5/16/2023 0 Comments Careerbuilder boolean searchNatural language search is essentially just what it says – searching by natural language using phrases or even full sentences.įacebook’s Graph Search is a good example of a natural language search interface (it doesn’t even support Boolean queries), and many people try typing in full questions in sentence form into Google and Bing, which could be classified as natural language search. Using standard Boolean operators, you simply cannot control precisely where your keywords and titles appear in results (resumes, LinkedIn profiles, etc.), and without that control, you will suffer lower relevance and a higher percentage of false positive results.Įven so, you can move beyond keyword and title search and search for exact phrases, which is what many people in sourcing and recruiting refer to as natural language search. The words you searched for are in the results, but the results do not match your need/intent. If your intent is to find iOS developers and you return results of people who mention iOS and development, but they do not have any iOS development experience – these results are known as false positives. Of course, just because certain words appear in a person’s resume or profile it does not mean that the person has been primarily responsible for working with those words (typically skills, technologies, etc.).įor example, if you were looking for someone who has iOS development experience, searching for, even along with titles and other terms, can and will return many people who do not have iOS development experience, but simply mention those words at various points of their resume or profile. That’s because the search engine doesn’t “know” what you’re looking for – it simply returns results with the keywords you asked for. Most sourcers and recruiters are actually trying to find people who have specific skills and experience, because most hiring managers typically want people who have been paid for very specific responsibilities. Highly “relevant” results are those that match exactly what the searcher is looking for.įor sourcing, highly relevant results are essentially people who are highly likely to be qualified and ideally interested in the opportunity you are sourcing/recruiting for. Relevance can be defined as the extent to which a search result matches the information need based on the intent of the person executing the search. That’s precisely why it can take so much time reviewing results – you have to inspect each result to see if it’s relevant. The vast majority of sourcers and recruiters create Boolean search strings with keywords and titles that simply return a collection of words on a resume, profile or page – not people with specific experience.Īs we all know – your search terms can appear on a resume, a LinkedIn profile or web result, but that doesn’t guarantee you that the result is viable or even relevant. The Problem “Standard” Boolean Search (AND / OR/ NOT) If you’d like to learn more about the 5 levels of semantic search, you can view this Slideshare presentation from my 2010 SourceCon keynote (starting on slide 72).īut before I get to explaining extended Boolean search, I am first going to explain the challenges of “standard” Boolean search which will set the stage for an appreciation of the power of level 3 semantic search. In this post, I’m going to review some sites/databases that claim to support proximity search (Monster, Google, Bing, Exalead) and show you how to use proximity search (a form of extended Boolean) to achieve level 3 semantic search – which is grammatical/natural language search using noun/verb combinations in your queries. Of course, at the heart of semantic search is semantics, which is the study of meaning inherent at the levels of words, phrases, and sentences. If you’re not very familiar with semantic search (for sourcing – not search engines), I strongly suggest you read my comprehensive article from January 2012 on the subject: The Guide to Semantic Search for Sourcing and Recruiting. I’m referring to user-defined semantic search, where you tell a search engine exactly what you want with your query, and the search engine doesn’t try to “understand” your search terms or “figure out” what you mean through taxonomies, RDFa, keyword to concept mapping, graph patterns, entity extraction, fuzzy logic, etc. Now, I am not talking about black box semantic search (e.g., Google, Monster’s 6Sense, etc.). When it comes to sourcing and recruiting, semantic search is perhaps the most powerful way to quickly find people who have experience you’re looking for.
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