4 ms·

Multiple comments here state that AI eliminates the need for Semantic web tech, and I can understand that perspective, but it's also a narrow way of interpretin

Multiple comments here state that AI eliminates the need for Semantic web tech, and I can understand that perspective, but it's also a narrow way of interpreting the question. While LLMs produce great results without relying on semantic relationships, they could also be used to build semantic relationships. There's probably applications there worth exploring. For example, if a general-purpose LLM can build a semantic dataset for solving specialized problems, then might that approach be more efficient versus training a specialized LLM?

dtagames 1y ago
They do build those relationships, and by being trained on large, general data sets rather than specialized ones. There's no need for special markup to achieve that.
- evanjrowley 1y ago
  Right. So I think the potential value here is not using a special markup to enable LLMs, but leveraging LLMs to build the special markup so that it can be applied towards other uses.
  - dtagames 1y ago
    I guess. If you really wanted something that took some text and wrapped it in markup, you can ask for that already with ChatGPT. It's easy to say, "lookup the top selling DSLR cameras and make me a list of their features in JSON." I can't see anyone making a special tool just for that as a general use case. Everybody wanting such output would want their own format for the markup, and we're right back to XML. Today, it's simpler to use RAG, which is just using the LLM to figure out the "English" part, then using regular (procedural, normal programming code) tools to put things in boxes, or data storage, (or markdown), etc. If you really want consistent output, you can't have the LLM generate it. You would need to RAG that output.
    - evanjrowley 1y ago
      The markup in this case would be some type of semantic web format, like JSON-LD[0] or OWL[1], or some database that can process SPARQL[2] queries. Goal being the "inverse" of something like OWL2Vec[2]. EDIT1: A few weeks ago, a team of Brazilian researchers published a report about using ChatGPT to enhance agriculture-focused OWL dataset[4]. EDIT2: In addition to training LLMs on ontologies, it looks like Palantir is using ontologies as guardrails to prevent LLM hallucinations[5]. Makes sense. [0] https://json-ld.org/ https://json-ld.org/ [1] https://www.w3.org/TR/owl2-syntax/ https://www.w3.org/TR/owl2-syntax/ [2] https://www.w3.org/TR/sparql11-query/ https://www.w3.org/TR/sparql11-query/ [3] https://arxiv.org/abs/2009.14654 https://arxiv.org/abs/2009.14654 [4] https://arxiv.org/abs/2504.18651 https://arxiv.org/abs/2504.18651 [5] https://blog.palantir.com/reducing-hallucinations-with-the-ontology-in-palantir-aip-288552477383 https://blog.palantir.com/reducing-hallucinations-with-the-o...

dtagames 1y ago

They do build those relationships, and by being trained on large, general data sets rather than specialized ones. There's no need for special markup to achieve that.

evanjrowley 1y ago
Right. So I think the potential value here is not using a special markup to enable LLMs, but leveraging LLMs to build the special markup so that it can be applied towards other uses.
- dtagames 1y ago
  I guess. If you really wanted something that took some text and wrapped it in markup, you can ask for that already with ChatGPT. It's easy to say, "lookup the top selling DSLR cameras and make me a list of their features in JSON." I can't see anyone making a special tool just for that as a general use case. Everybody wanting such output would want their own format for the markup, and we're right back to XML. Today, it's simpler to use RAG, which is just using the LLM to figure out the "English" part, then using regular (procedural, normal programming code) tools to put things in boxes, or data storage, (or markdown), etc. If you really want consistent output, you can't have the LLM generate it. You would need to RAG that output.
  - evanjrowley 1y ago
    The markup in this case would be some type of semantic web format, like JSON-LD[0] or OWL[1], or some database that can process SPARQL[2] queries. Goal being the "inverse" of something like OWL2Vec[2]. EDIT1: A few weeks ago, a team of Brazilian researchers published a report about using ChatGPT to enhance agriculture-focused OWL dataset[4]. EDIT2: In addition to training LLMs on ontologies, it looks like Palantir is using ontologies as guardrails to prevent LLM hallucinations[5]. Makes sense. [0] https://json-ld.org/ https://json-ld.org/ [1] https://www.w3.org/TR/owl2-syntax/ https://www.w3.org/TR/owl2-syntax/ [2] https://www.w3.org/TR/sparql11-query/ https://www.w3.org/TR/sparql11-query/ [3] https://arxiv.org/abs/2009.14654 https://arxiv.org/abs/2009.14654 [4] https://arxiv.org/abs/2504.18651 https://arxiv.org/abs/2504.18651 [5] https://blog.palantir.com/reducing-hallucinations-with-the-ontology-in-palantir-aip-288552477383 https://blog.palantir.com/reducing-hallucinations-with-the-o...