However, after a certain point of making the context larger you get diminishing returns, and contexts generally don't solve the problem of dynamic data, which is something that RAG is much more suited ...

Part II builds on the terms and concepts introduced in Part I and explores the difference in the meaning of some key terms used in both Property Graphs and Knowledge Graphs. Key terms used in these ...

For example, a knowledge graph could power a search engine that can understand the meaning and context of user queries, and provide relevant and diverse results. Knowledge graphs also face several ...

where understanding the difference in meaning between ‘Apple the company’ and ‘apple the fruit’ depends on the conversational context provided by the knowledge graph. The Future of ...

Abstract: This work proposes an approach for emotion recognition in conversation that leverages context modeling, knowledge enrichment, and multimodal (text and audio) learning based on a graph ...

have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency between an entity and its local graph context in KG (i.e., the ...

Also known as knowledge bases, knowledge graphs are collections of interlinked descriptions of entities and objects, which, thanks to their semantic metadata, give them context and connect them with ...

Knowledge graphs ground AI and large language models (LLMs) in context to avoid inaccuracies or hallucinations, increasing the precision of its output. Knowledge graphs: Simplify access to complex ...

Building on the terms and concepts introduced in Part I of this white paper, Part II digs deeper into the difference in the meaning of some key terms used in both Property Graphs and Knowledge Graphs, ...