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Semantic Search for Sourcing and Recruiting


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Intended for HR professionals, sourcers and recruiters, this presentation explains and explores the concepts of semantics and semantic search, including the 5 levels of semantic search: Conceptual Search, Contextual Search, Grammatical/Natural Language Search, Inferential Search, and Tagging.

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Semantic Search for Sourcing and Recruiting

  1. 1. Image source:
  2. 2.  “The holy grail of search is to understand what the user wants. Then you’re not matching words; you’re matching meaning.” – Amit Singhal, Google Can applications ever really know what we’re looking for?
  3. 3.  Semantics: The study of meaning, inherent at the levels of words, phrases, and sentences Semantic Search: Searching beyond the literal lexical match and into the meaning of words, phrases, and sentences 5 Levels
  4. 4. 1. Skill words/title association, variants, and misspellings  Director of business development, business development director, etc.  JDE, JD Edwards, etc.  10Q = SEC reporting  SAP = ERP  JMPC, JP Morgan, JPMorganChase
  5. 5. Level 1 Semantic Search can be achieved through: 1. Man  Application of knowledge of synonymous terms and the relationships between concepts to search for variants and related terms 2. Machine  Hierarchical or synonymous taxonomies  Semantic Clustering
  6. 6. 2. Contextual  Words have different meaning depending on where they are specifically mentioned in resumes  Summary, education, recent work experience…  Education vs. address (Harvard Ave.)
  7. 7. Level 2 Semantic Search can be achieved through: 1. Man  Innate understanding of contextual references  Field-based search of parsed resumes/profiles (most recent title, etc.) 2. Machine  Parsing of resumes and profiles  Automated field-based matching (most recent exp., etc.)
  8. 8. 3. Grammatical, natural language search  Targeting sentence-level meaning with noun/verb combinations  Sentence-level semantics are much more powerful, predictive, and flexible than word or phrase level semantics
  9. 9. Searching for an identifying specific noun & verbcombinations allows for the ability to target responsibilitiesand capabilities, not just keyword presence!  Examples of noun/verb combinations  "3 full life cycle SAP R/3 implementations"  "Carry out wound (pressure ulcer) assessment, recommend treatment…"  "SOX compliancy weekly internal auditing"  "Perform investment performance and attribution analysis"
  10. 10. Level 3 Semantic Search can be achieved through: 1. Man  Any search engine that supports fixed or configurable proximity – the ability to control the distance between search terms 2. Machine  No solution that I am aware of allows for the automation of specifically targeting of noun/verb combinations to isolate sentence-level meaning
  11. 11.  support* NEAR (CEO or CFO or CTO or CIO or "C-Level" or chief*)
  12. 12.  config* NEAR juniper NEAR router*
  13. 13.  “created access database”~7* PCRecruiter and some other ATS/CRM solutions use Lucene for text search/retrieval
  14. 14. 4. Implied skills, experience and responsibilities  Inferential semantic search is a form of Level 3 Talent Mining (Indirect search)  Inferential search involves specifically searching for what isnt explicitly mentioned – words and phrases that can imply experience that is not explicitly stated/present in a resume, LinkedIn profile, or other source of human capital data ▪ Infer: derive as a conclusion from facts or premises ▪ Imply: to contain potentially, to express indirectly
  15. 15.  Text-based human capital data (e.g., resumes, LinkedIn profiles, etc.) is intrinsically limited and never provides a complete picture People simply do not mention every detail about their professional career Many talented people simply cannot be found via direct search methods, because their experience isnt explicitly mentioned anywhere  If the text isnt present, it cant be retrieved!
  16. 16.  Lets say you need someone who has managed EMC SAN projects/environments  Realizing that some people will not explicitly mention EMC or SAN (or any variant) in their resume/profile, you could search specifically for data center move, migration and consolidation experience, because this can imply SAN experience, and EMC is one of the largest SAN players** This isnt a theoretical example - I achieved a high level placement with a fantastic candidate at EMC usingthis exact approach!
  17. 17.  Lets say you need a Business Analyst with PeopleSoft experience  After exhausting all search methods using "PeopleSoft" directly in queries, you could NOT out "PeopleSoft" and search for the mention of companies that you know use PeopleSoft  People who have worked at a company that is known to use PeopleSoft have a probability of experience with PeopleSoft, even in the absence of explicit mention of "PeopleSoft"** I filled a critical role at Sprint/Nextel using this exact method. The candidate had 3 recent and strong years ofPeopleSoft project experience, and neither PeopleSoft nor any PeopleSoft related terminology was anywherein her resume
  18. 18. Level 4 Semantic Search can be achieved through: 1. Man  Searching specifically for text that can imply skills and experience that isnt explicitly mentioned 2. Machine  No solution that I am aware of allows for inferential semantic search beyond Level 1 conceptual search achieved through synonymous or hierarchical taxonomies (e.g., GAAP implies accounting exp.)
  19. 19. 5. Human-reviewed and classified  The highest level of semantic search involves meaning applied by people and the ability to search for human capital data (resumes, social profiles, etc.) that has been identified, analyzed and labeled by a human  Searchable tagging allows the retrieval of human capital data that has been labeled after human analysis that can include information not actually present in the document/profile, as well as "intangibles" such as personality and cultural match
  20. 20. Level 5 Semantic Search can be achieved through: 1. Man  Tagging human capital documents, records and profiles and the ability to search by tags 2. Machine  Im not aware of any solution that has been developed to do this, but if I were to design one, it would involve the ability to automatically match across human-applied tags
  21. 21. Semantic Search Human ApplicationLevel 1 - conceptual YES YESLevel 2 - contextual YES YESLevel 3 - grammatical YES NOLevel 4 - inferential YES NOLevel 5 - tagged YES NO?
  22. 22. Thank You!Glen Cathey