Markov Logic

Markov Logic Networks (MLNs) combine the expressive power of first-order logic with the uncertainty handling of probabilistic graphical models, aiming to represent and reason with knowledge in complex, relational domains. Current research focuses on improving the efficiency and accuracy of inference in MLNs, particularly addressing challenges related to domain size generalization and the development of robust and scalable algorithms like variational inference and MCMC methods. These advancements are impacting various fields, including knowledge graph reasoning, reinforcement learning, and the development of more robust and explainable AI systems by integrating symbolic reasoning with neural networks.

Papers

October 23, 2024

Incremental Learning of Affordances using Markov Logic Networks
George Potter, Gertjan Burghouts, Joris Sijs
Incremental Learning Affordance Learning Markov Logic

September 30, 2024

Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truth
Joseph D. Ramsey, Bryan Andrews, Peter Spirtes
Ground Truth Conditional Independence Directed Acyclic Graph Markov Model Markov Logic

July 4, 2024

Neural Probabilistic Logic Learning for Knowledge Graph Reasoning
Fengsong Sun, Jinyu Wang, Zhiqing Wei, Xianchao Zhang
Knowledge Graph Knowledge Graph Reasoning Large Scale Knowledge Graph Markov Logic Neural Probabilistic

March 23, 2024

Understanding Domain-Size Generalization in Markov Logic Networks
Florian Chen, Felix Weitkämper, Sagar Malhotra
Strong Generalization Human Understanding Markov Chain Relational Model Markov Logic

December 13, 2023

On the verification of Embeddings using Hybrid Markov Logic
Anup Shakya, Abisha Thapa Magar, Somdeb Sarkhel, Deepak Venugopal
Jina Embeddings Verification Task Knowledge Tracing Learned Representation Symbolic Knowledge Markov Logic Probabilistic Language

November 27, 2023

Neural Markov Prolog
Alexander Thomson, David Page
Neural Architecture Markov Logic Neural Network Design SWI Prolog

February 6, 2023

On Exact Sampling in the Two-Variable Fragment of First-Order Logic
Yuanhong Wang, Juhua Pu, Yuyi Wang, Ondřej Kuželka
First Order Logic Probabilistic Logic Domain Transferability Markov Logic

November 29, 2022

Parameterisation of Reasoning on Temporal Markov Logic Networks
Victor David, Raphaël Fournier-S'niehotta, Nicolas Travers
Complex Reasoning Temporal Logic Formula Markov Logic Temporal Fact

October 13, 2022

Policy Gradient With Serial Markov Chain Reasoning
Edoardo Cetin, Oya Celiktutan
Reinforcement Learning Policy Gradient Action Selection Iterative Reasoning Markov Logic

October 1, 2022

Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs
Luigi Bellomarini, Eleonora Laurenza, Emanuel Sallinger, Evgeny Sherkhonov
Knowledge Graph Top Level Ontology Markov Logic

September 12, 2022

CARE: Certifiably Robust Learning with Reasoning via Variational Inference
Jiawei Zhang, Linyi Li, Ce Zhang, Bo Li
Native Robustness Complex Reasoning Variational Inference Robust Learning Markov Logic

June 23, 2022

plingo: A system for probabilistic reasoning in clingo based on lpmln
Susana Hahn (1), Tomi Janhunen (2), Roland Kaminski (1), Javier Romero (1), Nicolas Rühling (1), Torsten Schaub (1) ((1) University of Potsdam, Germany, (2) Tampere University, Finland)
System Description Probabilistic Approach Markov Logic Probabilistic Reasoning

April 8, 2022

On Projectivity in Markov Logic Networks
Sagar Malhotra, Luciano Serafini
Two Layer Neural Network Projection Bias Spatial Transformer Network Projective Simulation Markov Logic