Neuro-symbolic artificial intelligence: Current trends
Neuro-Symbolic Artificial Intelligence–the combination of symbolic methods with methods
that are based on artificial neural networks–has a long-standing history. In this article, we …
that are based on artificial neural networks–has a long-standing history. In this article, we …
Towards data-and knowledge-driven artificial intelligence: A survey on neuro-symbolic computing
Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …
Towards bridging the neuro-symbolic gap: Deep deductive reasoners
Symbolic knowledge representation and reasoning and deep learning are fundamentally
different approaches to artificial intelligence with complementary capabilities. The former are …
different approaches to artificial intelligence with complementary capabilities. The former are …
Towards data-and knowledge-driven AI: a survey on neuro-symbolic computing
Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …
Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges
Ontologies are used in various domains, with RDF and OWL being prominent standards for
ontology development. RDF is favored for its simplicity and flexibility, while OWL enables …
ontology development. RDF is favored for its simplicity and flexibility, while OWL enables …
Reason-able embeddings: Learning concept embeddings with a transferable neural reasoner
DM Adamski, J Potoniec - Semantic Web, 2024 - journals.sagepub.com
We present a novel approach for learning embeddings of ALC knowledge base concepts.
The embeddings reflect the semantics of the concepts in such a way that it is possible to …
The embeddings reflect the semantics of the concepts in such a way that it is possible to …
On the capabilities of pointer networks for deep deductive reasoning
The importance of building neural networks that can learn to reason has been well
recognized in the neuro-symbolic community. In this paper, we apply neural pointer …
recognized in the neuro-symbolic community. In this paper, we apply neural pointer …
Neuro-symbolic semantic reasoning
Humans have astounding reasoning capabilities. They can learn from very few examples
while providing explanations for their decision-making process. In contrast, deep learning …
while providing explanations for their decision-making process. In contrast, deep learning …
Deep deductive reasoning is a hard deep learning problem
Abstract Deep Deductive Reasoning refers to the training and then executing of deep
learning systems to perform deductive reasoning in the sense of formal, mathematical logic …
learning systems to perform deductive reasoning in the sense of formal, mathematical logic …
[PDF][PDF] Benchmarking Symbolic and Neuro-Symbolic Description Logic Reasoners.
G Singh - DC@ ISWC, 2023 - iswc2023.semanticweb.org
Ontologies are crucial in facilitating data sharing and integration across domains. The Web
Ontology Language (OWL and its current version, OWL 2) is widely used to build expressive …
Ontology Language (OWL and its current version, OWL 2) is widely used to build expressive …