Semantic Search for Sourcing and Recruiting

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Semantic Search “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?

Semantic Search 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

Level 1: Conceptual1. 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: How

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

Level 2: Contextual2. 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: How

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.)

Level 3: Grammatical3. 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

Level 3: ExamplesSearching 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: How

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

Level 3:Monster support* NEAR (CEO or CFO or CTO

or CIO or "C-Level" or chief*)

Level 3:Monster config* NEAR juniper NEAR router*

Level 3: Lucene

“created access database”~7

* PCRecruiter and some other ATS/CRM solutions use Lucene for text search/retrieval

Level 4: Inferential4. 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

Level 4: Inferential 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!

Level 4: Example 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!

Level 4: Example 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: How

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.)

Level 5: Tagging

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: How

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 CapabilitySemantic Search Human ApplicationLevel 1 - conceptual

YES YES

Level 2 - contextual

YES YES

Level 3 - grammatical

YES NO

Level 4 - inferential

YES NO

Level 5 - tagged YES NO?

Have a semantic search product? I'd be happy to evaluate it publicly or privately - Google me to contact

Thank You! Glen Cathey