A review of relational machine learning for knowledge graphs
Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …
graph-structured, data. In this paper, we provide a review of how such statistical models can …
Neuro-symbolic artificial intelligence: The state of the art
Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …
How large language models will disrupt data management
Large language models (LLMs), such as GPT-4, are revolutionizing software's ability to
understand, process, and synthesize language. The authors of this paper believe that this …
understand, process, and synthesize language. The authors of this paper believe that this …
Tensorlog: A differentiable deductive database
WW Cohen - arxiv preprint arxiv:1605.06523, 2016 - arxiv.org
Large knowledge bases (KBs) are useful in many tasks, but it is unclear how to integrate this
sort of knowledge into" deep" gradient-based learning systems. To address this problem, we …
sort of knowledge into" deep" gradient-based learning systems. To address this problem, we …
[HTML][HTML] Explainable acceptance in probabilistic and incomplete abstract argumentation frameworks
Abstract Dung's Argumentation Framework (AF) has been extended in several directions,
including the possibility of representing uncertainty about the existence of arguments and …
including the possibility of representing uncertainty about the existence of arguments and …