How do you go from learnable ontologies to world models? | LinkedIn
If an agent can learn an ontology from its own memory, and that ontology then shapes what it learns next, where does memory end and a world model begin? The usual story is that ontologies ground agents. But once agents are able to learn and enrich those ontologies from their own traces, you have a f
Ontology vs. Taxonomy vs. Knowledge Graph vs. Semantic Layer vs. Context Graph: A Field Guide | LinkedIn
"We need an ontology." "Isn't that just a knowledge graph?" Five terms, used almost interchangeably, that actually describe distinct layers of a knowledge architecture, from "how do we name and organise things" up to "what does an AI agent actually load into its context window.
Context graphs and truth in a changing world | LinkedIn
Quick introduction to context graphs We’ve all heard about context graphs: concepts linked by clearly defined relationships that represent some domain (like retail or agriculture). Context graphs’ purpose is to provide context to users—human or machine agents—so that they can take grounded, meaningf
6 Reasons to Consider Knowledge Graph as a Product: Monetizing your knowledge graph part 1
How to increase ROI, or create new revenue streams, from your graph data without selling the whole thing? That's a question many of us have been struggling w...
Research Assistant: AstraZeneca's Agentic System for R&D
We describe Research Assistant, an internal LLM-based system developed at AstraZeneca to help scientists and clinicians explore biomedical questions across a broad range of data sources. The...
What is an ontologist is a top question I hear from folks, from those looking to break into the knowledge graph or symbolic AI space, or those trying to hire...
descriptive access as a requirement for engineering semantic systems.
Context is still a nebulous concept that conveniently shape shifts. So as I work with clients, I am breaking down context according to types of context to help shape the conversations.
Borrowing from library and information science, I have started introducing the idea of descriptive access as a requirement for engineering semantic systems.
This is generally how an object is identified, discovered and retrieved and
Descriptive access is retrieval through the description of a resource rather than through its aboutness — access points drawn from descriptive metadata (creator, title, date, form/genre, extent, identifier) as opposed to subjects or classification.
It’s the “who/what/when” half of the catalog’s finding function; subject access is the “about” half. In the metadata-type taxonomy it’s the discovery-and-identification layer, distinct from administrative, structural, and preservation metadata.
The relationship to context is constitutive rather than incidental — a descriptive value only becomes an access point when a context makes it resolvable.
Authority control:
“Smith, John” is a string until an authority record supplies dates, associated entities, and scope. The disambiguating context is what converts description into access.
Provenance description:
Provenance and original order mean the description is a statement of context. This can be achieved by replacing the single hierarchical context with a graph, so a record becomes accessible through any of its contexts — agent, activity, mandate, place, date — instead of only through its individual elements.
Reference model:
Reference Information supplies the identifiers and access points; Context Information documents why the content exists and how it relates to other information. Both sit inside PDI, which is a fairly explicit institutional admission that access degrades without context.
SKOS and graph terms:
prefLabel gets you the node; inScheme, broader/narrower, scopeNote, and mappings are the context that fixes what the node means. Strip them and you have lexical matching without semantics. You see this failing with embedding-only retrieval, where “context” gets redefined as adjacent tokens rather than as documented relationships.
Context is what makes a descriptive value an access point instead of a string, and descriptive access is how context gets operationalized for retrieval.
Context is a dangerous catch all that's built for convenience, not results.
descriptive access as a requirement for engineering semantic systems.
From Triples to Truth: Why SHACL Will Not Save Ontological Engineering | LinkedIn
Note 1: A glossary is provided at the end of the article in order to decrypt the Ontology domain jargon ;) Note 2: This article is not intended to criticize SHACL as a technology or standard. SHACL plays an important and complementary role alongside OWL and Description Logics.
Two weeks ago I had the pleasure of attending the first GraphCon Seattle event. This inaugural practitioners conference, planned and produced by the Graph Geeks community, focused on how folks are solving real world problems in practical ways, using a range of technologies and approaches.
Ontology Development Suite A VS Code extension for creating ontologies (with owl:imports) and building TARQL-style CSV → RDF data graphs, with live diagnostics, a local SPARQL/SHACL/OWL2-RL checks engine, and DL-expressivity/OWL2-profile metrics
A Code extension for developing ontologies ... and a lot more besides - pwin/consolidated-ontology-quality-suite-webapp
Ontology Development Suite
A VS Code extension for creating ontologies (with owl:imports) and building
TARQL-style CSV → RDF data graphs, with live diagnostics, a local
SPARQL/SHACL/OWL2-RL checks engine, and DL-expressivity/OWL2-profile
metrics —
Graph Algorithms on Billions of Edges with Apache DataFusion
We replicate Sem Sinchenko's Apache DataFusion pipeline for running PageRank and Weakly Connected Components on billion-scale graphs using a laptop, and verify the results with gdotv.
Most "Ontologies" Don't Reason. Build Yours in the Right Order Anyway.
Most products marketed as ontologies do not reason. A six-rung maturity ladder for building an enterprise ontology and knowledge graph AI agents can trust.
What is an ontologist is a top question I hear from folks, from those looking to break into the knowledge graph or symbolic AI space, or those trying to hire these folks. Join me and some of the wonderful ontology folks from Bloomberg in the latest in this mini-series.
Sample job description from this chat: https://docs.google.com/document/d/1cBAUWZOSzwPpME_vpbv_VXrSd4mq9-j7DzlpOOMYzn8/edit?usp=sharing
Note: all opinions are my own as a data scientist and researcher in the field and are not representative of the tool/company being reviewed, nor of any other company.
Stay in touch:
LinkedIn: https://www.linkedin.com/in/ashleighnfaith/
Direct Message: isadatathing-at-gmail.com
