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.

1. Image source: http://www.flickr.com/photos/will-lion/2680454123/

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?

22. Thank You!
Glen Cathey