GraphRAG explains why AI is shifting from isolated text to connected knowledge, and what that means for AI search optimization. Making your brand machine-readable and increasing its chances of being ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?

A new AWS Forward Deployed Engineering organization will embed thousands of experts with customers to co-develop and deploy ...

Part of the SD Times 100 2026 series. See the full SD Times 100 2026 list for every category and honoree. Every conversation ...

Sales, a function that obviously runs on language, has been among the least changed by the technology built on language.

Abstract: Deep learning models present impressive capability for automatic feature extraction, where common features-based aggregation have demonstrated valuable potential in improving the model ...

Retrieval-augmented generation (RAG) has emerged as a pivotal framework in AI, significantly enhancing the accuracy and relevance of responses generated by large language models (LLMs) leveraging ...

Abstract: Knowledge base completion (KBC) aims to predict missing information in a knowledge base. Most existing embedding-based KBC models assume that all test entities are available at training time ...

This is the official code release of the following paper: Xiangrong Zhu, Guangyao Li, Wei Hu. Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning, WWW 2023. Federated Learning ...

Source code and dataset for EMNLP 2018 paper: HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. Overview of HyTE (proposed method). a temporally aware KG embedding method which ...