Transmission Scheduling for Distributed Knowledge-aware Edge Learning2023 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC55385.2023.10119099(1-6)Online publication date: Mar-2023
https://doi.org/10.1109/WCNC55385.2023.10119099
Huseynli AAkcayol M(2023)Continuous Knowledge Graph Refinement With Confidence PropagationIEEE Access10.1109/ACCESS.2023.328392511(59226-59237)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3283925
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Published In

cover image ACM Conferences

SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied Computing

March 2012

2179 pages

ISBN:9781450308571

DOI:10.1145/2245276

Conference Chairs:
Sascha Ossowski
University Rey Juan Carlos, Spain
,
Paola Lecca
The Microsoft Research - University of Trento COSBI, Italy

Copyright © 2012 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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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2012

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SAC 2012

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SIGAPP

SAC 2012: ACM Symposium on Applied Computing

March 26 - 30, 2012

Trento, Italy

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SAC '12 Paper Acceptance Rate 270 of 1,056 submissions, 26%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Wang ZHan FZhao S(2024)A Survey on Knowledge Graph Related Research in Smart City DomainACM Transactions on Knowledge Discovery from Data10.1145/367261518:9(1-31)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3672615
Chen QZhang ZWang WZhang Z(2023)Learning-efficient Transmission Scheduling for Distributed Knowledge-aware Edge Learning2023 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC55385.2023.10119099(1-6)Online publication date: Mar-2023
https://doi.org/10.1109/WCNC55385.2023.10119099
Huseynli AAkcayol M(2023)Continuous Knowledge Graph Refinement With Confidence PropagationIEEE Access10.1109/ACCESS.2023.328392511(59226-59237)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3283925
Suo XGuo BShen YChen YWang W(2023)KRL_MLCCL: Multi-label classification based on contrastive learning for knowledge representation learning under open worldInformation Processing & Management10.1016/j.ipm.2023.10341160:5(103411)Online publication date: Sep-2023
https://doi.org/10.1016/j.ipm.2023.103411
Su XYou ZHuang DWang LWong LJi BZhao B(2022)Biomedical Knowledge Graph Embedding with Capsule Network for Multi-label Drug-Drug Interaction PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3154792(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3154792
Zhou XNiu LZhu QZhu XLiu PTan JGuo L(2022)Knowledge Graph Embedding by Double Limit Scoring LossIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.306075534:12(5825-5839)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3060755
Liu LZeng JZheng X(2022)Learning structured embeddings of knowledge graphs with generative adversarial frameworkExpert Systems with Applications10.1016/j.eswa.2022.117361204(117361)Online publication date: Oct-2022
https://doi.org/10.1016/j.eswa.2022.117361
Suo XGuo BShen YDai SWang WChen YZhang Z(2022)KRL_Match: knowledge graph objects matching for knowledge representation learningKnowledge and Information Systems10.1007/s10115-022-01764-865:2(641-681)Online publication date: 26-Oct-2022
https://doi.org/10.1007/s10115-022-01764-8
Daza DCochez MGroth P(2021)Inductive Entity Representations from Text via Link PredictionProceedings of the Web Conference 202110.1145/3442381.3450141(798-808)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450141
Zhang JPei ZLuo Z(2021)Reasoning for Local Graph Over Knowledge Graph With a Multi-Policy AgentIEEE Access10.1109/ACCESS.2021.30837949(78452-78462)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3083794
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