In the GraphRAG pipeline, documents are linked together (graph of knowledge) through entities and relations among them, followed by community detection, which results in a summary for each ...

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

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 ...

In this study, we present a solution by introducing Common Metadata Ontology (CMO) which is used to construct an extensive AI Pipeline Metadata Knowledge Graph (AIMKG) consisting of 1.6 million ...

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 ...

Finally, we conduct experiments across three different prompting frameworks without using the ontology of the knowledge graph. The prompting frameworks we use are (1) Baseline, (2) ETT (both the ...

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 ...

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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 ...

Persistent Systems, a global digital engineering firm, today announced the release of Pi-OmniKG, an AI-powered knowledge graph solution developed in collaboration with Google Cloud. The new tool ...