Semantics bridges gaps in the AI era
Exploring three examples of semantics in tech and humanity.
“You hear what I say, but do you understand what I mean?” A question of nuance, riddled with doubt. We don’t aim to be vague or of no help. Rather, we want to be useful, understood, and today expect that same understanding from the systems and services programmed to help us.
Let’s take a moment to discuss, infer, relate, associate, and guess – to understand – the value of semantics in technology. Semantics is a bridge feature; one you use nearly every day whether you know it or not. And like most bridges, it takes time to build and cross them. Now, YOU might say semantics are gap fillers, the meaning of and the reasons for bridges. To which I say, great! It IS semantic. We’re talking semantics. We mean the same thing. And, imho, it is semantics that makes people and technology go ‘round and ‘round – and grow. At least that is what I’ll infer throughout this article. 😉
se·man·tics [sɪˈmantɪks] | The meaning of a word, phrase, or text. The study of meaning in languages.
Semantics can be one of those tricky words, like “quantum.” Use it once or twice and it feels uncertain and unknown. Put it in front of another word and it sounds academic, smart, but out of reach. But it’s important. Semantics in language help convey meaning and make communicating interesting. Semantics in technology is a magical middle layer that takes raw information and turns it into knowledge – aka, something relatable and clear; a bridge to understanding.
So, let’s explore it from a few angles and make sense of it together. In this article, we’ll explore three semantic steppingstones:
Human Communication – How we naturally use context and meaning to understand each other.
Microsoft Interactive Media Manager (IMM) – An early (2007) SharePoint-based media solution that leveraged a third-party semantics engine to manage video content metadata through its lifecycle.
Semantic Index for Microsoft 365 Copilot – How it connects unstructured data to a structured information architecture in modern AI.
Along the way, consider how semantics fills the gaps between unstructured data and more mature information architecture.
Human Semantics: How We Understand Each Other
Semantics are fundamentally human. Every time you hold a conversation or read an article (like this one!), you’re relying on semantic knowledge to make sense of language (and my writing style). You don’t stop to assess each word literally; you interpret meaning based on context and nuance of what you know and understand.
Consider this: If I say, “I hope my manager goes to bat for me,” you understand I mean I hope my manager defends or supports me, not that they literally grabbed a bat like mighty Casey. We don’t pause and consider these semantic leaps – like a clever turn of phrase, or joke, or bad pun (I know this area well) - we just “get it.” And maintain our goal of not striking out! 😊
And hey, it’s what we’re now trying to teach these machines – to bridge the gap between raw input and meaningful output. We want our software to do what we do effortlessly: Understand. And I think it’s the underlying message of the recent Microsoft term: Work IQ (explainer blog and video); new branding for some of the previous technology – like the semantic index covered below, the original platform for Office Delve, and the end user side of Microsoft Graph; we’ll table that as more details about it unfold over the coming months.
Instead, let’s start in the way back machine and review early progress I got to be a part of: The Interactive Media Manager.
A Blast from the Past: Semantic Metadata in Interactive Media Manager (IMM)
Long ago, in my work past, I worked on a solution called the Interactive Media Manager (IMM), a Microsoft experiment with semantics designed for media customers. Essentially it was a digital asset management (DAM) system built on SharePoint 2007. It was an exciting project that brought modern media workflows to SharePoint AND introduced a semantic approach to search and inference to managing video and audio content. And, personally, it was my first break as a product manager.
The IMM solution included an Ontology Editor built on a third-party component that let administrators define how their metadata got structured: what are the entities and how do they relate). This was cutting-edge for 2007 and made for a heck of a demo at NAB 2008: Instead of just tagging videos with flat keywords, we showed creating a meaningful graph of metadata through the lifecycle of a large media file. It facilitated smarter search and content reuse – linking data (videos, in this case) through meaning (who’s in it, what it’s about, where it’s set) so you could retrieve and recombine content in new, smart ways.
And then, years later, along came the Semantic Index…
Semantic Index for Microsoft 365 Copilot: A Map for Your Data
The design of Copilot’s semantic index combines general search and Microsoft Graph to generate context and relationships within your data and documents.
“Instead of data sitting in silos or requiring manual curation, the Semantic Index enables a layer of order and meaning on top of it.” If you ask Copilot, “Find that email where my colleague praised the new design,” a traditional search might struggle if you didn’t use the exact words “praise” or “design” in the email. But the Semantic Index will include conceptually related terms (maybe “excited,” “impressed,” etc.) to retrieve that email without you having to guess the exact wording.” (Microsoft Learn)
The Semantic Index is essentially a map of your data that understands content by meaning, not just by keywords. It was introduced to bridge the gap between unstructured data (documents, emails, sites, chats, etc.) and the structured answers or insights people need.
With Copilot, semantics fills the gap between the unstructured ways we often store information and the structured way we retrieve and comprehend it. The Semantic Index provides a smart mapping of all the data and content in your organization – “enabling Microsoft 365 Copilot to deliver personalized, relevant, and actionable responses.” It gives you more-natural wiggle room to find what you’re looking for and create what you need.
💡 Learn more: “How Semantic Index for Copilot works in Microsoft 365” (Microsoft Mechanics) and “Semantic indexing for Microsoft 365 Copilot” (Microsoft Learn).
NOTE: SharePoint plays a crucial role in grounding Copilot. It is one of the primary content services that can help better organize your documents and knowledge and all the related metadata — all while establishing and maintaining good governance and permissions management. If used right, it’s THE grounding source for mature information architecture. And if you achieve that, and you can, Copilot’s reliability goes way up, too.
In the end…
It’s all about making sense of your data AND having your data make sense to others. Content and communication are more than just text – it’s context – a shared understanding; we wish to understand and not be misunderstood. I believe at least it’s important to understand some of the magic in the middle layer. Or like me: Simply trying to understand what makes the tech tick today. In Microsoft 365, the noise to good-signals ratio is the difference between Copilot being useful and Copilot being a useful idiot.
Or is it all just semantics?
Cheers, your peer, Mark “Cementing the Semantics” Kashman 🧔🏻♂️