Everyone is talking about “context graphs” and AI, but we need to be clear about what they are and what they aren’t. Eilon Reshef breaks it down in his new blog.

This illustrates the boundary of the Semantic Web stack.

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How a fifty-year-old idea from biology is reshaping the way we think about knowledge graphs

This is update from previous blog (version 1): GraphRAG and PostgreSQL integration in docker with Cypher query and AI agents | Microsoft Community...

  Why should I care?   ⚡️In under 15 minutes, you’ll have a Cypher-powered, semantically rich knowledge graph you can query...

A Comparative Architecture Analysis

Context graphs are a hot new conversation these days. And yet I feel like I’ve seen this story before.

Retrieval-Augmented Generation (RAG) systems typically treat documents as flat text, ignoring the structured metadata and linked relationships that knowledge graphs provide. In this paper, we...

🧩 What Are Modalities in Knowledge Graphs — And Why They Matter

🧠 This one's flying under the radar and it shouldn't be.

Kinetica-Graph Explorer is now available.

Competency Questions for System Architectures

The shift from "finding data" to "reasoning and understanding context" is the driver for a more robust context layer that needs to provide the operational nuances that are typically locked within systems, teams, and organizational silos.

There are essentially five potential vectors toward shared understanding of semantics: * #ad_hoc. This is the JSON model; send the data without schemas, let the receiver figure it out. Usually involves human-in-the-loop and much swearing when things don't work as agreed upon. * #contractual . You and I agree a priori on a common schema or ontology. This can be extended to a certain extent to a network of users sharing that same schema. This is the semantic web model, and it works reasonably well, but also means that communicating nuance or variation is hard, especially the larger the network becomes. * #declarative . When I transmit my data, the ontology is part of that data. The recipient of the data then has instructions concerning how to work with that data. This increases the overall size of data transmissions, but has the advantage of packaging data and schema together so that the recipient does not necessarily need point-to-point synchronous communication. This is the data package approach, and works very well with SHACL. Its principal disadvantage is that while schematic information is usually fairly condensed, taxonomic information can be extensive. * #negotiative. A system receives a data packet and attempts to ascertain its schematic rules contextually, in terms of a known internal schema resolution (or a derived schema from some other data context). Negotiative schemas have the advantage in that they don't require complex parsers and transformation layers (though you can argue that an LLM or similar cognitive system is hardly a trivial intermediation layer) but they suffer from ambiguity, lack of provenance, and don't deal with large, complex data structures well. * #cached_transformative. A variation of #negotiative, the participants in a data exchange each submit target schemas, which are then used by a transformer (perhaps an LLM) to negotiate some form of point-to-point transformation from one schema to the next, with each schema identified by a unique IRI. The transformation can work with test data to ascertain degrees of lossiness (some lossiness is expected), then, when this drops below an acceptable threshold (or can be filtered out via validation), the transformations are cached. Data so transmitted then includes the source and target schema IRIs to retrieve the relevant transformation. Most data systems use some or even all of these approaches. JSON is mostly #ad_hoc, JSON-LD is somewhat #declarative. Most RDF is #contractual - you work against existing schemas. #negotiative and #cached_transformative are both comparatively recent, and are dependent upon intelligent agents; the primary difference is that #negotiative transformations are repeated for every transaction, #cached_transformative are only regenerated when a schematic change takes place. Each has its place.

The origin of most data in the world comes from closed-world systems. Forms, logging systems, and sensors all generate data in predefined structures where the schema is known in advance. Yet when peop

Google Gemini has totally crushed all other models according to Knwler's benchmark.

Table of Contents

Most Graph RAG discussions are still around 𝘸𝘩𝘪𝘤𝘩 𝘨𝘳𝘢𝘱𝘩 𝘥𝘢𝘵𝘢𝘣𝘴𝘦 𝘵𝘰 𝘶𝘴𝘦? Serious architecture asks: Which graph structure fits the retrieval job? That is the Graph-Type Fit Pattern. The mistake is thinking “graph” is a feature.

Gartner D&A is always a mix of signal and noise.

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