Re: Semantic Hypermedia Addressing

Hi Adam,

On 10/12/25 3:00 PM, Adam Sobieski wrote:
> Sabastian Samaruga,
> All,
>
> Hello. Being able to reference hypermedia resources within webpages, 
> a.k.a., "semantic hypermedia addressing", would be useful and enable 
> some approaches for solving "deepfakes" and related challenges.
>
> With (decentralized) annotation capabilities, e.g., via typed 
> hyperlinks on annotators' websites or social-media posts, people and 
> organizations could annotate specific hypermedia resources as being 
> "deepfakes" or, instead, as being "vetted" or "blessed". There may be, 
> for these scenarios, more types of annotation links than two Boolean 
> ratings, thumbs-up and thumbs-down. Also, these kinds of annotations 
> could be accompanied by justification or argumentation.
>
> In addition to performing logical inferencing and reasoning upon 
> decentralized and, importantly, paraconsistent collections of such 
> annotation links, there is the matter of computing floating-point 
> numerical attributes for annotated multimedia resources. That is, from 
> a set of annotations from a set of annotators who each have annotation 
> histories, these annotators potentially disagreeing with one another, 
> calculate a floating-point number between 0.0 and 1.0 for the 
> probability that an annotated multimedia resource is, for example, a 
> "deepfake".
>
> Here are two ideas towards delivering the capabilities to reference 
> and to annotate hypermedia resources in webpages:
>
> 1) The annotating party or software tool could use selectors from the 
> Web Annotation Data Model [1].
>
> 2) The content-providing party could use metadata to indicate 
> canonical URIs/URLs for a (multi-source) multimedia resources. This 
> might resemble:
>
> <video canonical="https://www.socialmedia.site/media/video/12345678.mp4">
>   ...
> </video>
>
> or:
>
> <video>
>   <link rel="canonical" 
> link="https://www.socialmedia.site/media/video/12345678.mp4" />
>   ...
> </video>
>
> Note that, while the example, above, uses a generic social-media 
> website URL, social-media services could provide their end-users — 
> individuals and organizations — with menu options on hypermedia 
> resources for these purposes: to "flag" or to "bless" specific 
> multimedia resources.
>
> Proponents of automation, in these regards, have expressed that rapid 
> responses are critical for these annotation scenarios as viral content 
> could spread around the world faster than human content-checkers might 
> be able to create (decentralized) annotations. Aware of these 
> considerations, AI agents and other advanced software tools could use 
> these same content-referencing and content-annotation techniques under 
> discussion.
>
> I'm recently brainstorming about approaches including some inspired by 
> the Web Annotation Data Model [1] and Pingback [2] which would involve 
> the capability to send annotation event data to multiple recipients, 
> destinations, and/or third-party services in addition to the 
> content-providing websites.
>
>
> Best regards,
> Adam Sobieski
>
> P.S.: As interesting, there are also to consider capabilities for 
> end-users and/or AI agents to annotate annotation statements; we might 
> call this: "annotation-*" or "annotation-star". These concepts seem to 
> have been broached in your second paragraph with: "reifying links"?
>
> [1] https://www.w3.org/TR/annotation-model/ 
> <https://www.w3.org/TR/annotation-model/>
>
> [2] https://hixie.ch/specs/pingback/pingback 
> <https://hixie.ch/specs/pingback/pingback>


These capabilities are all achievable now. You can build AI-based Agents 
that perform such reasoning and inference-driven tasks.

I’ll leave you (and any other interested party) with a simple demo of an 
AI Agent — one that leverages LLMs for natural language processing and 
is loosely coupled with a knowledge graph. This design avoids subtle 
issues like an LLM (e.g., Google Gemini) insisting on using terms from 
https://schema.org even when it was explicitly instructed to use 
http://schema.org when generating knowledge graphs.

Demo Links:

[1] 
https://linkeddata.uriburner.com/chat/?chat_id=s-YjsaUP3Ur6gHbWn9Me4wDzsWvg8vbQyP5auMteK359k#asi-46736 
— static page you can scroll through to see how it answered the question.

[2] 
https://linkeddata.uriburner.com/chat/?chat_id=s-YjsaUP3Ur6gHbWn9Me4wDzsWvg8vbQyP5auMteK359k&t=120 
— animated view if you just want to sit back and watch.

The Agent used here was created using natural language via an Agents.md 
Markdown document that defines its planning logic and service/tool 
bindings. Tooling in this case includes Virtuoso Stored Procedures, 
external OpenAPI services, and MCP (Model Context Protocol) servers.

The Agent itself is usable from any client environment that supports MCP 
or OpenAPI—all loosely coupled.

Related

[1] Github Repo -- https://github.com/OpenLinkSoftware/Assistants

[2] Agents.md example (note how reasoning and inference is integrated) 
-- 
https://github.com/OpenLinkSoftware/Assistants/blob/main/basic-agent-in-agents-dot-md-form-template.md

-- 
Regards,

Kingsley Idehen 
Founder & CEO
OpenLink Software
Home Page:http://www.openlinksw.com
Community Support:https://community.openlinksw.com

Social Media:
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