Constraint-Based Synthesis of Datalog Programs
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ILP systems have proven effective in learning SQL queries.
“ILP systems have proven effective in learning SQL queries (Albarghouthi et al., 2017; Sivaraman et al., 2019)”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Albarghouthi et al., 2017
Cites
de Moura, L., & Bjørner, N. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-78800-3_24 · Cited by 6,595 · see citing works
Muggleton, S. (1991) · New Generation Computing · doi:10.1007/bf03037089 · Cited by 1,044 · see citing works
Combinatorial sketching for finite programs
Solar-Lezama, A., Tancau, L., Bodík, R., Seshia, S., & Saraswat, V. (2006) · ASPLOS XII · doi:10.1145/1168857.1168907 · Cited by 922 · see citing works
Program Analysis and Specialization for the C Programming Language
Andersen, L. O., & Lee, P. (2005) · Cited by 903 · see citing works
Cloning-based context-sensitive pointer alias analysis using binary decision diagrams
Whaley, J., & Lam, M. S. (2004) · Proceedings of the ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/996841.996859 · Cited by 590 · see citing works
Towards a Mathematical Science of Computation
McCarthy, J. (1993) · Studies in cognitive systems · doi:10.1007/978-94-011-1793-7_2 · Cited by 529 · see citing works
Oracle-guided component-based program synthesis
Jha, S., Gulwani, S., Seshia, S. A., & Tiwari, A. (2010) · Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 · doi:10.1145/1806799.1806833 · Cited by 365 · see citing works
Foundations of Databases: The Logical Level
Abiteboul, S., Hull, R. B., & Vianu, V. (1995) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 341 · see citing works
Logical and Relational Learning
De Raedt, L. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88190-2_1 · Cited by 326 · see citing works
Spreadsheet data manipulation using examples
Gulwani, S., Harris, W. R., & Singh, R. K. (2012) · Communications of the ACM · doi:10.1145/2240236.2240260 · Cited by 296 · see citing works
Synthesis of loop-free programs
Gulwani, S., Jha, S., Tiwari, A., & Venkatesan, R. (2011) · ACM SIGPLAN Notices · doi:10.1145/1993316.1993506 · Cited by 246 · see citing works
Design and implementation of the LogicBlox system
Aref, M., ten Cate, B., Green, T. J., Kimelfeld, B., Olteanu, D., Pasalic, E., Veldhuizen, T. L., & Washburn, G. (2015) · Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2723372.2742796 · Cited by 233 · see citing works
Logical and Relational Learning
(2008) · Cognitive Technologies · doi:10.1007/978-3-540-68856-3 · Cited by 233 · see citing works
Synthesis of loop-free programs
Gulwani, S., Jha, S., Tiwari, A., & Venkatesan, R. (2011) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/1993498.1993506 · Cited by 201 · see citing works
Program synthesis from polymorphic refinement types
Polikarpova, N., Kuraj, I., & Solar-Lezama, A. (2016) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/2908080.2908093 · Cited by 189 · see citing works
Smaragdakis, Y., & Balatsouras, G. (2015) · Foundations and Trends® in Programming Languages · doi:10.1561/2500000014 · Cited by 189 · see citing works
Type-and-example-directed program synthesis
Osera, P.-M., & Zdancewic, S. (2015) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/2737924.2738007 · Cited by 185 · see citing works
Declarative information extraction using datalog with embedded extraction predicates
Shen, W., Doan, A., Naughton, J. F., & Ramakrishnan, R. (2007) · Cited by 180 · see citing works
Albarghouthi, A., Gulwani, S., & Kincaid, Z. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39799-8_67 · Cited by 147 · see citing works
Self-citationMeta-interpretive learning: application to grammatical inference
Muggleton, S. H., Lin, D., Pahlavi, N., & Tamaddoni-Nezhad, A. (2014) · Machine Learning · doi:10.1007/s10994-013-5358-3 · Cited by 120 · see citing works
Counterexample-Guided Quantifier Instantiation for Synthesis in SMT
Reynolds, A., Deters, M., Kunčak, V., Tinelli, C., & Barrett, C. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21668-3_12 · Cited by 119 · see citing works
Synthesis modulo recursive functions
Kneuss, E., Kuraj, I., Kunčak, V., & Suter, P. (2013) · Conference on Object-Oriented Programming Systems, Languages, and Applications · doi:10.1145/2509136.2509555 · Cited by 109 · see citing works
Satisfiability Modulo Recursive Programs
Suter, P., Köksal, A. S., & Kunčak, V. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23702-7_23 · Cited by 101 · see citing works
SociaLite: Datalog extensions for efficient social network analysis
Seo, J., Guo, S. D., & Lam, M. S. (2013) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2013.6544832 · Cited by 99 · see citing works
μZ– An Efficient Engine for Fixed Points with Constraints
Hoder, K., Bjørner, N., & de Moura, L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22110-1_36 · Cited by 92 · see citing works
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
Kitzelmann, E., & Schmid, U. (2006) · doi:10.5555/1248547.1248562 · Cited by 85 · see citing works
Example-directed synthesis: a type-theoretic interpretation
Frankle, J., Osera, P.-M., Walker, D., & Zdancewic, S. (2016) · ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL) · doi:10.1145/2837614.2837629 · Cited by 73 · see citing works
Perelman, D., Gulwani, S., Grossman, D., & Provost, P. (2014) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/2594291.2594297 · Cited by 72 · see citing works
Asynchronous and fault-tolerant recursive datalog evaluation in shared-nothing engines
Wang, J., Bałazińska, M., & Halperin, D. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2824032.2824052 · Cited by 59 · see citing works
Smaragdakis, Y., & Balatsouras, G. (2015) · now publishers, Inc. eBooks · doi:10.1561/9781680830217 · Cited by 55 · see citing works
Inductive synthesis of recursive logic programs: achievements and prospects
Flener, P., & Yıilmaz, S. (1999) · The Journal of Logic Programming · doi:10.1016/s0743-1066(99)00028-x · Cited by 51 · see citing works
Bradley, A. R., & Manna, Z. (2007) · Springer eBooks · doi:10.1007/978-3-540-74113-8 · Cited by 37 · see citing works
Optimizing Large-Scale Semi-Naïve Datalog Evaluation in Hadoop
Shaw, M., Koutris, P., Howe, B., & Suciu, D. M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-32925-8_17 · Cited by 33 · see citing works
Self-citationMeta-Interpretive Learning of Data Transformation Programs
Cropper, A., Tamaddoni-Nezhad, A., & Muggleton, S. H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-40566-7_4 · Cited by 32 · see citing works
(2015) · International Conference on Artificial Intelligence · Cited by 25 · see citing works
Bias reformulation for one-shot function induction
Lin, D., Dechter, E., Ellis, K., Tenenbaum, J. B., & Muggleton, S. (2014) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 24 · see citing works
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Learning programs by learning from failures
Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works
“Indeed, PL approaches rarely evaluate the ability of their systems to synthesise solutions that generalise, i.e. they do not measure predictive accuracy (Feser et al. 2015; Polikarpova et al. 2016; Albarghouthi et al. 2017; Feng et al. 2018; Raghothaman et al. 2020).”
BackgroundInfluentialInductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
“they do not measure predictive accuracy (Feser et al., 2015; Osera & Zdancewic, 2015; Albarghouthi et al., 2017).”
BackgroundMethodInfluentialTurning 30: New Ideas in Inductive Logic Programming
Cropper, A., Dumančić, S., & Muggleton, S. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/673 · Cited by 78 · see citing works
Inductive logic programming at 30
Cropper, A., Dumančić, S., Evans, R., & Muggleton, S. H. (2022) · Machine Learning · doi:10.1007/s10994-021-06089-1 · Cited by 61 · see citing works
Provenance-guided synthesis of Datalog programs
Raghothaman, M., Mendelson, J., Zhao, D., Naik, M., & Scholz, B. (2019) · Proceedings of the ACM on Programming Languages · doi:10.1145/3371130 · Cited by 50 · see citing works
Self-citationSyntax-guided synthesis of Datalog programs
Si, X., Lee, W., Zhang, R., Albarghouthi, A., Koutris, P., & Naik, M. (2018) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3236024.3236034 · Cited by 49 · see citing works
Self-citationSynthesizing database programs for schema refactoring
Wang, Y., Dong, J., Shah, R., & Dillig, I. (2019) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3314221.3314588 · Cited by 45 · see citing works
Synthesizing Datalog Programs using Numerical Relaxation
Si, X., Raghothaman, M., Heo, K., & Naik, M. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/847 · Cited by 48 · see citing works
Self-citationLogical reduction of metarules
Cropper, A., & Tourret, S. (2020) · Machine Learning · doi:10.1007/s10994-019-05834-x · Cited by 29 · see citing works
Data migration using datalog program synthesis
Wang, Y., Shah, R., Criswell, A., Pan, R., & Dillig, I. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3384345.3384350 · Cited by 28 · see citing works
Learning Security Classifiers with Verified Global Robustness Properties
Chen, Y., Wang, S., Qin, Y., Liao, X.-J., Jana, S., & Wagner, D. A. (2021) · Conference on Computer and Communications Security · doi:10.1145/3460120.3484776 · Cited by 36 · see citing works
Formulog: Datalog for SMT-based static analysis
Bembenek, A., Greenberg, M., & Chong, S. (2020) · Proceedings of the ACM on Programming Languages · doi:10.1145/3428209 · Cited by 26 · see citing works
A Critical Review of Inductive Logic Programming Techniques for Explainable AI
Zhang, Z., Yilmaz, L., & Liu, B. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3246980 · Cited by 25 · see citing works
Rules, L. O.-R., & A, F. A. L. (1999) · Lecture notes in computer science · doi:10.1007/3-540-48751-4 · Cited by 24 · see citing works
Derivation Reduction of Metarules in Meta-interpretive Learning
Cropper, A., & Tourret, S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-99960-9_1 · Cited by 15 · see citing works
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Pailoor, S., Wang, Y., & Dillig, I. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3632870 · Cited by 13 · see citing works
Example-guided synthesis of relational queries
Thakkar, A., Naik, A., Sands, N., Rajeev Alur, A., Naik, M., & Raghothaman, M. (2021) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3453483.3454098 · Cited by 12 · see citing works
Self-citationGENSYNTH: Synthesizing Datalog Programs without Language Bias
Mendelson, J., Naik, A., Raghothaman, M., & Naik, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i7.16799 · Cited by 12 · see citing works
Self-citationTyped Meta-interpretive Learning of Logic Programs
Morel, R., Cropper, A., & Ong, C.-H. L. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-19570-0_13 · Cited by 11 · see citing works
Towards Example-Guided Network Synthesis
Chen, H., Wang, A., & Loo, B. T. (2018) · Asia-Pacific Workshop on Networking (APNet) · doi:10.1145/3232565.3234462 · Cited by 9 · see citing works
Datalog-based scalable semantic diffing of concurrent programs
Sung, C., Lahiri, S. K., Enea, C., & Wang, C. (2018) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3238147.3238211 · Cited by 8 · see citing works
Principles and Practice of Constraint Programming
German, G., Olivier Briant, C., Cambazard, H., & Jost, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66158-2 · Cited by 7 · see citing works
Synthesizing code quality rules from examples
Garg, P., & Sengamedu, S. H. (2022) · Proceedings of the ACM on Programming Languages · doi:10.1145/3563350 · Cited by 9 · see citing works
From SMT to ASP: Solver-Based Approaches to Solving Datalog Synthesis-as-Rule-Selection Problems
Bembenek, A., Greenberg, M., & Chong, S. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3571200 · Cited by 6 · see citing works
Relational Program Synthesis with Numerical Reasoning
Hocquette, C., & Cropper, A. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25790 · Cited by 5 · see citing works
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Krogmeier, P., & Madhusudan, P. (2022) · Proceedings of the ACM on Programming Languages · doi:10.1145/3498671 · Cited by 6 · see citing works
Toward Example-Driven Program Synthesis of Story Sifting Patterns.
Kreminski, M., Wardrip–Fruin, N., & Mateas, M. (2020) · Cited by 4 · see citing works
Lubin, J. P., Ziegler, P., & Chasins, S. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3729264 · Cited by 3 · see citing works
Design Datalog Templates for Synthesizing Bidirectional Programs from Tabular Examples
Trong, B. N., Tsushima, K., & Hu, Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45784-5_2 · Cited by 3 · see citing works
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Tourret, S., & Cropper, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-19570-0_17 · Cited by 3 · see citing works
Generating Programmatic Referring Expressions via Program Synthesis
Huang, J., Smith, C., Bastani, O., Singh, R., Albarghouthi, A., & Naik, M. (2020) · International Conference on Machine Learning · Cited by 3 · see citing works
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Liu, J., Murphy, C., Grover, A., Johnson, K. J. C., Reps, T., & D’Antoni, L. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3689724 · Cited by 1 · see citing works
FormuLog: Datalog for static analysis involving logical formulaePreprint
Bembenek, A., & Chong, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06274
SLD-Resolution Reduction of Second-Order Horn Fragments -- technical report --Preprint
Tourret, S., & Cropper, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09900 · Cited by 1 · see citing works
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Krogmeier, P., & Madhusudan, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03534
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- Authors
- Albarghouthi, A., Koutris, P., Naik, M., & Smith, C.
- Affiliations
- University of Wisconsin–Madison (US)
- University of Pennsylvania (US)
- Corresponding author
- Albarghouthi, Aws
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 689-706
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2750179104
- Cited by
- 40
- References
- 37
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 7 · 2020: 6 · 2019: 8 · 2018: 5 · 2017: 1
- FWCI
- 2.49
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Keywords
- program synthesis from examples, SMT solvers, parse trees, recursive programs, graph manipulation
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