Statistical Relational Structure Learning with Scaled Weight Parameters

Weitkämper, Felix ; Ravdin, Dmitriy und Fabry, Ramona (2023): Statistical Relational Structure Learning with Scaled Weight Parameters. 32nd International Conference on Inductive Logic Programming (ILP), Bari, Italy, 13. - 15. Oktober 2023. Bellodi, Elena; Lisi, Francesca Alessandra und Zese, Riccardo (Hrsg.): In: Inductive Logic Programming, Lecture Notes in Computer Science Bd. 14363 Cham: Springer. S. 139-153

Abstract

Markov Logic Networks (MLNs) combine relational specifications with probabilistic learning and reasoning. Although they can be specified without reference to a particular domain, their performance across domains of different sizes is generally unfavorable. Domain-size Aware Markov Logic Networks (DA-MLNs) address this issue by scaling down weight parameters based on the domain size. This allows for faster learning by training models on a random sample of the original domain. DA-MLNs also enable transferring models between naturally occurring domains of different sizes. This study proposes a combination of functional gradient boosting and weight scaling for single-target structure learning on large domains. It also evaluates performance and runtime on two benchmark domains of contrasting sizes. The results demonstrate that training a DA-MLN from a sample can significantly reduce learning time for large domains with minor performance trade-offs, which decrease with the size of the original domain. Additionally, the study explores how scaling reacts to varying domain sizes in a synthetic social network domain. It is observed that DA-MLNs outperform unscaled MLNs when the number of connections between individuals grows with domain size, but perform worse when the number of connections remains constant. This justifies the use of unscaled MLNs when sampling isolated subcommunities in areas such as social sciences research.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-031-49298-3 ; 978-3-031-49299-0
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch