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   “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?
   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
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
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
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.)
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.)
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
Searching for an identifying specific noun & verb
combinations allows for the ability to target responsibilities
and 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"
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
   support* NEAR (CEO or CFO or CTO or CIO
    or "C-Level" or chief*)
   config* NEAR juniper NEAR router*
    “created access database”~7




* PCRecruiter and some other ATS/CRM solutions use Lucene for text search/retrieval
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 isn't 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
 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 isn't
  explicitly mentioned anywhere
    If the text isn't present, it can't be retrieved!
 Let's 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 isn't a theoretical example - I achieved a high level placement with a fantastic candidate at EMC using
this exact approach!
 Let's 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 of
PeopleSoft project experience, and neither PeopleSoft nor any PeopleSoft related terminology was anywhere
in her resume
Level 4 Semantic Search can be achieved through:
 1. Man
       Searching specifically for text that can imply skills
        and experience that isn't 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.)
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
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
       I'm 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
Semantic Search         Human   Application
Level 1 - conceptual     YES       YES
Level 2 - contextual     YES       YES

Level 3 - grammatical    YES       NO

Level 4 - inferential    YES       NO

Level 5 - tagged         YES       NO?
Thank You!
Glen Cathey

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How semantic search can go beyond keywords to match meaning

  • 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. 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. 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. 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. 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. 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. 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. Searching for an identifying specific noun & verb combinations allows for the ability to target responsibilities and 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. 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. support* NEAR (CEO or CFO or CTO or CIO or "C-Level" or chief*)
  • 12. config* NEAR juniper NEAR router*
  • 13. “created access database”~7 * PCRecruiter and some other ATS/CRM solutions use Lucene for text search/retrieval
  • 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 isn't 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.  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 isn't explicitly mentioned anywhere  If the text isn't present, it can't be retrieved!
  • 16.  Let's 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 isn't a theoretical example - I achieved a high level placement with a fantastic candidate at EMC using this exact approach!
  • 17.  Let's 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 of PeopleSoft project experience, and neither PeopleSoft nor any PeopleSoft related terminology was anywhere in her resume
  • 18. Level 4 Semantic Search can be achieved through: 1. Man  Searching specifically for text that can imply skills and experience that isn't 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. 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. 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  I'm 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. Semantic Search Human Application Level 1 - conceptual YES YES Level 2 - contextual YES YES Level 3 - grammatical YES NO Level 4 - inferential YES NO Level 5 - tagged YES NO?

Editor's Notes

  1. Source:Creative Commons/Flickr
  2. Mr. Singhal is the the master of what Google calls its “ranking algorithm” — the formulas that decide which Web pages best answer each user’s question
  3. The study of meaning, inherent at the levels of words, phrases, and sentences
  4. via verb/noun combinations - Responsibilities and capabilities rather than titles or words mentioned
  5. via verb/noun combinations - Responsibilities and capabilities rather than titles or words mentioned
  6. Infer:toderive as a conclusion from facts or premises:Imply: to contain potentially, to express indirectly
  7. Infer:toderive as a conclusion from facts or premises:Imply: to contain potentially, to express indirectly
  8. Infer:toderive as a conclusion from facts or premises:Imply: to contain potentially, to express indirectly
  9. Infer:toderive as a conclusion from facts or premises:Imply: to contain potentially, to express indirectly