Inference, Learning, and Population Size: Projectivity for SRL Models
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Abstract (arXiv)
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size. This paper connects the dependence on population size to the classic notion of projectivity from statistical theory: Projectivity implies that relational predictions are robust with respect to changes in domain size. We discuss projectivity for a number of common SRL systems, and identify syntactic fragments that are guaranteed to yield projective models. The syntactic conditions are restrictive, which suggests that projectivity is difficult to achieve in SRL, and care must be taken when working with different domain sizes.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2018.
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Citing papers per year: 2021: 2; 2022: 4; 2023: 0; 2024: 1.
7 papers.
- 2021An asymptotic analysis of probabilistic logic programming, with implications for expressing projective families of distributionscited by 8
- 2022On Projectivity in Markov Logic Networkscited by 5
- 2021Scaling the weight parameters in Markov logic networks and relational logistic regression modelscited by 3
- 2022Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequenciescited by 3
- 2022Projectivity revisitedcited by 2
- 2022The generalised distribution semantics and projective families of distributionscited by 0
- 2024Understanding Domain-Size Generalization in Markov Logic Networkscited by 0
- 2024Understanding Domain-Size Generalization in Markov Logic Networks23 Mar 2024
- 2022The generalised distribution semantics and projective families of distributions12 Nov 2022
- 2022Projectivity revisited1 Jul 2022
- 2022On Projectivity in Markov Logic Networks8 Apr 2022
- 2022Probabilities of the Third Type: Statistical Relational Learning and Reasoning with Relative Frequencies21 Feb 2022
- 2021Scaling the weight parameters in Markov logic networks and relational logistic regression models28 Mar 2021
- 2021An asymptotic analysis of probabilistic logic programming, with implications for expressing projective families of distributions17 Feb 2021