They require a knowledge graph. How does the journey to a knowledge graph start with unstructured data—such as text, images, and other media? The evolution of web search engines offers an ...

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 ...

A Heterogeneous Graph Transformer (HGT)-based model for protein function prediction using biological knowledge graphs and protein language models ...

@article{gao2023leveraging, title={Leveraging a medical knowledge graph into large language models for diagnosis prediction ... python triattn_trainer.py --lr 1e-5 --max_k 2 --top_n 8 --epoch 10 ...

Abstract: Graph Neural Networks (GNNs) show great power in Knowledge Graph Completion (KGC) as they can handle nonEuclidean graph structures and do not depend on the specific shape or topology of the ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Diffbot, a small Silicon Valley company best known for maintaining ...

Tana, the all-in-one workspace that naturally integrates AI into everyday work, announced a $14 million Series A round led by ...

Besides, most of the current techniques focus solely on local information in the KG. However, the learning process can utilise valuable global information in the entire graph. In this paper, we ...

A study titled Do LLMs Have Distinct and Consistent Personality?, detailed in a paper from Yonsei University and Seoul ...

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 ...