research-article

Tracy: Tracing Facts over Knowledge Graphs and Text

Authors:

Mohamed H. Gad-Elrab,

Daria Stepanova,

Gerhard WeikumAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 3516 - 3520

https://doi.org/10.1145/3308558.3314126

Published: 13 May 2019 Publication History

Abstract

In order to accurately populate and curate Knowledge Graphs (KGs), it is important to distinguish ?s?p?o? facts that can be traced back to sources from facts that cannot be verified. Manually validating each fact is time-consuming. Prior work on automating this task relied on numerical confidence scores which might not be easily interpreted. To overcome this limitation, we present Tracy, a novel tool that generates human-comprehensible explanations for candidate facts. Our tool relies on background knowledge in the form of rules to rewrite the fact in question into other easier-to-spot facts. These rewritings are then used to reason over the candidate fact creating semantic traces that can aid KG curators. The goal of our demonstration is to illustrate the main features of our system and to show how the semantic traces can be computed over both text and knowledge graphs with a simple and intuitive user interface.

References

[1]

Xin Luna Dong and Divesh Srivastava. 2013. Compact Explanation of Data Fusion Decisions. In Proceedings of the 22Nd International Conference on World Wide Web(WWW '13). ACM, 379-390.

Digital Library

[2]

Mohamed H Gad-Elrab, Daria Stepanova, Jacopo Urbani, and Gerhard Weikum. 2016. Exception-enriched rule learning from knowledge graphs. In Proceedings of ISWC. 234-251.

[3]

Mohamed H. Gad-Elrab, Daria Stepanova, Jacopo Urbani, and Gerhard Weikum. 2019. ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text. In Proceedings of WSDM. 87-95.

Digital Library

[4]

Luis Antonio Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek. 2013. AMIE: Association Rule Mining Under Incomplete Evidence in Ontological Knowledge Bases. In Proceedings of WWW. 413-422.

Digital Library

[5]

Daniel Gerber, Diego Esteves, Jens Lehmann, Lorenz Bühmann, Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo, and Rene´ Speck. 2015. DeFacto-Temporal and Multilingual Deep Fact Validation. Web Semant. 35, P2 (Dec. 2015), 85-101.

Digital Library

[6]

Vinh Thinh Ho, Daria Stepanova, Mohamed H. Gad-Elrab, Evgeny Kharlamov, and Gerhard Weikum. 2018. Rule Learning from Knowledge Graphs Guided by Embedding Models. In The Semantic Web - ISWC 2018, Proceedings, Part I. 72-90.

[7]

Robert Kowalski and Donald Kuehner. 1971. Linear resolution with selection function. Artificial Intelligence 2, 3 (1971), 227 - 260.

[8]

Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, Bo Zhao, Wei Fan, and Jiawei Han. 2015. A Survey on Truth Discovery. SIGKDD Explorations 17(2015), 1-16.

Digital Library

[9]

Ndapandula Nakashole and Tom M. Mitchell. 2014. Language-Aware Truth Assessment of Fact Candidates. In Proceedings of ACL. 1009-1019.

[10]

Ndapandula Nakashole, Gerhard Weikum, and Fabian Suchanek. 2012. PATTY: A Taxonomy of Relational Patterns with Semantic Types. In Proceedings of EMNLP. 1135-1145.

Digital Library

[11]

Jeff Pasternack and Dan Roth. 2013. Latent credibility analysis. In Proceedings of WWW. 1009-1020.

Digital Library

[12]

Heiko Paulheim. 2017. Knowledge graph refinement: A survey of approaches and evaluation methods. Semantic Web 8, 3 (2017), 489-508.

Digital Library

[13]

Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017. Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media. In Proceedings of WWW. 1003-1012.

Digital Library

[14]

Daria Stepanova, Mohamed H. Gad-Elrab, and Vinh Thinh Ho. 2018. Rule Induction and Reasoning over Knowledge Graphs. In Reasoning Web (2018) (Lecture Notes in Computer Science), Vol. 11078. Springer, 142-172.

[15]

Fabian M. Suchanek, Gjergji Kasneci, and Gerhard Weikum. 2007. Yago: A Core of Semantic Knowledge. In Proceedings of WWW. 697-706.

Digital Library

[16]

Denny Vrandecic and Markus Krötzsch. 2014. Wikidata: a free collaborative knowledgebase. Communications of ACM 57, 10 (2014), 78-85.

Digital Library

[17]

Zhichun Wang and Juan-Zi Li. 2015. RDF2Rules: Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles. CoRR abs/1512.07734(2015).

[18]

Zhuoyu Wei, Jun Zhao, Kang Liu, Zhenyu Qi, Zhengya Sun, and Guanhua Tian. 2015. Large-scale Knowledge Base Completion: Inferring via Grounding Network Sampling over Selected Instances. In Proceedings of CIKM '15. 1331-1340.

Digital Library

Cited By

Wang XBan TChen LWu XLyu DChen H(2024)Knowledge Verification From DataIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320224435:3(4324-4338)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3202244
Hang CYu PTan C(2024)TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking2024 58th Annual Conference on Information Sciences and Systems (CISS)10.1109/CISS59072.2024.10480162(1-6)Online publication date: 13-Mar-2024
https://doi.org/10.1109/CISS59072.2024.10480162
Xue XZhang CWang YSong HXue XNiu Z(2024)Heterogeneous Views and Spatial Structure Enhancement for triple error detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124938256:COnline publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124938
Show More Cited By

Recommendations

ExFaKT: A Framework for Explaining Facts over Knowledge Graphs and Text
WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

Fact-checking is a crucial task for accurately populating, updating and curating knowledge graphs. Manually validating candidate facts is time-consuming. Prior work on automating this task focuses on estimating truthfulness using numerical scores which ...
Expanding Domain-Specific Knowledge Graphs with Unknown Facts
Natural Language Processing and Information Systems
Abstract
Many knowledge graphs have been created to support intelligent applications, such as search engines and recommendation systems. Some domain-specific knowledge graphs contain similar contents in nature (e.g., the FreeBase contains information about ...
FedE: Embedding Knowledge Graphs in Federated Setting
IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs

Knowledge graphs (KGs) become widespread and many organizations construct as well as maintain their own knowledge graphs. Same as the data isolation which has been a long-standing problem, knowledge graph isolation is common in real knowledge graph ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 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]

In-Cooperation

IW3C2: International World Wide Web Conference Committee

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
393
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)2

Reflects downloads up to 27 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang XBan TChen LWu XLyu DChen H(2024)Knowledge Verification From DataIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.320224435:3(4324-4338)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3202244
Hang CYu PTan C(2024)TrumorGPT: Query Optimization and Semantic Reasoning over Networks for Automated Fact-Checking2024 58th Annual Conference on Information Sciences and Systems (CISS)10.1109/CISS59072.2024.10480162(1-6)Online publication date: 13-Mar-2024
https://doi.org/10.1109/CISS59072.2024.10480162
Xue XZhang CWang YSong HXue XNiu Z(2024)Heterogeneous Views and Spatial Structure Enhancement for triple error detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124938256:COnline publication date: 5-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124938
Qudus UPekarou FSilva ARöder MNgomo A(2024)FaVEL: Fact Validation Ensemble LearningKnowledge Engineering and Knowledge Management10.1007/978-3-031-77792-9_13(209-225)Online publication date: 25-Nov-2024
https://dl.acm.org/doi/10.1007/978-3-031-77792-9_13
Liu ZLu Y(2024)A task‐centric knowledge graph construction method based on multi‐modal representation learning for industrial maintenance automationEngineering Reports10.1002/eng2.12952Online publication date: 7-Jul-2024
https://doi.org/10.1002/eng2.12952
Huang XZhang JGao SChen JChen ZPeng C(2023)A Knowledge Graph Quality Evaluation Method for Multilevel Power Grid Dispatching Cloud2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA)10.1109/ICIPCA59209.2023.10257867(1304-1309)Online publication date: 11-Aug-2023
https://doi.org/10.1109/ICIPCA59209.2023.10257867
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
Xue BZou L(2022)Knowledge Graph Quality Management: a Comprehensive SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150080(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3150080
Ou JNguyen UIsmail T(2022)COVMIS: A Dataset for Research on COVID-19 Misinformation2022 5th International Conference on Data Science and Information Technology (DSIT)10.1109/DSIT55514.2022.9943904(1-11)Online publication date: 22-Jul-2022
https://doi.org/10.1109/DSIT55514.2022.9943904
Timmerman YBronselaer A(2022)Automated monitoring of online news accuracy with change classification modelsInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10310559:6Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.103105
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten