Confirmation-Guided Discovery of First-Order Rules with Tertius
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Part of this paper’s reference list could not be split into single entries.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771 · see citing works
Measures of Association for Cross Classifications
Goodman, L. A., & Kruskal, W. (1979) · Springer series in statistics · doi:10.1007/978-1-4612-9995-0 · Cited by 2,255 · see citing works
Knowledge Discovery in Databases
Piateski, G., & Frawley, W. (1991) · Medical Entomology and Zoology · Cited by 1,746 · see citing works
Muggleton, S. (1995) · New Generation Computing · doi:10.1007/bf03037227 · Cited by 1,411 · see citing works
Discovery, Analysis, and Presentation of Strong Rules
Piatetsky-Shapiro, G. (1991) · Medical Entomology and Zoology · Cited by 988 · see citing works
Pattern-Directed Inference Systems
(1978) · Elsevier eBooks · doi:10.1016/c2013-0-11671-6 · Cited by 777 · see citing works
An algorithm for multi-relational discovery of subgroups
Wrobel, S. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63223-9_108 · Cited by 561 · see citing works
Explora: a multipattern and multistrategy discovery assistant
Klösgen, W. (1996) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 439 · see citing works
Rule Evaluation Measures: A Unifying View
Lavrač, N., Flach, P. A., & Zupan, B. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4_17 · Cited by 387 · see citing works
Self-citationSearch through systematic set enumeration
Rymon, R. (1992) · Cited by 346 · see citing works
Multiway Contingency Tables Analysis for the Social Sciences
Wickens, T. D. (2014) · Psychology Press eBooks · doi:10.4324/9781315807850 · Cited by 324 · see citing works
Discovery of frequent DATALOG patterns
Dehaspe, L., & Toivonen, H. T. T. (1999) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009863704807 · Cited by 315 · see citing works
De Raedt, L., & Dehaspe, L. (1997) · Machine Learning · doi:10.1023/a:1007361123060 · Cited by 235 · see citing works
Mining association rules in multiple relations
Dehaspe, L., & Raedt, L. D. (1997) · Lecture notes in computer science · doi:10.1007/3540635149_40 · Cited by 189 · see citing works
Database dependency discovery: a machine learning approach
Flach, P. A., & Savnik, I. (1999) · doi:10.5555/1216155.1216159 · Cited by 171 · see citing works
Self-citationLogical settings for concept-learning
Raedt, L. D. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00041-6 · Cited by 149 · see citing works
Multiway Contingency Tables Analysis for the Social Sciences.
Caliński, T., Krajewski, P., & Wickens, T. D. (1991) · Biometrics · doi:10.2307/2532679 · Cited by 136 · see citing works
1BC: A First-Order Bayesian Classifier
Flach, P. A., & Lachiche, N. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4_10 · Cited by 85 · see citing works
Self-citationSearching for Structure in Multiple Streams of Data.
Oates, T., & Cohen, P. R. (1996) · Cited by 74 · see citing works
INDUCTIVE LEARNING OF RELATIONAL PRODUCTIONS
Vere, S. A. (1978) · Elsevier eBooks · doi:10.1016/b978-0-12-737550-2.50018-x · Cited by 49 · see citing works
Strongly typed inductive concept learning
Flach, P. A., Giraud-Carrier, C. G., & Lloyd, J. W. (1998) · Lecture notes in computer science · doi:10.1007/bfb0027322 · Cited by 48 · see citing works
Self-citationRefinement Operators Can Be (Weakly) Perfect
Badea, L., & Stanciu, M. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4_4 · Cited by 39 · see citing works
Predicate invention in inductive data engineering
Flach, P. A. (1993) · Lecture notes in computer science · doi:10.1007/3-540-56602-3_129 · Cited by 38 · see citing works
Self-citationMultiple Predicate Learning in Two Inductive Logic Programming Settings
Raedt, L. D., & Lavrač, N. (1996) · Logic Journal of IGPL · doi:10.1093/jigpal/4.2.227 · Cited by 25 · see citing works
Measures of Association for Cross Classifications.
Simon, G., Goodman, L. A., & Kruskal, W. (1980) · Journal of the American Statistical Association · doi:10.2307/2287207 · Cited by 24 · see citing works
Inductive learning of relational productions
Vere, S. A. (1977) · ACM SIGART Bulletin · doi:10.1145/1045343.1045370 · Cited by 21 · see citing works
Integrating explanatory and descriptive learning in ILP
Dimopoulos, Y., Džeroski, S., & Kakas, A. C. (1997) · Cited by 18 · see citing works
Background Knowledge in the Tertius First Order Knowledge Discovery Tool
Dahl, T. (1999) · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583 · see citing works
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766 · see citing works
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570 · see citing works
Association rule mining to detect factors which contribute to heart disease in males and females
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.028 · Cited by 394 · see citing works
Context-aware rule learning from smartphone data: survey, challenges and future directions
Sarker, I. H. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0258-4 · Cited by 105 · see citing works
Sarker, I. H., & Kayes, A. S. M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102762 · Cited by 75 · see citing works
- Authors
- Flach, P. A., & Lachiche, N.
- Affiliations
- University of Bristol (GB)
- Venue
- Machine Learning
- Published
- 2001-01
- Volume
- 42
- Issue
- 1-2
- Pages
- 61-95
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1806329564
- Cited by
- 131
- References
- 28
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 1 · 2022: 7 · 2021: 7 · 2020: 6 · 2019: 3 · 2018: 6 · 2017: 5 · 2016: 3 · 2015: 7 · 2014: 3 · 2013: 4 · 2012: 13
- FWCI
- 5.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Bristol Research (University of Bristol) (submittedVersion)
- Explore Bristol Research (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- best-first search, integrity constraints, mixed theories, inductive logic programming
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 ›
Are you an author, or do you see a problem with this page? Request a review