What it takes to operationalize entities and schema across large organizations, without breaking governance or increasing ...

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...

AI has transformed how we interact with technology, moving from traditional graphical interfaces to language-based, collaborative systems. To measure these new human-AI interactions, we developed ...

Abstract: Knowledge distillation (KD) has become a cornerstone for compressing deep neural networks, allowing a smaller student model to learn from a larger teacher model. In the context of semantic ...

Scalable, high performance knowledge graph memory system with semantic retrieval, contextual recall, and temporal awareness. Provides any LLM client that supports the model context protocol (e.g., ...

An agentic system that reads research papers about Gaussian Splatting and constructs a semantic knowledge graph stored in PostgreSQL. The system goes beyond simple citations to extract deep ...

Multi-modal Knowledge Graphs (MMKGs) have been widely applied across various domains for knowledge representation. However, the existing MMKGs are significantly fewer than required, and their ...