Towards Automatically Setting Language Bias in Relational Learning
Abstract
Relational databases are valuable resources for learning novel and interesting relations and concepts. Relational learning algorithms learn the definition of new relations in terms of the existing relations in the database. In order to constraint the search through the large space of candidate definitions, users must specify a language bias. Unfortunately, specifying the language bias is done via trial and error and is guided by the expert's intuitions. Hence, it normally takes a great deal of time and effort to effectively use these algorithms. We report our on-going work on building AutoMode, a system that leverages information in the schema and content of the database to automatically induce the language bias used by popular relational learning algorithms.
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It is still a major challenge to choose appropriate mode declarations.
“Although there is some work on learning mode declarations (McCreath & Sharma, 1995; Ferilli et al., 2004; Picado et al., 2017), it is still a major challenge to choose appropriate ones.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Picado et al., 2017
Although there is some work on mode learning and identifying suitable metarules, this area of research is largely under-researched.
“Although there is some work on mode learning (McCreath & Sharma, 1995; Ferilli et al., 2004; Picado et al., 2017) and work on identifying suitable metarules (Cropper & Tourret, 2020), this area of research is largely under-researched.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Picado et al., 2017
Cites
Introduction to Statistical Relational Learning
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Schema Independent Relational Learning
Picado, J., Termehchy, A., Fern, A., & Ataei, P. (2017) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3035918.3035923 · Cited by 20 · see citing works
Self-citationAbout Knowledge and Inference in Logical and Relational Learning
Raedt, L. D. (2009) · Studies in computational intelligence · doi:10.1007/978-3-642-05179-1_7 · Cited by 18 · see citing works
Inductive Logic Programming Meets Relational Databases: Efficient Learning of Markov Logic Networks
Malec, M., Khot, T., Nagy, J. G., Blask, E., & Natarajan, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63342-8_2 · Cited by 8 · see citing works
Automatic Induction of First-Order Logic Descriptors Type Domains from Observations
Ferilli, S., Esposito, F., Basile, T. M. A., & Di Mauro, N. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30109-7_12 · Cited by 8 · see citing works
An Algorithm for Incremental Mode Induction
Mauro, N. D., Esposito, F., Ferilli, S., & Basile, T. M. A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24677-0_53 · Cited by 2 · see citing works
Automatic induction of domain-related information: learning descriptors type domains
Ferilli, S., Esposito, F., Basile, T. M. A., & Mauro, N. D. (2004) · Cited by 1 · see citing works
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Inductive 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
“Although there is some work on learning mode declarations (McCreath & Sharma, 1995; Ferilli et al., 2004; Picado et al., 2017), it is still a major challenge to choose appropriate ones.”
BackgroundInductive 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
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
Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning
Jin, Y., Liu, J., Luo, Z., Peng, Y., Qin, Z., Dai, W.-Z., Ding, Y.-X., & Zhou, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-09087-4_11
- Authors
- Picado, J., Termehchy, A., Fern, A., & Pathak, S.
- Affiliations
- Oregon State University (US)
- Venue
- Proceedings of the 1st Workshop on Data Management for End-to-End Machine Learning
- Publisher
- ACM
- Published
- 2017-05-14
- Pages
- 1-4
- OpenAlex
- W2611695879
- Cited by
- 4
- References
- 9
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Workshop on Data Management for End-to-End Machine Learning (DEEM) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- relational learning, language bias, relational databases, schema information, concept learning
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