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Ontology vs. LLM for Question Growth (or each?)

I work at a recruitment company the place as a job seeker you may seek for jobs and as a recruiter you may seek for candidates in our candidate pool that may match the job description. At present each search engines like google are primarily based on Elastic Search with some dealing with of synonyms, however we nonetheless have issues with exhibiting all related search outcomes if the search time period does not match the job description or CV (for instance if some particular frontend framework is required for a job, a candidate with expertise in an analogous framework ought to nonetheless be proven within the outcomes however with barely decrease relevancy.

With out a lot consideration for various approaches (as a result of we do not have a lot NLP Experience within the firm and have a fairly new information science division), we already experimented with constructing an ontology primarily based on exterior ontologies and our personal information (e.g. Python is utilized in Information Science) to search out carefully associated phrases and develop the search queries primarily based on these relationships. Whereas this strategy appears to work considerably, it feels sort of cumbersome, outdated and can in all probability want a variety of upkeep in the long term. For instance utilizing a immediate in GPT yielded very comparable ends in a matter of seconds, which raises the query if, for instance, simply utilizing the embeddings of the search phrases would already be sufficient to develop a customers search question with further related phrases.

What strategy would you recommend when coping with the issue of question growth? Or would a mix of each approaches make sense (e.g. utilizing an LLM to automate constructing an ontology). Are ontologies concerning that use case outdated or am i simply falling for the ChatGPT hype?

I might very a lot respect your insights!

Comments ( 3 )

  1. I would go for the llm. As a recruiting company I assume you’d have access to good training data.

    As you said building an ontology is a lot of work, not only will you need to constantly update it (e.g., to reflect tech changes) but you will also have to define the actual ontology tree. For a job search I assume you want to have a somewhat good resolution of the ontology leaves, which might be too much work.
    Even if you automate the ontology creation, you’ll spend a lot of time curating it. And I’m not sure how much you’d gain from it.

  2. Given that you already have an ontology, you could also try to find the most similar terms between the query and ontology to expand the query. The similarity can be derived using embeddings from sentence tramsformers for instance.

  3. I’m a bot, *bleep*, *bloop*. Someone has linked to this thread from another place on reddit:

    – [/r/datascienceproject] [Ontology vs. LLM for Query Expansion (or both?) (r/DataScience)](

     *^(If you follow any of the above links, please respect the rules of reddit and don’t vote in the other threads.) ^([Info](/r/TotesMessenger) ^/ ^[Contact](/message/compose?to=/r/TotesMessenger))*

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