Explaining and predicting abnormal expenses at large scale using knowledge graph based reasoning
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Lecue and Wu successfully predicted abnormal expenses through combined knowledge of Semantic Web and machine learning technologies.
“Lecue and Wu [65] successfully predicted abnormal expenses through combined knowledge of Semantic Web and ML technologies.”
From Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2024) · cites this paper as [65]
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Sorted by citations
Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10179-8 · Cited by 2,062 · see citing works
“Lecue and Wu [65] successfully predicted abnormal expenses through combined knowledge of Semantic Web and ML technologies.”
BackgroundA Survey of Data-driven and Knowledge-aware eXplainable AI
Li, X.-H., Cao, C. C., Shi, Y., Bai, W., Gao, H., Qiu, L., Wang, C., Gao, Y., Zhang, S., Xue, X., et al. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2983930 · Cited by 224 · see citing works
- Authors
- Lecue, F., & Wu, J.
- Affiliations
- Accenture (Ireland) (IE)
- ORCID
- Wu, Jiewen
- Venue
- Journal of Web Semantics
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 44
- Pages
- 89-103
- ISSN
- 1570-8268 (ISSN-L)
- OpenAlex
- W2619976014
- Cited by
- 9
- References
- 0
- Citations by year
- 2024: 1 · 2023: 1 · 2021: 1 · 2020: 2 · 2019: 4
- FWCI
- 0.98
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Web Semantics (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- business travel expenses, knowledge graph reasoning, semantic web, anomaly explanation, large-scale data
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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