Hi, I have a bunch of text, that I need to analyse to build a tree map of concepts. The concepts need to be sorted from higher lvls of abstraction to more detailed. For example, the program needs to understand that the idea "I want to create beauty outside of factory-based presets" mentioned in the text is the highest lvl and includes ideas like "I was inspired by Kabuki theater" in itself.

Note: Ideas themselves sometimes aren't presented in the source material bluntly, i.e. the idea of "I want to create beauty outside of factory-based presets" can be mentioned in fragments only that the program also needs to understand and put together.

Are there any available programs or basic frameworks to approach this?

My BA was in game design; I can code at a beginner to intermediate level but have zero interest in being a corporate software engineer. Basically what I’m looking for is a graduate program (or second undergraduate program) focused on symbolic or semiotic representations of data, so that I can create culturally relevant knowledge graphs or maps to be incorporated into large language models.

Examples of this, that I’ve noticed language models get laughably wrong:

• the feeling and/or smell of “back to school fall” and why that’s different from “harvest season fall” and why both of those are different from “Halloween fall” (or, explaining to a model why a list of fall vibes movies should not include Saw; in the same way that when I was working in a children’s library, I might explain to a clueless parent that Princess Mononoke is not for children even though it contains the keywords “princess” and “animated”)
• that the phrase “I love fall” means something completely different coming from a deer hunter, someone who likes PSL coffee and cozy sweaters, a college professor, a leaf peeper, and a horror movie enthusiast
• categorizing different products people can buy based on vibe (understanding that the person buying a Prius is probably not the same person buying Black Rifle Coffee)

It’s about the understanding that there are a variety of things that humans just inherently know, but that language models and software algorithms do not.

What I actually want to do is somewhere in the adjacent realm of:

• building these semiotic maps either as part of LLMs work, or as Loras for existing LLMs (like a plugin that gives an existing LLM semiotic product or culture knowledge); less interested in building the LLMs themselves. I’ve made simple Loras for stable diffusion before but I’d want formal training to actually do what I’m trying to do.
• also interested in red teaming the LLM to ensure that it’s using the data correctly
• also interested in metadata and metadata maps
• also interested in a11y representations of data specifically for neurodivergent groups
• also interested in something I’ve been calling ”playlists” but there’s likely a real word for it. it’s the idea that media and objects should have metadata tags that go beyond just the genre or 2-15 keywords, so that when an application like Spotify or Netflix or Amazon shopping recommends “something else you might like“ it pulls from a web of shared meaning rather than just listing items from the same genre or keyword

Note: I’m aware that I’m probably using a lot of wrong vocabulary. I have no formal training in any of this, my interest in this comes from years of red-teaming my own system prompts in my LLM home lab at a hobbyist level.

I open-sourced a pipeline that starts from Zotero PDFs and tries to do idea discovery, not just summarization.

It extracts claims into Neo4j, finds cross-document relations, then proposes research links between claims from different papers (SYNERGIZES_WITH, INSPIRES_FOLLOWUP, POTENTIAL_APPLICATION, etc.), with an optional novelty check via OpenAlex + Semantic Scholar.

There’s also a “ring” scraper to build corpora from distant domains so the ideas are less samey.

Repo: https://github.com/hergit/eureka-graph

Examples: https://github.com/hergit/eureka-graph/tree/main/examples

Ring design: https://github.com/hergit/eureka-graph/blob/main/docs/eureka-rings.md

MIT. Local Neo4j + any OpenAI-compatible LLM.

Happy to answer setup questions (GROBID optional, OpenAlex key recommended).

Hi everyone,

I am working on a transformation from OWL + SHACL models into UML + OCL, and I’d love to get some opinions from people in the semantic web community.

The motivation is that many companies already have their own knowledge graphs, and domain ontologies often already describe their domains quite well. So, instead of rebuilding everything from scratch for software development, why not reuse that knowledge to accelerate software production?

Ontologies are great for structuring and reasoning over data, but when it comes to application design and code generation, UML is often a closer representation of what software teams actually work with. The idea is therefore to automatically transform a KG/ontology into UML models and OCL constraints that can then be used by existing MDE and code generation tools (with and without LLMs). Note that we don't simply represent an ontology using a class diagrams, we automatically convert all logical constraints from such ontology as well.

I wrote a short blog post explaining the transformation rules if anyone is interested:
https://modeling-languages.com/owl2_shacl-uml_ocl/

We’ll also present the work at the KGMDSE workshop in October, co-located with ISWC.

The long-term vision is something like:

KG → UML/OCL → automatically generated software system

I’m curious about the semantic web community opinion about this. Does it sound useful in practice? Where do you see the biggest limitations?

Any feedback is welcome!

Using sentence-transformers/all-MiniLM-L6-v2 to Fix Our Web Crawler

Our web crawler was returning entire pages. A question like "what is the return policy" would get the whole product page back. A 5000 word article when we only needed 200 words.

This was wasteful. One page used 50,000 tokens. The model got confused reading all the noise and started making things up.

We used sentence-transformers/all-MiniLM-L6-v2 to filter. It converts text into numbers that capture meaning. Similar meanings land close together. So it finds chunks that match the question's meaning, not just keywords.

Result: 5,000 tokens per page instead of 50,000. 90 percent savings. No more hallucinations.

If you deal with web crawling or large text, this model is worth trying

I never messed with that in my long SWE career. i remember the semantic web from the 90s, design by committee VRML and it was during the AI winter. Some students went to France to study on that, who knows what happened to them.

Last week i was working with an ai agent, my idea was take a pdf create a report of all the columns where account starts with 1. Perfect, next file, dont have the account field and it just make something up spent a night testing, "dont lie to me", i had a 64kb of psychotherapy and it never got a good result with my dataset. Mission Failed.

The solution was an ontology what the thing IS, not an example, for a programmer is which is the Class not the Object ...In Mexico they call it agua AQUA.... you cant tell it what it looks like how similar it is you need its name.

It was overfitting, anything i said in instructions was followed LITERALLY, it contaminated everything and my idea to work with samples wouldn't work. if i add a rule tomorrow it will break everything. I tried to strip the name of the columns... he saw a column "value" and went bananas, put the grammar only, failed. At the end the idea of the ontology popped up and told it to create an ontology from the examples, like 5k. It was blazing fast and the end result was a 64kb file. With that we were back in business.

In that tiny file is the distilled knowledge of years of work, pure unadultered business rules. I created some other ontologies over the ux the interactions, the flows intentions. the reports how they are organized, the founding principles why are we doing this in the first place. Sometimes while merging two of them the result was awful others unexpectedly good. Downloaded a huge GitHub collection and applied the same "reverse engineering" technique.

I like always with the codex to create an infography a website, a visualization 3d html+js, a way you can see. sometimes it talks about words i cant really define like deontology and hyperdimensions, i just say go on present them in a 3d or 2d way.

Tl;dr use ontologies with AI.

PD 1. Pellet and the hermit are a waste of time, i really tried but we are in 2026 and your app is a Java Swing 1.4, you all had the chance to be trillionaires but decided to use Java. I dont have any issue with the maintainers but i hope the project dies and proper semantic tools pop up. You had 20 YEARS to make something with government money and did nothing.

PD 2. I just cant use the term Ontology i cant go to the library and talk about it without some weird Haskell guy talking about his monads. (even if some prolog code popped up i dont know why). And wont go to the office monday 8am to give a Predictive History lesson to my boss. No thanks, Ontology is too cringe and will use at most Concept.

Not sure if some stuff was AI or semantic, anyway. if i have something wrong i am ready to be corrected.

I come from a philosophy background and now work in AI governance. Is applied ontology actually as hot as it looks, does a philosophy background give a real edge there or is it better to just go technical? Trying to figure out if this is worth specializing in. My coding experience is somewhat limited but I'm in a position where I can learn from a ton of data scientists in my company. My read on the AI landscape is that world models / contextual awareness are the next big bet in AI which seems to suggest ontology builders will only get more in demand.

I have been trying to create perfect graph visualisation without success, every time I think I am getting close, but at the end it just does not work. Do you think it is even possible?

Hi! Is there anybody who knows if there is a function in Sketch Engine that allows to cluster different words, related to a specific one, in different semantic domains. I tried to use the "Cluster similar item" function but it is not properly what I need because it requires then a manual categorization.

Can anyone help me or give me a name of a software that can do this?

Thanks!

My problem is I need to create KG for the unstructured data like text, word, pdf.. and for structured data like database tables.

Let say we have created the KG or some context layer for the structure data.. Next problem is retrieval of the data correctly for the given query..

please suggest your ideas 💡

Hello,

I need to split a 5 GB TTL file into smaller chunks of less than 500 MB (I'm told the file is in RDF format). How can I do this, and what apps or websites should I use without losing the file's integrity ? (I'm on mobile.)

Hello there. I've recently started a new job at a media museum/archive. The institution is on the smaller end, with 6 active employees total, circa 20k individual cultural objects, almost no digital infrastructure and virtually no legacy data.

Since we're starting from scratch and aren't constrained yet by boring traditional ways, I decided to look into modern data technologies and became very interested in the use of semantic web, RDF, RDFS and linked open data models in the cultural heritage sector.

I can program and understand topics of computer and information science, but I'm no trained professional, but I'm committed. I know the learning curve is steep, but I'm wondering if the bet would pay off in 2 years through providing more flexibility, enriched documents, connections outside our local system, nice and new UX, creative projects and increased public impact.

Could more experienced people help me understand the costs involved and if it would be worth it to our business case? We intend to remain lightweight on the amount of total metadata/statements produced and in theory we can always ask for special funds and technical assistance from our parent institution.

If you know of any small(ish) cultural heritage institutions using semantic web as the basis to their data systems, please tell me who are they?

I’m working on an ai scheduling os for small and large businesses. I’m trying to make a 3d knowledge graph of the ai the system will use but can’t seem to figure it out any help would be awesome.

Hey everyone,

A lot of introductory material on RDF and Turtle syntax is either strictly academic or jumps straight into heavy tooling without explaining the underlying domain modeling mindset.

I put together a free guide aimed at newcomers: Ontology and Turtle for Beginners. It covers:

• How ontologies define shared meaning across scattered enterprise data
• The core mental model behind RDF triples and practical Turtle notation
• Foundations for moving toward knowledge graphs, digital twins, and reasoning engines

You can view the page and download the PDF directly here:

https://www.swatantra.ai/resource-ontology-turtle.html

Would love to get your thoughts, constructive feedback, or hear how your teams introduce semantic concepts to non-experts!

I have been reading around ISO 15926, DEXPI and CFIHOS, mostly to understand how the process industry wires its handover data together, and I ran into a number I do not trust. It is big enough that my first assumption is that I have misunderstood what these identifiers are for. I would rather be corrected here than write anything confident about it.

Background for anyone who has not touched this stack. CFIHOS is the capital facilities information handover specification, governed by IOGP, and it is what an engineering contractor uses to hand plant data over to an operator. It sits above ISO 15926, whose reference data library is curated by the POSC Caesar Association. CFIHOS 2.0 publishes a crosswalk file mapping its own object codes to POSC Caesar RDS identifiers, and the whole thing is a free download with no registration.

What I did. The file CFIHOS CORE CFIHOS object equivalent mapping v2.0.csv has 3,091 rows across four coding sources. 1,832 of those rows are POSC Caesar, carrying 1,777 distinct identifiers. On 18 August 2026 I dereferenced all of them against https://data.posccaesar.org/rdl/{id}, then re-checked every failure against a staging host I stumbled on afterwards.

What came back.

• 967 (54.4%) resolve on production.
• 611 (34.4%) resolve only on staging4.data.posccaesar.org, whose certificate expired on 6 April 2026, so a normal client will not connect without an override.
• 199 (11.2%) resolve on neither.

At the object level, 825 of the 1,832 CFIHOS objects carrying a POSC Caesar mapping point at something I cannot look up today.

The part that makes me think I am misreading something is that the failures are perfectly partitioned. Every one of the 810 production failures is an identifier beginning RDS22. Nothing in that block resolves. Nothing outside it fails. Real link rot is never that tidy, so I suspect this is a deliberate arrangement I do not understand rather than a fault.

Things I already ruled out, so you can skip them.

• Not a wrong base URI. Production does not serve the /iso/15926-4/rdl/ path at all, including for identifiers that definitely exist.
• Not a host migration. rds.posccaesar.org/rdl/ returns 404 even for RDS414674, which resolves fine on production and is labelled "VESSEL".
• Not a soft-200 resolver. Nonsense identifiers hard-404 on both hosts, so a 200 carries information.
• There is a third host, data.15926.org, which I had to discard because it returns a byte-identical 200 response for its own root, a real identifier, a dead identifier and complete nonsense. It is a JavaScript shell. If you audit linked data, run a nonsense-identifier control before you believe anything, because that host would have told me everything was fine.

So my actual questions.

1. Are these RDS values supposed to be dereferenceable at all, or are they intended as opaque cross-references that happen to look like they map onto a URL pattern? If it is the latter, my whole framing is wrong and I would like to know before repeating it.
2. Is the RDS22 block deprecated, pending publication, or something else? PCA's own vocabulary exposes statusPCA, valEffectiveDate and valDeprecationDate, which would settle it instantly, but the endpoint serving that data returns 404 for me.
3. Is it normal in this world for part of a published reference library to live only on staging? I genuinely do not know whether that is a problem or just how the release process works here.
4. Does anyone consume this crosswalk programmatically, or is it a human-reference artifact nobody dereferences in practice? That would explain a lot.

One smaller thing I am more confident about. The same column addresses what looks like the same registry in two formats. Rows tagged POSC CAESAR carry values like RDS2227295, while rows tagged ISO 15926 part4 carry bare integers like 670171, for which I found no resolver in any form I tried. A single dereferencing routine over that column would silently fail on 570 rows.

I have not written to POSC Caesar or IOGP yet, because I wanted to find out here whether I am misreading the whole thing first. If it turns out to be real I will report it to both.

All of it is reproducible from the free CFIHOS download, and I am happy to hand over the raw HTTP logs and the scripts to anyone who wants to check my work, which is really the point of posting.

Disclosure: identifier and register integrity is what I do for a living, so I look at this sort of thing habitually. Nothing to sell here, no links, and I am not redistributing anyone's reference data.

-----

I genuinely hope for someone saying "those were never meant to resolve." That kills the headline and saves me from publishing a wrong article!!

Also If needed I have a raw logs to hand.

1. I have created a graph using database schema and using that KG as a schematic layer --> traverse the layer for initial context and generate a SQL query for a given user query.

2. Creating whole structure data into KG.

Please prove best practices for the KG graph building, do we have any pipeline for knowledge graph building.

I'm working on a project involving Augmentation Intelligence. Actually trying to transform unstructured text into organized knowledge format.

LLMs could work, but the hard part is handling language nuances.

Example problem: "It's been much warmer in recent years"

How we can handle text like this? Like Fuzzy Logic?

Hey everyone,

I’m working on a personal project to build a completely local, lightweight system (codename: Orin) that can process messy unstructured information and segregate/clean it into highly structured, tabular formats (CSV files). Essentially, it's meant to be a better, fully offline version of Atlas.

Here is the exact data structure and the pipeline I am trying to build:

1. The Target Data Schema

The model needs to take raw info and divide it into clear subtopics:

• Columns: Topic | Subtopic1 | Subtopic2 | Subtopic3 | Info
• Example Output:
  • Topic: Flying machine
  • Subtopic1: Airplane
  • Subtopic2: Passenger plane
  • Example Scenario: If incoming news data says "Qatar Airways wins starring award again", the model should automatically categorize it under the correct subtopic hierarchies and store the relevant data in the final Info column.

2. Proposed Pipeline & Architecture

I am planning a Retrieval-Augmented Generation (RAG) approach using a combination of specialized, local agents:

• A Fact Searcher / Main Topic Searcher: To find missing points and gather core data from the dataset.
• A Local Summarizer / Keyword Generator: Acting as a text quantizer to condense the given prompt or raw context.
• A Joke Generator (Optional Component): To add humor or personality to the generated answer output.
• The Core Logic Flow: Prompt → Gathers data for it → Finds missing points → Fills the spots (to Phrase) → Final Answer.

3. The Big Bottleneck: Hardware Constraints & Failed Attempts

Since this system must run locally, finding the right LLM engine and model has been incredibly difficult. Here is what I’ve attempted so far:

• llama.cpp: Would technically work, but performance is a massive issue (it took over 2 hours just to compile 8%).
• TinyStories: Super fast at stitching sentences together, but it only tells stories; it cannot handle this specific data formatting task.
• TinyLlama (llama.co): Unable to get it to work properly / wouldn't run.
• Ollama: Cannot use it seamlessly because it isn't properly optimized or built for my hardware (ARM chips).

I would like to ask the community how to make the better and how to develop it to efficient RAG model For my Project.

And want to confirm it's on the right track

Hi everyone,

I have an idea for a personal project, and I want to know if I'm thinking in the right direction.

I don't want to build just another note-taking app or mind map. I want to build a knowledge graph where I can enter any word or concept, and the system automatically knows where it belongs.

My goal is to build a personal knowledge system that becomes smarter over time, rather than just storing notes.

For example:

- If I enter a new word, it should suggest the best place in the graph.

- If that word belongs in multiple places, it should connect it to all of them.

- If a new concept should come between two existing concepts, it should reorganise the graph instead of just adding another node.

- It should detect duplicates and synonyms.

- It should explain *why* it placed a concept there.

- If it isn't confident, it should ask me instead of guessing.

- It should also ask if I want to expand that concept further and generate the next level of the graph.

I'm building this only for myself, not as a commercial product, but I want to build it with commercial-level reliability.

While thinking about this, I came across topics like **knowledge graphs**, **ontology engineering**, and **semantic search**, and now I realise that the hardest part is probably designing the ontology, not writing the code.

I have a few questions:

- Has anyone built something similar, and what should I study before I go too far?

- What are the biggest challenges in building a system like this?

- How do companies like Google or researchers design and improve ontologies?

- If you were starting from scratch, what would you learn first?

- Are there any books, papers, courses, or open-source projects that you think are must-reads for this?

- Also, if you were designing this today, what architecture or tech stack would you choose?

I'm still learning, so even if you think my idea has flaws, I'd really appreciate honest feedback. I'd rather know what's difficult now than after spending months building it.

Thanks!

Hey everyone, I built this niche tool to structure data for knowledge layer for agents, its a bit like an acoustic guitar for critical industries that demand heavy data reasoning … would love to hear your feedback, some small bugs like analyzers staying prompted to the template doc

https://github.com/galeluka/Pennant