The DOI matches its Crossref record; 40 of the 45 references it cites were found in a registry or an index.
Learning Logical Definitions from Relations
Open accessOverview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“ILP systems such as ALEPH (Srinivasan 2001) and FOIL (Quinlan 1990) have no predicate invention and limited recursion learning and therefore cannot learn recursive grammars from example sequences.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Quinlan 1990
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Indeed, many popular ILP systems, such as FOIL (Quinlan 1990), Progol (Muggleton 1995), TILDE (Blockeel and De Raedt 1998), and Aleph (Srinivasan 2001) struggle to learn recursive programs.”
From Learning programs by learning from failures (2021) · cites this paper as Quinlan 1990
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Although early work in ILP explored this form of learning (Sammut, 1981; Quinlan, 1990), it has been under-explored until recently (Lin et al., 2014; Cropper, 2019, 2020; Hocquette & Muggleton, 2020; Dumancic et al., 2021), mostly because of new techniques for PI.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Quinlan's (1990) FOIL system is one of the most well-known ILP systems and is a natural extension of ID3 (Quinlan, 1986) from the propositional setting to the first-order setting.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
The cited source says what the claim attributes to it.
In the citing paper
“The above paragraph refers only to generality orders on single clauses, as many systems employ the covering algorithm whereby hypotheses are constructed iteratively clause by clause (Quinlan, 1990; De Raedt & Dehaspe, 1997; Muggleton, 1995; Blockeel & De Raedt, 1998; Srinivasan, 2001).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
The cited source says what the claim attributes to it.
In the citing paper
“Top-down algorithms, (Quinlan, 1990; Blockeel & De Raedt, 1998; Bratko, 1999; Muggleton et al., 2008) start with a general hypothesis and then specialise it.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Quinlan, 1990
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Self-citationClassification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
Self-citationClark, P. A., & Niblett, T. (1989) · Machine Learning · doi:10.1023/a:1022641700528 · Cited by 2,267
A Universal Prior for Integers and Estimation by Minimum Description Length
Rissanen, J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346150 · Cited by 1,655
Explanation-Based Generalization: A Unifying View
Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. (1986) · Machine Learning · doi:10.1023/a:1022691120807 · Cited by 1,382
Expert systems in the micro-electronic age
Michie, D. (1979) · CERN Document Server (European Organization for Nuclear Research) · Cited by 1,119
Shapiro, E. Y. (1983) · doi:10.7551/mitpress/1192.001.0001 · Cited by 1,085
Research works printed but not linked: 2
- Brebner, P.C. (1988). Gargantubrain: An heuristic algorithm for learning non-recursive Horn clauses from positive and negative examples. (Technical Report 8802). Sydney, Australia: University of New South Wales, School of Electrical Engineering and Computer Science.
- Schlimmer, J.C., and Fisher, D. (1986). A case study of incremental concept formation. Proceedings of the Fifth National Conference on Artificial Intelligence (pp.296-501). Philadelphia, PA: Morgan Kaufmann.
Cited by
Sorted by citations
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Cohen, W. W. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50023-2 · Cited by 3,890
A Review of Relational Machine Learning for Knowledge Graphs
Nickel, M., Murphy, K. P., Volker Tresp, C., & Gabrilovich, E. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2483592 · Cited by 1,780
Inductive Logic Programming: Theory and methods
Muggleton, S., & de Raedt, L. (1994) · The Journal of Logic Programming · doi:10.1016/0743-1066(94)90035-3 · Cited by 1,537
Statistical Fraud Detection: A Review
Bolton, R. J., & Hand, D. J. (2002) · Statistical Science · doi:10.1214/ss/1042727940 · Cited by 1,536
Muggleton, S. (1995) · New Generation Computing · doi:10.1007/bf03037227 · Cited by 1,411
Artificial Intelligence: A New Synthesis
(1998) · Elsevier eBooks · doi:10.1016/c2009-0-27773-7 · Cited by 1,211
Hand, D. J. (2007) · Drug Safety · doi:10.2165/00002018-200730070-00010 · Cited by 1,057
Dynamically discovering likely program invariants to support program evolution
Ernst, M. D., Cockrell, J., Griswold, W. G., & Notkin, D. S. (2001) · IEEE Transactions on Software Engineering · doi:10.1109/32.908957 · Cited by 1,037
Learning Information Extraction Rules for Semi-Structured and Free Text
Soderland, S. G. (1999) · Machine Learning · doi:10.1023/a:1007562322031 · Cited by 928
Fawcett, T. E., & Provost, F. J. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009700419189 · Cited by 889
Hong, Y. E., Hsu, K., Sorooshian, S., & Gao, X. (2004) · Journal of applied meteorology · doi:10.1175/jam2173.1 · Cited by 869
Enhanced hypertext categorization using hyperlinks
Chakrabarti, S., Dom, B., & Indyk, P. (1998) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/276304.276332 · Cited by 768
K*: An Instance-based Learner Using an Entropic Distance Measure
Cleary, J. G., & Trigg, L. E. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50022-0 · Cited by 761
Knowledge-based artificial neural networks
Towell, G. G., & SHAVLIK, J. W. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90105-8 · Cited by 770
Sarawagi, S. (2008) · Foundations and Trends in Databases · doi:10.1561/1900000003 · Cited by 731
Machine Learning: The art and science of algorithms that make sense of data
Flach, P. A. (2012) · Cited by 694
Bump hunting in high-dimensional data
Friedman, J. H., & Fisher, N. I. (1999) · Statistics and Computing · doi:10.1023/a:1008894516817 · Cited by 626
Handbook of Perception and Cognition
(1994) · Elsevier eBooks · doi:10.1016/b978-0-08-057169-0.50001-2 · Cited by 577
A survey of online failure prediction methods
Salfner, F., Lenk, M., & Malek, M. (2010) · ACM Computing Surveys · doi:10.1145/1670679.1670680 · Cited by 575
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 566
Separate-and-Conquer Rule Learning
Fürnkranz, J. (1999) · Artificial Intelligence Review · doi:10.1023/a:1006524209794 · Cited by 531
Quinlan, J. R., & Cameron-Jones, R. M. (1993) · Lecture Notes in Computer Science · doi:10.1007/3-540-56602-3_124 · Cited by 528
Self-citationDžeroski, S. (2006) · Springer eBooks · doi:10.1007/0-387-25465-x_41 · Cited by 523
Learning to Learn: Introduction and Overview
Thrun, S., & Pratt, L. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_1 · Cited by 521
WEKA: The Waikato Environment for Knowledge Analysis
Garner, S. (1996) · Cited by 508
Relational Learning of Pattern-Match Rules for Information Extraction.
Califf, M. E., & Mooney, R. J. (1997) · Cited by 507
Offline Arabic handwriting recognition: a survey
Lorigo, L. M., & Govindaraju, V. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.102 · Cited by 498
Das, R., Dhuliawala, S., Zaheer, M., Vilnis, L., Durugkar, I., Krishnamurthy, A., Smola, A. J., & McCallum, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05851 · Cited by 518
Learning to construct knowledge bases from the World Wide Web
Craven, M. W., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T. M., Nigam, K., & Slattery, S. (2000) · Artificial Intelligence · doi:10.1016/s0004-3702(00)00004-7 · Cited by 462
Welbl, J., Stenetorp, P., & Riedel, S. (2018) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.1162/tacl_a_00021 · Cited by 474
FURIA: an algorithm for unordered fuzzy rule induction
Hühn, J., & Hüllermeier, E. (2009) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-009-0131-8 · Cited by 442
Learning Rules that Classify E-Mail
Cohen, W. W. (1996) · Cited by 406
Domingos, P., & Lowd, D. (2009) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00206ed1v01y200907aim007 · Cited by 385
Cohen, W. W. (1998) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/276304.276323 · Cited by 379
Relational Reinforcement Learning
Džeroski, S., De Raedt, L., & Driessens, K. (2001) · Machine Learning · doi:10.1023/a:1007694015589 · Cited by 371
Incremental Reduced Error Pruning
Fürnkranz, J., & Widmer, G. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50017-9 · Cited by 371
Integrating planning and learning: the PRODIGY architecture
Veloso, M., Carbonell, J., PÉREZ, A., Borrajo, D., Fink, E., & Blythe, J. (1995) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/09528139508953801 · Cited by 365
Context-sensitive learning methods for text categorization
Cohen, W. W., & Singer, Y. (1999) · ACM Transactions on Information Systems · doi:10.1145/306686.306688 · Cited by 357
Dynamically discovering likely program invariants to support program evolution
Ernst, M. D., Cockrell, J., Griswold, W. G., & Notkin, D. S. (1999) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/302405.302467 · Cited by 357
An efficient algorithm for discovering frequent subgraphs
Kuramochi, M., & Karypis, G. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.33 · Cited by 360
Selected techniques for data mining in medicine
Lavrač, N. (1999) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(98)00062-1 · Cited by 356
Machine Learning for Information Extraction in Informal Domains
Freitag, D. (2000) · Machine Learning · doi:10.1023/a:1007601113994 · Cited by 354
Reducing Misclassification Costs
Pazzani, M. J., Merz, C. J., Murphy, P. M., Ali, K., Hume, T., & Brunk, C. A. (1994) · Elsevier eBooks · doi:10.1016/b978-1-55860-335-6.50034-9 · Cited by 343
A simple, fast, and effective rule learner
Cohen, W. W., & Singer, Y. (1999) · Cited by 341
Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco
Moritz, D., Wang, C., Nelson, G. L., Lin, H., Smith, A. M., Howe, B., & Heer, J. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2865240 · Cited by 317
A Study of Approaches to Hypertext Categorization
Yang, Y., Slattery, S., & Ghani, R. (2002) · Journal of Intelligent Information Systems · doi:10.1023/a:1013685612819 · Cited by 306
The Utility of Knowledge in Inductive Learning
Pazzani, M. J., & Kibler, D. F. (1992) · Machine Learning · doi:10.1023/a:1022628829777 · Cited by 305
Learning the structure of Markov logic networks
Kok, S., & Domingos, P. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102407 · Cited by 307
Theories for mutagenicity: a study in first-order and feature-based induction
Srinivasan, A., Muggleton, S., Sternberg, M. J. E., & King, R. D. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(95)00122-0 · Cited by 298
Error Reduction through Learning Multiple Descriptions
Ali, K., & Pazzani, M. J. (1994) · Machine Learning · doi:10.1023/a:1018249309965 · Cited by 300
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287
“Compositional Knowledge Base Inference Extracting Horn rules from the KB has been studied extensively in the Inductive Logic Programming literature (Quinlan, 1990; Muggleton, 1995).”
MethodRecognizing Textual Entailment
Dagan, I., Roth, D., Sammons, M., & Zanzotto, F. M. (2013) · Synthesis lectures on human language technologies · doi:10.2200/s00509ed1v01y201305hlt023 · Cited by 283
Multi-relational data mining: an introduction
Džeroski, S. (2003) · SKDD · doi:10.1145/959242.959245 · Cited by 298
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
Tang, L. R., & Mooney, R. J. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44795-4_40 · Cited by 263
Statistical Relational Artificial Intelligence
Raedt, L. D., Kersting, K., Natarajan, S., & Poole, D. (2016) · Synthesis lectures on artificial intelligence and machine learning · doi:10.2200/s00692ed1v01y201601aim032 · Cited by 267
Context-sensitive learning methods for text categorization
Cohen, W. W., & Singer, Y. (1996) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/243199.243278 · Cited by 249
Baltsavias, E. P. (2003) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2003.09.002 · Cited by 248
Learning Trees an ules with Set-val ed Features
Cohen, W. W. (1996) · Cited by 247
Unifying Instance-Based and Rule-Based Induction
Domingos, P. (1996) · Machine Learning · doi:10.1007/bf00058656 · Cited by 240
Sequential covering rule induction algorithm for variable consistency rough set approaches
Błaszczyński, J., Słowiński, R., & Szeląg, M. (2010) · Information Sciences · doi:10.1016/j.ins.2010.10.030 · Cited by 238
Information extraction from HTML: application of a general machine learning approach
Freitag, D. (1998) · Cited by 236
De Raedt, L., & Dehaspe, L. (1997) · Machine Learning · doi:10.1023/a:1007361123060 · Cited by 235
A flexible learning system for wrapping tables and lists in HTML documents
Cohen, W. W., Hurst, M. F., & Jensen, L. S. (2002) · International World Wide Web Conference (WWW) · doi:10.1145/511446.511477 · Cited by 234
Decision Trees for Uncertain Data
Tsang, S., Kao, B., Yip, K. Y.-L., Ho, W.-S., & Lee, S. D. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.175 · Cited by 231
ROC ?n? Rule Learning?Towards a Better Understanding of Covering Algorithms
Fürnkranz, J., & Flach, P. A. (2005) · Machine Learning · doi:10.1007/s10994-005-5011-x · Cited by 229
Data integration using similarity joins and a word-based information representation language
Cohen, W. W. (2000) · ACM Transactions on Information Systems · doi:10.1145/352595.352598 · Cited by 225
Supervised versus multiple instance learning
Ray, S., & Craven, M. W. (2005) · International Conference on Machine Learning · doi:10.1145/1102351.1102439 · Cited by 222
Intelligent financial fraud detection practices in post-pandemic era
Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100176 · Cited by 238
Learning to predict rare events in event sequences
Weiss, G. M., & Hirsh, H. (1998) · Cited by 219
Machine Learning SNP Based Prediction for Precision Medicine
Ho, D., Schierding, W. S., Wake, M. A., Saffery, R., & O'Sullivan, J. M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00267 · Cited by 220
Howley, T., Madden, M. G., O’Connell, M.-L., & Ryder, A. G. (2006) · Knowledge-Based Systems · doi:10.1016/j.knosys.2005.11.014 · Cited by 212
Combining Classifiers with Meta Decision Trees
Todorovski, L., & Džeroski, S. (2003) · Machine Learning · doi:10.1023/a:1021709817809 · Cited by 204
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets
Moore, A., & Lee, M. S. (1998) · Journal of Artificial Intelligence Research · doi:10.1613/jair.453 · Cited by 201
Ontology learning: state of the art and open issues
Zhou, L. (2007) · Information Technology and Management · doi:10.1007/s10799-007-0019-5 · Cited by 200
Recognizing textual entailment models and applications
Dagan, I. (2013) · Cited by 205
Relational reinforcement learning
Džeroski, S., Raedt, L. D., & Blockeel, H. (1998) · Lecture notes in computer science · doi:10.1007/bfb0027307 · Cited by 196
Adaptive information extraction
Turmo, J., Ageno, A., & Català, N. (2006) · ACM Computing Surveys · doi:10.1145/1132956.1132957 · Cited by 193
Rhee, S., Seo, S., & Kim, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/490 · Cited by 191
Mooney, R. J. (1996) · arXiv (Cornell University) · doi:10.48550/arxiv.cmp-lg/9612001 · Cited by 192
Search-Intensive Concept Induction
Giordana, A., & Neri, F. (1995) · Evolutionary Computation · doi:10.1162/evco.1995.3.4.375 · Cited by 189
Temporal classification : extending the classification paradigm to multivariate time series
Kadous, M. W. (2022) · doi:10.26190/unsworks/8829 · Cited by 188
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Muggleton, S. H., Lin, D., & Tamaddoni-Nezhad, A. (2015) · Machine Learning · doi:10.1007/s10994-014-5471-y · Cited by 184
Machine Learning and Software Engineering
Zhang, D., & Tsai, J. J. P. (2003) · Software Quality Journal · doi:10.1023/a:1023760326768 · Cited by 184
Dynamically discovering likely program invariants
Ernst, M. D., & Notkin, D. S. (2000) · Cited by 183
Induction of logic programs: FOIL and related systems
Quinlan, J. R., & Cameron-Jones, R. M. (1995) · New Generation Computing · doi:10.1007/bf03037228 · Cited by 182
Self-citationEffective learning in dynamic environments by explicit context tracking
Widmer, G., & Kubát, M. (1993) · Lecture notes in computer science · doi:10.1007/3-540-56602-3_139 · Cited by 176
Applying machine learning to agricultural data
McQueen, R. J., Garner, S. R., Nevill-Manning, C. G., & Witten, I. H. (1995) · Computers and Electronics in Agriculture · doi:10.1016/0168-1699(95)98601-9 · Cited by 173
Ontology Population and Enrichment: State of the Art
Petasis, G., Karkaletsis, V., Παλιούρας, Γ., Krithara, A., & Zavitsanos, E. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-20795-2_6 · Cited by 171
Shanahan, J. G. (2000) · Springer eBooks · doi:10.1007/978-1-4615-4335-0_2 · Cited by 170
Trading Accuracy for Simplicity in Decision Trees
Bohanec, M., & Bratko, I. (1994) · Machine Learning · doi:10.1023/a:1022685808937 · Cited by 170
Learning First-Order Horn Clauses from Web Text
Schoenmackers, S., Etzioni, O., Weld, D. S., & Davis, J. (2010) · Lirias · Cited by 168
A hybrid decision tree/genetic algorithm method for data mining
Carvalho, D. R., & Freitas, A. A. (2003) · Information Sciences · doi:10.1016/j.ins.2003.03.013 · Cited by 168
Bottom-up learning of Markov logic network structure
Mihalkova, L. S., & Mooney, R. J. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273575 · Cited by 168
Learning nonrecursive definitions of relations with linus
Nada Lavrač, A., Džeroski, S., & Grobelnik, M. (1991) · Lecture notes in computer science · doi:10.1007/bfb0017020 · Cited by 164
Applications of inductive logic programming
Bratko, I., & Muggleton, S. (1995) · Communications of the ACM · doi:10.1145/219717.219771 · Cited by 164
Wrapper Maintenance: A Machine Learning Approach
Lerman, K., Minton, S., & Knoblock, C. A. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1145 · Cited by 162
Kernels and Distances for Structured Data
Gärtner, T., Lloyd, J. W., & Flach, P. A. (2004) · Machine Learning · doi:10.1023/b:mach.0000039777.23772.30 · Cited by 159
Enhanced hypertext categorization using hyperlinks
Chakrabarti, S., Dom, B., & Indyk, P. (1998) · ACM SIGMOD Record · doi:10.1145/276305.276332 · Cited by 159
Theory refinement combining analytical and empirical methods
Ourston, D., & Mooney, R. J. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90028-0 · Cited by 158
Metadata
- Authors
- Quinlan, J. R.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Quinlan, J.R.
- Venue
- Machine Learning
- Published
- 1990-08
- Volume
- 5
- Issue
- 3
- Pages
- 239-266
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1999138184
- Cited by
- 1,644
- References
- 45
- Citations by year
- 2026: 8 · 2025: 18 · 2024: 21 · 2023: 23 · 2022: 22 · 2021: 34 · 2020: 34 · 2019: 26 · 2018: 36 · 2017: 29 · 2016: 25 · 2015: 29 · 2014: 44 · 2013: 44 · 2012: 41
- FWCI
- 47.93
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Funders
- Australian Research Council
- 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 › Machine Learning and Algorithms
- Keywords
- foil, inductive logic programming, Horn clauses, relational data, first-order logic, rule induction
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