Semantics, Platforms, and the Illusion of Control: Why Open Standards Alone Will Not Save Enterprise Meaning | LinkedIn
Recent discussions around platform consolidation, real-time data pipelines, and AI architecture increasingly emphasize the strategic role of formal semantics. The argument is compelling: as cloud providers, streaming platforms, and AI stacks become vertically integrated, enterprises risk losing not
2026: Moving Beyond "Data-First" to a "Knowledge-First" Architecture
The generative AI gold rush of previous years has matured into a pragmatic operational reality in 2026. The industry's focus, once fixed on model scale and compute power (a "data-first" race), has shifted.
Last week, I attended the Masterclass day of Connected Data London 2025. It was a good excuse to catch up with Rahel Anne Bailie and Helen Lippell, meet Paul Appleby and Ravinder Singh face to face, and meet more of the Kinetic Council, Larry Swanson and Cruce Saunders.
As a non-technical practitioner, I was worried that the conference would be beyond my skillset as a linguist. There were certainly large portions that were, but I gained new insight from every session I attended that helped close the gap and gave me new perspective on how semantic solutions can create tangible value.
- Up to now, I've only used RDF and SPARQL so it was good to see an alternative approach with Labelled Property Graphs and Cypher – similar in principle but with different purposes. Thanks to Martin O'Hanlon from Neo4j for providing a useful hands-on intro for an LPG newbie in Understand relationships in your data with Knowledge Graphs. I'll be revisiting the Neo4j Graph Academy to top up what I learned in the Masterclass.
- One of the most eye-opening Masterclasses was Text, Images, and Audio for Multimodal Intelligence in GraphRAG presented by Amy Hodler and David Hughes. It was a fascinating insight into how graphs could also be used to support GraphRAG for non-textual information. I'm intrigued to better understand how non-textual information is actually captured in a semantic layer but this really adds another dimension to the potential of Knowledge Graphs for DAMs and content personalisation with the recent uplift in video content.
- A big part of my personal learning curve was understanding which standard schema to adopt to enable interoperability for different use cases (schema.org? FOAF? Dublin Core?). Alaaeddin Alweish and Cruce Saunders shared a new, more efficient method of unifying these separate schemas in Decoupling Schemas from Systems - A Unified Modeling Approach for Data Products and Semantic Interoperability.
- My final live Masterclass was a personal success given the slightly higher level of technical knowledge required. After a couple of failed attempts (due only to my own technical limitations!), I was able to complete the RDFox Ultimate Guide to Semantic Reasoning workshop thanks to a well-crafted and practical session by Tom Vout from Oxford Semantic Technologies.
It wasn't easy to decide which sessions to attend live on the day so I will be making good use of the recordings. My main regret is that, based on other write-ups, I think I may have missed out on some gems on day two. Let that be a lesson to me...
#ConnectedData #CDL25 #Semantics #ControlledVocabulary #Taxonomy #Ontology #KnowledgeGraphs #OneDayWonder
Stop Relying on Vector Search Alone: Build a Hybrid RAG System with Knowledge Graphs and Local LLMs
Hybrid RAG combines vector search with knowledge graphs to fix retrieval blind spots, boost support accuracy, and cut investigation time in enterprise systems.
Scalable Feature Learning on Huge Knowledge Graphs for Downstream...
Many machine learning tasks can benefit from external knowledge. Large knowledge graphs store such knowledge, and embedding methods can be used to distill it into ready-to-use vector...
Beyond Palantir’s Ontology: The Paradigm, the Platform, and the Path to Open Semantics
Beyond Palantir’s Ontology: The Paradigm, the Platform, and the Path to Open Semantics From proprietary semantics to open metadata — understanding Ontology as a paradigm, not a product. When I …
Stop Right There: Conducting Fraud Detection with Aerospike Graph
Discover how to detect fraudulent financial transactions with speed and accuracy using G.V() and Aerospike Graph Database in this developer walkthrough.
HyperbolicRAG: Curved Spaces, Better Answers — AI Innovations and Insights 94
Traditional RAG systems are pretty familiar by now: retrieve a few relevant passages using dense retrieval, then feed them to a language model for answering the question.
Demystifying ontologies: What ontologies really do in knowledge graphs | LinkedIn
If you talk to people working with data, AI, or enterprise architecture and ask, “What is an ontology?”, you’re likely to get very different answers. For some, ontology is a kind of clever data schema.
Yesterday, I came across LinkedIn posts by 𓋹 Athanassios Hatzis, Ph.D. ("What Do You Use to Visualize RDF/OWL Ontologies") and Connected Data ("How do you visualize, edit and create Ontologies?") about creating and sharing ontology models, making this post feel timely.
In the past, I've consistently struggled to find effective ways to share developed ontology models with business and tech teams. Business teams want to ensure semantic models accurately represent domain concepts. Meanwhile, tech teams are interested in understanding how data attributes, stored in specific database columns, are represented in the ontology model. Our only choices were either 1) to share the ontology file with the business team—who generally couldn't understand a turtle (.ttl) file format or understand what the various axioms meant—or 2) to manually create visuals in PowerPoint, or use a tool to depict node/edge relationships. Since ontologies continually evolve, these manually created visuals quickly became outdated.
For a while, we used documentation generated by Widoco. The documentation is handy for browsing lists of classes, properties, and instances (if there are any), and reading their definitions. However, it doesn't illustrate how classes are connected within a semantic model, nor does it indicate which relationships in an ontology model link Class A to Class B. #WebVOWL do offer a visual however we didn't find it very intuitive.
We recently built OntoView - A semantic model viewer; by leveraging SHACL Shapes, our internally developed Metadata Ontology Model, and #Ontodia capabilities in #Metaphactory (metaphacts GmbH). We can now share data models at various levels: for a single class, for a specific data source, for an ontology, or even for the entire Knowledge Graph. The models can be visualized either in Graph or Table View, which is particularly useful when there are many overlapping nodes and relationships. Users can select classes from the detailed models and create custom navigation paths. These visuals can be exported and easily shared with team members. Each ontology class node includes an info icon, allowing users to view the class IRI, definition, and other details from the ontology. Our metadata model demonstrates how relational table columns are represented in the ontology and informs tech teams about which triple patterns to implement in their ETL pipelines.
Questions that can be answered from the OntoView:
- How is data modeled from a specific data source?
- Which classes are defined under a given data source or dataset?
- What is the data modeling approach for a particular dataset product?
- How is a specific RDB column value reflected in the semantic model?
- Can I filter, export, and share specific linked data model paths?
Thanks to the support from the Metaphacts team - Ademar Crotti Junior & Kai Preuss
#RDF, #OWL, #Ontology, #KnowledgeGraph, #Semantics
