r/semanticweb

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!

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.

I'm working on Hyper-Markdown (.hmd): ordinary Markdown plus rich visuals plus wiki links into a knowledge graph. Every .md file is already valid .hmd.

The project is still at an early stage, which means the specification is very much open to change.

I'm looking for:

• contributors
• design/spec feedback
• people willing to challenge the current ideas

There is already a lightweight proposal process for discussing language changes.

https://hyper-markdown.org/

If this sounds interesting, I'd be very happy to have people get involved early.

Hello,

I am looking for an IT taxonomy for software (and maybe hardware) to put concepts like desktop application, microservice, cloud, cicd pipeline, etc. into a structure.

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!