Prasad Y.

The blog discusses in the section "How LLMs Can Help Generate Knowledge Graphs" .. partly how our (leadsemantics.com) Text to Knowledge Graph pipeline (TextDistil) is implemented. TextDistil in version 1.5 generates fact graphs conforming to an Ontology from text like news, etc. We are presenting TextDistil as part of our talk on extracting ESG knowledge from global News at the KGC2023 as part of the Sustainability track. Good times ahead for Knowledge Graph tech 👍 Thanks for sharing Prasad

Hi Ashleigh F. Question - Who are going to be presenting in the 'tools and demonstrations' on May 6th? Thanks in advance

Thank you François S.

Hi Efstratios K. Thanks for the interest in TextDistil. Congratulations on the work and the paper. Thanks for sharing, reading the paper has given us some understanding of the goals (though, we have to reread portions of the paper closely!). TextDistil, while a reasonable architectural solution, I expect (code) changes be made in the rules and classifier modules (not just new training!). We have to check out CLTK (may be others to account for the archaic/extra-ordinary constructions in the text) how it can substitute/supplement the current lexical modules in the pipeline. At the other end of the pipeline - we are not yet clear about the support for reification statements for such things as contradictions ! I believe our team will be required for code changes and to help with (re) configuration along side you all, should we attempt it. Given the research (non revenue) nature of the project, it would be a matter of scheduling on our side. We are very much interested in the domain and open to discuss

Thanks and Thank you for attending the webinar Shashishekar R.. At this time, we have given access to one academic institute and educating ourselves on the cost implications. We love to give access to interested folk who want to try in a non-commercial setting. Hope to have an answer soon. Thanks for the suggestion.

Hi Kyle This is Prasad from Lead Semantics, I presented 'TextDistil and Fluree' usecase in the webinar you mentioned. Thanks for attending the webinar. To clarify, the Ontology is specific to the domain and the application for which the extraction is meant for. Another domain and application would call for another Ontology. (we are not doing open domain extraction - that is not our focus, and there are many solutions to do that!) TextDistil is designed to work with different Ontologies. If you have an Ontology that is great, then with TextDistil, you only have to map the rest of the pipeline to fit your Ontology (and of course factor in implications of the input text corpus). In case I am not clear, please feel free to reach out to me.

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