how Knowledge Graphs could be used to provide context
📚 Definition number 0️⃣0️⃣0️⃣0️⃣0️⃣1️⃣0️⃣1️⃣
🌊 It is pretty easy to see how context is making really big waves recently. Not long time ago, there were announcements about Model Context Protocol (MCP). There is even saying that Prompt Engineers changed their job titles to Context Engineers. 😅
🔔 In my recent few posts about definitions I tried to show how Knowledge Graphs could be used to provide context as they are built with two types of real definitions expressed in a formalised language. Next, I explained how objective and linguistic nominal definitions in Natural Language can be linked to the models of external things encoded in the formal way to increase human-machine semantic interoperability.
🔄 Quick recap: in KGs objective definitions define objects external to the language and linguistic definitions relate words to other expressions of that language. This is regardless of the nature of the language under consideration - formalised or natural. Objective definitions are real definitions when they uniquely specify certain objects via their characteristics - this is also regardless of the language nature. Not all objective definitions are real definitions and none of linguistic definitions are real definitions.
💡 Classical objective definitions are an example of clear definitions. Another type of real definitions that could be encountered either in formalised or Natural Language are contextual definitions. An example of such definition is ‘Logarithm of a number A with base B is such a number C that B to the power of C is equal to A’. Obviously this familiar mathematical definition could be expressed in formalised language as well. This makes Knowledge Graphs capable of providing context via contextual definitions apart from other types of definitions covered so far.
🤷🏼♂️ At the same time another question appears. How is it possible to keep track of all those different types of definitions and always be able to know which one is which for a given modelled object? In my previous posts, I have shown how definitions could be linked via ‘rdfs:comment’ and ‘skos:definition’. However, that is still pretty generic. It is still possible to extend base vocabulary provided by SKOS and add custom properties for this purpose. Quick reminder: property in KG corresponds to relation between two other objects. Properties allowing to add multiple types of definitions in Natural Language can be created as instances of owl:AnnotationProperty as follows:
namespace:contextualDefiniton a owl:AnnotationProperty .
After that this new annotation property instance could be used in the same way as more generic linking definitions to objects in KGs. 🤓
🏄♂️ The above shows that getting context right way can be tricky endeavour indeed. In my next posts, I will try to describe some other types of definitions, so they can also be added to KGs. If you'd like to level up your KG in this way, please stay tuned. 🎸😎🤙🏻
#ai #knowledgegraphs #definitions
how Knowledge Graphs could be used to provide context
Why Ontologies Matter and Why They are Hard to Develop | Jun 25, 2025
This blog post explores why building ontologies is essential yet notoriously difficult, and proposes a faster, more adaptive approach that bridges technical and domain expertise
Confession: until last week, I thought graphs were new
Confession: until last week, I thought graphs were new.
I shared what I thought was a fresh idea: that enterprise structured data should be modeled as a graph to make it digestible for today’s AI with its short context windows and text-based architecture.
My post attracted graph leaders with roots in the Semantic Web. I learned that ontology was the big idea when the Semantic Web launched in 2001, and fell out of fashion by 2008. Then Google brought it back in 2012 —rebranded as the “knowledge graph” - and graphs became a mainstay in SEO.
We’re living through the third wave of graphs, now driven by the need to feed data to AI agents. Graphs are indeed not new.
But there’s no way I - or most enterprise data leaders of my generation - would have known that. I started my data career in 2013 - peak love for data lakes and disregard for schemas. I haven't met a single ontologist until 3 months ago (hi Madonnalisa C.!). And I deal with tables in the enterprise domain, not documents in public domain. These are two different worlds.
Or are they?..
This 1999 quote from Tim Berners-Lee, the father of the Semantic Web hit me:
“I have a dream for the Web [in which computers] become capable of analyzing all the data... When it [emerges], the day-to-day mechanisms of trade, bureaucracy, and our daily lives will be handled by machines talking to machines... The ‘intelligent agents’... will finally materialize.”
We don't talk about this enough - but we are all one:
➡️ Semantic Web folks
➡️ Enterprise data teams
➡️ SEO and content teams
➡️ data providers like Scale AI and Surge AI
In the grand scheme of things, we are all just feeding data into computers hoping to realize Tim’s dream.
That’s when my initial shame turned into wonder.
What if we all reimagined our jobs by learning from each other?
What if enterprise data teams:
▶️ Prioritized algorithmic discoverability of their data assets, like SEOs do?
▶️ Pursued missing data that improves AI outcomes, like Scale AI does?
▶️ Took ownership of all data—not just the tables?
Would we be the generation that finally realizes the dream?
What a time to be alive. | 10 comments on LinkedIn
Confession: until last week, I thought graphs were new
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There’s a lot of buzz about #semanticlayers on LinkedIn these days. So what is a semantic layer?
According to AtScale, “The semantic layer is a metadata and abstraction layer built on the source data (eg.. data warehouse, data lake, or data mart). The metadata is defined so that the data model gets enriched and becomes simple enough for the business user to understand.”
It’s a metadata layer.
Which can be taken a step further. A metadata layer is best implemented using metadata standards that support interoperability and extensibility.
There are open standards such as Dublin Core Metadata Initiative and there are home-grown standards, established within organizations and domains.
If you want to design and build semantic layers, build from metadata standards or build a metadata standard, according to #FAIR principles (findable, accessible, interoperable, reusable).
Some interesting and BRILLIANT ✨folks to check out in the metadata domain space:
Ole Olesen-Bagneux (O2)’s (check out his upcoming book about the #metagrid)
Lisa N. Cao
Robin Fay
Jenna Jordan
Larry Swanson
Resources in comments 👇👇👇 | 29 comments on LinkedIn
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Siren Adopts ISO-Standard GQL to Power the Next Generation of Graph Intelligence - SIREN
Uniquely Pioneering Graph Analytics Combined With Deep Search Galway, Ireland – 24th June, 2025 — Siren, the all-in-one investigation company, today announced its adoption of Graph Query Language (GQL), the world’s first ISO-standard query language for graphs, made public in 2024. With this move, Siren becomes the first investigative platform to offer seamless, standards-based graph … Continue reading "Siren Adopts ISO-Standard GQL to Power the Next Generation of Graph Intelligence"
The Question That Changes Everything: "But This Doesn't Look Like an Ontology" | LinkedIn
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Building Truly Autonomous AI: A Semantic Architecture Approach | LinkedIn
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