On the Benefits of OWL-based Knowledge Graphs for Neural-Symbolic Systems

Knowledge graphs, as understood within the Semantic Web and Knowledge Representation communities, are more than just graph data. OWL-based knowledge graphs offer the benefits of being based on an ecosystem of open W3C standards that are implemented in a range of reusable existing resources (e.g. curated ontologies, software tools, web-wide linked data) and that also permit researchers to tailor resources for their unique needs (e.g. custom ontologies). Additionally, OWL-based knowledge graphs offer the benefits of formal, logical symbolic reasoning (e.g. reliable inference of new knowledge based on Description Logics, semantic consistency checking, extensions via user-defined Datalog rules). These capabilities allow OWL-based knowledge graphs to be leveraged in the form of active reasoning agents to guide deep learning during training and to participate in refining neural inference. We enumerate a host of such benefits to using OWL-based knowledge graphs in neural-symbolic systems. We illustrate several of these by drawing upon examples from our research in visual relationship detection within images, and we point to promising research directions and challenging opportunities

On the Potential of Logic and Reasoning in Neurosymbolic Systems using OWL-based Knowledge Graphs

Knowledge graphs feature ever more frequently as symbolic components in neurosymbolic research and systems. But even though a central concern of neurosymbolic AI is to combine neural learning with symbolic reasoning, relatively little neurosymbolic research focuses on leveraging the logical representation and reasoning capabilities of OWL-based knowledge graphs. The objective of this position paper is to inspire more neurosymbolic researchers to embrace the OWL and the Semantic Web by raising awareness of the benefits, capabilities, and applications of OWL-based knowledge graphs, particularly with respect to logical reasoning. We describe the ecosystem of open W3C standards-based resources available that support the adoption and use of OWL-based knowledge graphs; we describe tools that exist for engineering custom OWL ontologies tailored to particular research needs; we discuss the encoding of background KG knowledge in subsymbolic embedding spaces and various applications of this approach; we discuss and illustrate the reasoning capabilities of OWL-based knowledge graphs; and we describe several promising directions for research that focus on leveraging these reasoning capabilities. We also discuss the specialised resources needed to undertake research on OWL-based knowledge graphs in neurosymbolic systems. We use the example of NeSy4VRD, an image dataset with a custom-designed companion OWL ontology. The scarcity of this kind of resource should be addressed to accelerate research in this field

Proceedings of The Tenth International Workshop on Ontology Matching (OM-2015)

shvaiko2016aInternational audienceno abstrac

Unchain My EL Reasoner.

We study a restriction of the classification procedure for EL++ where the inference rule for complex role inclusion axioms (RIAs) is applied in a "left-linear" way in analogy with the well-known procedure for computing the transitive closure of a binary relation. We introduce a notion of left-admissibility for a set of RIAs, which specifies when a subset of RIAs can be used in a left-linear way without loosing consequences, prove a criterion which can be used to effectively check this property, and describe some preliminary experimental results analyzing when the restricted procedure can give practical improvements

Unchain My EL Reasoner

We study a restriction of the classification procedure for EL++ where the inference rule for complex role inclusion axioms (RIAs) is applied in a ``left-linear'' way in analogy with the well-known procedure for computing the transitive closure of a binary relation. We introduce a notion of left-admissibility for a set of RIAs, which specifies when a subset of RIAs can be used in a left-linear way without loosing consequences, prove a criterion which can be used to effectively check this property, and describe some preliminary experimental results analyzing when the restricted procedure can give practical improvements