Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

TL;DR

NeuroLang is a novel ontology language designed for scalable, probabilistic query answering over large, uncertain neuroscience datasets, integrating ontological knowledge with tractable computation.

Contribution

The paper introduces NeuroLang, a new ontology language with existential rules and probabilistic reasoning, enabling efficient querying of complex neuroscience data.

Findings

Supports large-scale, uncertain neuroscience data analysis

Provides tractable query answering mechanisms

Demonstrates practical applicability in real-world scenarios

Abstract

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, key words associated with studies, etc. Furthermore, there is an inherent uncertainty associated with brain scans arising from the mapping between voxels -- 3D pixels -- and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this kind of task is that only very limited propositional query languages have been developed. In this paper, we present NeuroLang, an ontology language with existential rules, probabilistic uncertainty, and built-in mechanisms to guarantee tractable query answering over very large datasets. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios for which current tools are inadequate.