ExPAD: An Explainable Distributed Automatic Anomaly Detection Framework over Large KGs-Reference-Cited by-同舟云学术

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ExPAD: An Explainable Distributed Automatic Anomaly Detection Framework over Large KGs

Published:2023-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 17th International Conference on Semantic Computing (ICSC)
language:
Short-container-title:

Author:

Moghaddam Farshad Bakhshandegan¹,Lehmann Jens²,Jabeen Hajira³

Affiliation:

1. University of Bonn,Bonn,Germany

2. TU Dresden, Amazon (Work Done Outside of Amazon),Dresden,Germany

3. GESIS-Leibniz Institute for the Social Sciences,Cologne,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10066535/10066543/10066741.pdf?arnumber=10066741

Reference31 articles.

1. A unified approach to interpreting model predictions;lundberg;Advances in neural information processing systems,2017

2. Anchors: High-Precision Model-Agnostic Explanations

3. Variations of box plots;mcgill;The American Statistician,1978

4. Causality-based Explanation of Classification Outcomes

5. SDM-RDFizer

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Anomaly Detection for Numerical Literals in Knowledge Graphs: A Short Review of Approaches;2023 IEEE Sixth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE);2023-09-25

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