The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Modelling the Outlier Detection Problem in ASP(Q)
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“ASP has been increasingly recognized as a valuable tool for anomaly detection [5] and cybersecurity applications [10].”
From Logic-Driven Cybersecurity: A Novel Framework for System Log Anomaly Detection using Answer Set Programming (2025) · cites this paper as [5]
Citations
Cites
Classical negation in logic programs and disjunctive databases
Gelfond, M., & Lifschitz, V. (1991) · New Generation Computing · doi:10.1007/bf03037169 · Cited by 2,259
Data Mining and Knowledge Discovery Handbook
(2010) · doi:10.1007/978-0-387-09823-4 · Cited by 2,190
Outlier Detection for Temporal Data: A Survey
Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.184 · Cited by 1,001
Answer set programming at a glance
Brewka, G., Eiter, T., & Truszczyński, M. (2011) · Communications of the ACM · doi:10.1145/2043174.2043195 · Cited by 931
On the computational cost of disjunctive logic programming: Propositional case
Eiter, T., & Gottlob, G. (1995) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/bf01536399 · Cited by 376
Ord, K. (1996) · International Journal of Forecasting · doi:10.1016/0169-2070(95)00625-7 · Cited by 58
Outlier detection by logic programming
Angiulli, F., Greco, G., & Palopoli, L. (2007) · ACM Transactions on Computational Logic · doi:10.1145/1297658.1297665 · Cited by 21
Beyond NP: Quantifying over Answer Sets
AMENDOLA, G., RICCA, F., & Truszczyński, M. (2019) · Theory and Practice of Logic Programming · doi:10.1017/s1471068419000140 · Cited by 19
Self-citationODCA: An Outlier Detection Approach to Deal with Correlated Attributes
Angiulli, F., Fassetti, F., & Serrao, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86534-4_17 · Cited by 1
Cited by
Li, F., Zuo, F., & Gupta, G. (2025) · arXiv · doi:10.48550/arxiv.2512.04908 · Cited by 1
“ASP has been increasingly recognized as a valuable tool for anomaly detection [5] and cybersecurity applications [10].”
Metadata
- Authors
- Bellusci, P., Mazzotta, G., & Ricca, F.
- Affiliations
- University of Calabria (IT)
- Corresponding author
- Mazzotta, Giuseppe
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 15-23
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4205210833
- Cited by
- 5
- References
- 9
- Citations by year
- 2026: 5
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Lecture notes in computer science (publishedVersion)
- Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- outlier detection, knowledge discovery, declarative modeling, scalability
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
