Traditional KT models, such as Bayesian Knowledge Tracing (BKT) and early deep learning-based approaches like Deep Knowledge Tracing (DKT), have ... which lowers their efficiency in working with large ...

Abstract: This letter presents a novel knowledge-informed deep learning method for the fine-grained localization of forced oscillation ... Subsequently, a spatial-temporal graph attention (ST-GAT) ...

Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Chunming Wu and Shouling Ji, Multilevel Graph Matching Networks for Deep Graph Similarity Learning, IEEE Transactions on Neural Networks ...

While GraphRAG relies on the reasoning capabilities of LLMs to connect text-based data in a graph of knowledge, the third type of question—those requiring deep reasoning—need more than a GoK.

We evaluate these six prompting methods on the newly created Spider4SPARQL benchmark, as it is the most complex SPARQL-based Knowledge Graph Question Answering (KGQA) benchmark to date. Across the ...

A knowledge graph is a collection of relationships between entities defined using a standardized vocabulary. It structures data in a meaningful way, enabling greater efficiencies and accuracies in ...

Abstract: This letter introduces a graph learning approach leveraging prior knowledge of graph topology. For this, we integrate the concept of polytopic uncertainty into existing approaches that learn ...

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our ...