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Title: Efficient Probabilistic Logic Reasoning with Graph Neural Networks

Paper content:
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems.
However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult.
In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems.
Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task.
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model.
Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.
Knowledge graphs collect and organize relations and attributes about entities, which are playing an increasingly important role in many applications, including question answering and information retrieval.
Since knowledge graphs may contain incorrect, incomplete or duplicated records, additional processing such as link prediction, attribute classification, and record de-duplication is typically needed to improve the quality of knowledge graphs and derive new facts.
Markov Logic Networks (MLNs) were proposed to combine hard logic rules and probabilistic graphical models, which can be applied to various tasks on knowledge graphs (Richardson & Domingos, 2006) .
The logic rules incorporate prior knowledge and allow MLNs to generalize in tasks with small amount of labeled data, while the graphical model formalism provides a principled framework for dealing with uncertainty in data.
However, inference in MLN is computationally intensive, typically exponential in the number of entities, limiting the real-world application of MLN.
Graph neural networks (GNNs) have recently gained increasing popularity for addressing many graph related problems effectively (Dai et al., 2016; Li et al., 2016; Kipf & Welling, 2017; Schlichtkrull et al., 2018) .
However, the design and training procedure of GNNs do not explicitly take into account the prior knowledge in the form of logic rules.
To achieve good performance, these models typically require sufficient labeled instances on specific end tasks (Xiong et al., 2018) .
In this paper, we explore the combination of the best of both worlds, aiming for a method which is data-driven yet can exploit the prior knowledge encoded in logic rules.
To this end, we design a simple variant of graph neural networks, named ExpressGNN, which can be efficiently trained in the variational EM framework for MLN.
An overview of our method is illustrated in Fig. 1 .
ExpressGNN and the corresponding reasoning framework lead to the following desiderata:
• Efficient inference and learning: ExpressGNN can be viewed as the inference network for MLN, which scales up MLN inference to much larger knowledge graph problems.
• Combining logic rules and data supervision: ExpressGNN can leverage the prior knowledge encoded in logic rules, as well as the supervision from labeled data.
• Compact and expressive model: ExpressGNN may have small number of parameters, yet it is sufficient to represent mean-field distributions in MLN.
This paper studies the probabilistic logic reasoning problem, and proposes ExpressGNN to combine the advantages of Markov Logic Networks in logic reasoning and graph neural networks in graph representation learning.
ExpressGNN addresses the scalability issue of Markov Logic Networks with efficient stochastic training in the variational EM framework.
ExpressGNN employs GNNs to capture the structure knowledge that is implicitly encoded in the knowledge graph, which serves as supplement to the knowledge from logic formulae.
ExpressGNN is a general framework that can trade-off the model compactness and expressiveness by tuning the dimensionality of the GNN and the embedding part.
Extensive experiments on multiple benchmark datasets demonstrates the effectiveness and efficiency of ExpressGNN.

assistant: text: We employ graph neural networks in the variational EM framework for efficient inference and learning of Markov Logic Networks.