#knowledge-graphs

#knowledge-graphs

Dropbox engineers have detailed how the organization was able to build the context engine behind Dropbox Dash, demonstrating a shift towards index-based retrieval, knowledge graph-derived context, and continuous evaluation to support enterprise AI knowledge retrieval at scale. The design points to a broader pattern emerging across enterprise assistants, whereby teams are deliberately constraining their live tool usage and instead relying more heavily on pre-processed, permission-aware context to speed latency, improve quality and ease token pressure.

Large language models (LLMs) base their predictions on training data and cannot respond effectively to queries about other data. The AI industry has dealt with that limitation through a process called retrieval-augmented generation (RAG), which gives LLMs access to external datasets. Google's AI Overviews in Search, for example, use RAG to provide the underlying Gemini model with current, though not necessarily accurate, web data.

Upper is based on W3C standards such as RDF for conceptual graph representation and SHACL for validation, and it enables the principle of "model once, represent everywhere" across the data ecosystem.Upper organizes concepts through keyed entities, their attributes, and their relationships across domain boundaries. The modeling grammar and validation structure are designed to maintain consistency as definitions evolve. Keyed concepts can be extended monotonically, allowing new attributes or relationships without modifying existing definitions allowing domains to expand over time without breaking existing models.

The answer lies not in what AI can produce, but in what humans can decide. The real transformation is not about replacing expertise, but about separating the visible outputs of design and strategy from the judgement that gives those outputs meaning. The part of professional work being automated is not the expertise itself. It is the formatting. The model doesn't replace human judgement; it replicates its surface patterns.

The answer lies not in what AI can produce, but in what humans can decide. The real transformation is not about replacing expertise, but about separating the visible outputs of design and strategy from the judgement that gives those outputs meaning. The part of professional work being automated is not the expertise itself. It is the formatting. The model doesn't replace human judgement; it replicates its surface patterns.

Knowledge graphs, introduced by Google in 2012, represent structured data with connections, highlighting relationships between entities such as Da Vinci and the Mona Lisa.

Large language models are mistaken for reasoning tools; they merely refine text prediction without genuine understanding, underscoring the need for knowledge graphs and RAG.

Our priority is establishing a robust mechanism for identifying personally identifiable information (PII) by leveraging advanced techniques that integrate Named Entity Recognition within diverse contexts.

In our research, we successfully designed a functional material KG by employing fine-tuned large language models (LLMs), assuring traceability throughout the information process.