A Bayesian Approach to Constructing Probabilistic Models from Knowledge Graphs

Making predictions bounded by uncertainty is a crucial component of modern decision support systems. Knowledge graphs provide a structured, semantic-oriented approach to data integration, storage and retrieval; however, they represent data in an absolute way and do not natively support reasoning under uncertain future conditions. In this paper, we present a novel technique for building a probabilistic model from a knowledge graph using a Bayesian network, which is a step towards enabling probabilistic reasoning under uncertainty within a knowledge graph. Our approach supports mixing of continuously and discretely-distributed variables, which is necessary for jointly processing real-world data, but has not typically been supported in prior work. As an interface to the probabilistic model, we propose an extension of the SPARQL query language called Orion DSL, which is currently a working prototype. We also define a custom probabilistic ontology in order to store outputs of the model directly in the knowledge graph alongside the original data, which we refer to as a Probabilistic Knowledge Graph (PKG). The evaluation shows that: (1) the dependencies and distributions of data in a synthetically-generated knowledge graph were accurately captured by the Bayesian model, and (2) an SPARQL query against the PKG to retrieve computed probability distributions was orders of magnitude more performant than a similarly-intentioned query against the base knowledge graph. We anticipate that the models generated by this system will have applications in the areas of predicting missing data, approximate query processing, and utility-based optimization. Future work will involve more detailed explorations of each of these topics as we work towards a PKG-based decision support system.