Optimism can carry a project only so far. Every transformation hits a reality check. The excitement of the launch fades, ...
SM-GNN prunes multi-view GNNs to pure propagation, cutting training time while outperforming prior MKGC accuracies on two ...
Knowledge Graph, Large Language Model, BERT, Knowledge Management, Small and Medium-Sized Enterprises, Accounting, Supply Chain Management Zheng, Y. (2026) Knowledge Graph Application in KM for ...
This week Australian startups saw $25.85 million raised across the AI security, fitness and deceased estate management spaces ...
The December jobs report shows a weak labor market, slow growth, wage gains above inflation, and a K-shaped expansion. Learn ...
Deep Learning Methods, GNN Model, Community Service Work, China and USA, Neural Networks Share and Cite: Liu, J.X. (2026) A Comparative Study of Community Service Work between China and the United ...
The 2026 ACS president will spend the society's 150th anniversary focusing on growing ACS's platform and engaging its members ...
Abstract: Knowledge graph is a form of data representation that uses graph structure to model the connections between things. The intention of knowledge graph is to optimize the results returned by ...
For years, SEOs optimized pages around keywords. But Google now understands meaning through entities and how they relate to one another: people, products, concepts, and their topical connections ...
Using structured output with schema does not work properly for the knowledge retention metric and the schema defined and passed in for it. Examples of truncated model responses in the case where ...
What if you could transform overwhelming, disconnected datasets into a living, breathing map of relationships, one that not only organizes your data but also reveals insights you didn’t even know you ...
When we have a dynamo_output_graph from tlparse this can be a helpful reproducer for problems in AOTAutograd. However, Dynamo cannot reliably retrace the output graphs it generates. The biggest ...