Assessing the Quality of a Knowledge Graph via Link Prediction Tasks-Reference-Cited by-同舟云学术

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Assessing the Quality of a Knowledge Graph via Link Prediction Tasks

Published:2023-12-15 Issue: Volume: Page:124-129
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval
language:
Short-container-title:

Author:

Zhu Ruiqi¹^ORCID,Bundy Alan¹^ORCID,Pan Jeff¹^ORCID,Nuamah Kwabena¹^ORCID,Wang Fangrong¹^ORCID,Li Xue¹^ORCID,Xu Lei²^ORCID,Mauceri Stefano²^ORCID

Affiliation:

1. School of Informatics, University of Edinburgh, United Kingdom

2. , Huawei Ireland Research Centre, Ireland

Funder

UK Research and Innovation

Huawei Ireland Research Centre

EPSRC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3639233.3639357

Reference37 articles.

1. Developing a Predictive Model of Predicting Appointment No-Show by Using Machine Learning Algorithms

2. Negative statements considered useful

3. What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization

4. Data-driven assessment of structural evolution of RDF graphs

5. Freebase

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