SlideShare a Scribd company logo
1 of 13
SKILLS ONTOLOGY
FROM IT’S YOUR SKILLS
A s m a r t l a r g e m e n u c a r d o f s k i l l s
i t s y o u r s k i l l s . c o m
itsyourskills.com
WHAT’S THIS
SKILLS ONTOLOGY
Skills Ontology is a rich database of
skills with the skills being organized in
a manner whereby it becomes easy to
get an understanding of the context of
skills and relationships between skills
BEFORE WE START
itsyourskills.com
Domain or contextual
experience
Functional/
Technical skills
Behavioral
Skills
Knowledge (of say
concepts, methods,
standards etc.)
Activities Certifications
By the word skills we refer to terms covering:
Facets of IYS Skills Ontology
NORMALIZATION OF TERMS
Acronyms – Supply Chain Management = SCM
Synonyms – Internet Marketing = Web Marketing
Spelling Differences – Dot Net = .Net
As of 25 Jan 2020 there
are 130,000 terms in the
skills ontology
Terms that are Acronyms or Synonyms or Used with a different
spelling are normalized for common interpretation when same
term is used differently by different people
itsyourskills.com
E X A M P L E S
Facets of IYS Skills Ontology
CATEGORIZATION
Skills terms are categorized into logical groups or same family
E
X
A
M
P
L
E
S
C++, Java and others are categorized
under Programming Languages
Workflow Study, Productivity Analysis
and others are categorized under
Industrial Engineering
As of 25 Jan 2020 there
are 3,647 categories of
skills terms
itsyourskills.com
Facets of IYS Skills Ontology
HIERARCHICAL ASSOCIATIONS
Skills terms and skills categories
are hierarchically associated
(similar to states under countries
and cities under states).
Hierarchical associations give
a good understanding when
drilled down from higher to
lower level
Machine Learning Algorithm Bayesian Algorithms Naïve Bayes
The hierarchical
levels range from 3 to
7 for different skills
itsyourskills.com
E X A M P L E S
Facets of IYS Skills Ontology
GRAPHICAL ASSOCIATIONS
Skills terms and skills
categories are graphically
associated (Roger is friend of
Peter and Peter is friend of
Julia so there is probability
that Julia is a friend of Peter).
Graphical networks are very
useful in creating holistic
skills profile
itsyourskills.com
Salesmen activities include customer
relationship management and thus CRM
tools are associated with Salesmen
There are over 50,000 graphical
associations in the skills ontology
E X A M P L E S
itsyourskills.com
Facets of IYS Skills Ontology
RATING SCALE LEGENDS
Behavioral skills are assigned rating legend – Needs
Improvement, Acceptable, A Strength, Stands Out Using
of Graphic Design Tools is assigned rating legend –
Aware, Familiar, Proficient, Expert
In order to bring out proficiency levels in skills a four scale rating is
used. The rating scale legend is different for different categories of
skills as would be appropriate for the skills
E X A M P L E S
itsyourskills.com
Facets of IYS Skills Ontology
PROXY TERMS
Under Playing Musical Instrument
category we may have Guitar and Drums.
But the proxy terms used in search for
these are Playing Guitar and Playing Drums
and not just Guitar or Drums
Skills space is unique in the sense
the way a skill is written maybe
different from the way it is used in
conversations (or searched in a
database). Skills are assigned
proxies to enable proper meaning to
the skill in search
E X A M P L E S
Facets of IYS Skills Ontology
CONTEXTUAL REFERENCES
True sense of skills come from their contextual reference. Same
word can refer to different skill based on the context often derived
from use of a preceding or following word or term
itsyourskills.com
Driving cars, Designing cars, Repairing cars are different skills derived
around cars derived from the prefixes used with cars
E X A M P L E S
DATA MODEL OF
SKILLS PROFILE
Associations
Where terms are associated
hierarchically as well as graphically.
Also different properties such as rating
legend are assigned to terms here
Unique Terms
Where all the terms are captured and
inter related if they are acronyms or
synonyms or spelling variations of same
term
Categorization
Where terms are grouped into categories
The Skills Ontology
database has three layers:
itsyourskills.com
FINALLY,
CONSTANT UPDATION OF
SKILLS ONTOLOGY
The Skills Ontology is constantly updated in three ways
User Contribution + IYS Governance – Users
of Skills Profiler can contribute skills not in
skills ontology. IYS will screen them before
including them in the skills ontology
Curation using Data Mining, Screening and
recommendations using Machine Learning
and NLP, Inclusion of terms in Ontology
using Human Intelligence
Secondary Research
itsyourskills.com
i t s y o u r s k i l l s . c o m
THANK YOU

More Related Content

What's hot

Transfer Learning: An overview
Transfer Learning: An overviewTransfer Learning: An overview
Transfer Learning: An overviewjins0618
 
Python and Machine Learning
Python and Machine LearningPython and Machine Learning
Python and Machine Learningtrygub
 
Types of machine learning
Types of machine learningTypes of machine learning
Types of machine learningHimaniAloona
 
Machine Learning and its types - Internship Presentation - week 8
Machine Learning and its types - Internship Presentation - week 8Machine Learning and its types - Internship Presentation - week 8
Machine Learning and its types - Internship Presentation - week 8Devang Garach
 
Top Libraries for Machine Learning with Python
Top Libraries for Machine Learning with Python Top Libraries for Machine Learning with Python
Top Libraries for Machine Learning with Python Chariza Pladin
 
Chapter 04 object oriented programming
Chapter 04 object oriented programmingChapter 04 object oriented programming
Chapter 04 object oriented programmingPraveen M Jigajinni
 
Introducing machine learning to kids
Introducing machine learning to kidsIntroducing machine learning to kids
Introducing machine learning to kidsDale Lane
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & ReasoningSajid Marwat
 
AI, Machine Learning and Deep Learning - The Overview
AI, Machine Learning and Deep Learning - The OverviewAI, Machine Learning and Deep Learning - The Overview
AI, Machine Learning and Deep Learning - The OverviewSpotle.ai
 
Machine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachinePulse
 
Python interview questions
Python interview questionsPython interview questions
Python interview questionsPragati Singh
 
فروض البحث
فروض البحثفروض البحث
فروض البحثsasso_ik
 

What's hot (17)

CS3391 -OOP -UNIT – I NOTES FINAL.pdf
CS3391 -OOP -UNIT – I  NOTES FINAL.pdfCS3391 -OOP -UNIT – I  NOTES FINAL.pdf
CS3391 -OOP -UNIT – I NOTES FINAL.pdf
 
Transfer Learning: An overview
Transfer Learning: An overviewTransfer Learning: An overview
Transfer Learning: An overview
 
Rule Based System
Rule Based SystemRule Based System
Rule Based System
 
Python and Machine Learning
Python and Machine LearningPython and Machine Learning
Python and Machine Learning
 
2021 05-04-u2-net
2021 05-04-u2-net2021 05-04-u2-net
2021 05-04-u2-net
 
Types of machine learning
Types of machine learningTypes of machine learning
Types of machine learning
 
Machine Learning and its types - Internship Presentation - week 8
Machine Learning and its types - Internship Presentation - week 8Machine Learning and its types - Internship Presentation - week 8
Machine Learning and its types - Internship Presentation - week 8
 
Top Libraries for Machine Learning with Python
Top Libraries for Machine Learning with Python Top Libraries for Machine Learning with Python
Top Libraries for Machine Learning with Python
 
Chapter 04 object oriented programming
Chapter 04 object oriented programmingChapter 04 object oriented programming
Chapter 04 object oriented programming
 
Introducing machine learning to kids
Introducing machine learning to kidsIntroducing machine learning to kids
Introducing machine learning to kids
 
Knowledge Representation & Reasoning
Knowledge Representation & ReasoningKnowledge Representation & Reasoning
Knowledge Representation & Reasoning
 
Knowledge Management - إدارة المعرفة
Knowledge Management - إدارة المعرفةKnowledge Management - إدارة المعرفة
Knowledge Management - إدارة المعرفة
 
AI, Machine Learning and Deep Learning - The Overview
AI, Machine Learning and Deep Learning - The OverviewAI, Machine Learning and Deep Learning - The Overview
AI, Machine Learning and Deep Learning - The Overview
 
Machine Learning and Real-World Applications
Machine Learning and Real-World ApplicationsMachine Learning and Real-World Applications
Machine Learning and Real-World Applications
 
Python interview questions
Python interview questionsPython interview questions
Python interview questions
 
فروض البحث
فروض البحثفروض البحث
فروض البحث
 
Lecture 2
Lecture 2Lecture 2
Lecture 2
 

Similar to Skills ontology

LinkedIn Skills: RecSys Conference 2014
LinkedIn Skills: RecSys Conference 2014LinkedIn Skills: RecSys Conference 2014
LinkedIn Skills: RecSys Conference 2014Mathieu Bastian
 
2015-User Modeling of Skills and Expertise from Resumes-KMIS
2015-User Modeling of Skills and Expertise from Resumes-KMIS2015-User Modeling of Skills and Expertise from Resumes-KMIS
2015-User Modeling of Skills and Expertise from Resumes-KMISHua Li, PhD
 
Overview of Taxonomies and Artificial Intelligence
Overview of Taxonomies and Artificial IntelligenceOverview of Taxonomies and Artificial Intelligence
Overview of Taxonomies and Artificial IntelligenceEnterprise Knowledge
 
The need for sophistication in modern search engine implementations
The need for sophistication in modern search engine implementationsThe need for sophistication in modern search engine implementations
The need for sophistication in modern search engine implementationsBen DeMott
 
Employee Skills Management SAAS Application
Employee Skills Management SAAS ApplicationEmployee Skills Management SAAS Application
Employee Skills Management SAAS ApplicationRamu Govindan
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
Veda Semantics - introduction document
Veda Semantics - introduction documentVeda Semantics - introduction document
Veda Semantics - introduction documentrajatkr
 
Semantic Search_ NLP_ ML.pdf
Semantic Search_ NLP_ ML.pdfSemantic Search_ NLP_ ML.pdf
Semantic Search_ NLP_ ML.pdfPlamenaDzharadat
 
Boost Your Text Analytics Accuracy - MeaningCloud Webinar
Boost Your Text Analytics Accuracy - MeaningCloud WebinarBoost Your Text Analytics Accuracy - MeaningCloud Webinar
Boost Your Text Analytics Accuracy - MeaningCloud WebinarMeaningCloud
 
Diagrammatic knowledge modeling for managers – ontology-based approach
Diagrammatic knowledge modeling for managers  – ontology-based approachDiagrammatic knowledge modeling for managers  – ontology-based approach
Diagrammatic knowledge modeling for managers – ontology-based approachDmitry Kudryavtsev
 
Resume_Clasification.pptx
Resume_Clasification.pptxResume_Clasification.pptx
Resume_Clasification.pptxMOINDALVS
 
Taxonomy_DDTA2016
Taxonomy_DDTA2016Taxonomy_DDTA2016
Taxonomy_DDTA2016Tyler Mehay
 
Semantics In Declarative Systems
Semantics In Declarative SystemsSemantics In Declarative Systems
Semantics In Declarative SystemsOptum
 
Semantic Search for Sourcing and Recruiting
Semantic Search for Sourcing and RecruitingSemantic Search for Sourcing and Recruiting
Semantic Search for Sourcing and RecruitingGlen Cathey
 
Skillset Talent Management System [Dec. 3 Community Call]
Skillset Talent Management System [Dec. 3 Community Call]Skillset Talent Management System [Dec. 3 Community Call]
Skillset Talent Management System [Dec. 3 Community Call]Open Badges
 
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for Everyone
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneGDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for Everyone
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneJames Anderson
 

Similar to Skills ontology (20)

LinkedIn Skills: RecSys Conference 2014
LinkedIn Skills: RecSys Conference 2014LinkedIn Skills: RecSys Conference 2014
LinkedIn Skills: RecSys Conference 2014
 
Ontology
OntologyOntology
Ontology
 
2015-User Modeling of Skills and Expertise from Resumes-KMIS
2015-User Modeling of Skills and Expertise from Resumes-KMIS2015-User Modeling of Skills and Expertise from Resumes-KMIS
2015-User Modeling of Skills and Expertise from Resumes-KMIS
 
Overview of Taxonomies and Artificial Intelligence
Overview of Taxonomies and Artificial IntelligenceOverview of Taxonomies and Artificial Intelligence
Overview of Taxonomies and Artificial Intelligence
 
The need for sophistication in modern search engine implementations
The need for sophistication in modern search engine implementationsThe need for sophistication in modern search engine implementations
The need for sophistication in modern search engine implementations
 
Iys skills api
Iys skills api Iys skills api
Iys skills api
 
Employee Skills Management SAAS Application
Employee Skills Management SAAS ApplicationEmployee Skills Management SAAS Application
Employee Skills Management SAAS Application
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
Veda Semantics - introduction document
Veda Semantics - introduction documentVeda Semantics - introduction document
Veda Semantics - introduction document
 
Semantic Search_ NLP_ ML.pdf
Semantic Search_ NLP_ ML.pdfSemantic Search_ NLP_ ML.pdf
Semantic Search_ NLP_ ML.pdf
 
The Scannable Resume
The Scannable ResumeThe Scannable Resume
The Scannable Resume
 
Boost Your Text Analytics Accuracy - MeaningCloud Webinar
Boost Your Text Analytics Accuracy - MeaningCloud WebinarBoost Your Text Analytics Accuracy - MeaningCloud Webinar
Boost Your Text Analytics Accuracy - MeaningCloud Webinar
 
Diagrammatic knowledge modeling for managers – ontology-based approach
Diagrammatic knowledge modeling for managers  – ontology-based approachDiagrammatic knowledge modeling for managers  – ontology-based approach
Diagrammatic knowledge modeling for managers – ontology-based approach
 
Resume_Clasification.pptx
Resume_Clasification.pptxResume_Clasification.pptx
Resume_Clasification.pptx
 
Taxonomy_DDTA2016
Taxonomy_DDTA2016Taxonomy_DDTA2016
Taxonomy_DDTA2016
 
Semantics In Declarative Systems
Semantics In Declarative SystemsSemantics In Declarative Systems
Semantics In Declarative Systems
 
Semantic Search for Sourcing and Recruiting
Semantic Search for Sourcing and RecruitingSemantic Search for Sourcing and Recruiting
Semantic Search for Sourcing and Recruiting
 
IYS Investor Pitch
IYS Investor Pitch IYS Investor Pitch
IYS Investor Pitch
 
Skillset Talent Management System [Dec. 3 Community Call]
Skillset Talent Management System [Dec. 3 Community Call]Skillset Talent Management System [Dec. 3 Community Call]
Skillset Talent Management System [Dec. 3 Community Call]
 
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for Everyone
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for EveryoneGDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for Everyone
GDG Cloud Southlake #17: Meg Dickey-Kurdziolek: Explainable AI is for Everyone
 

More from Ramu Govindan

Skills Ontology and Skills Profiler
Skills Ontology and Skills ProfilerSkills Ontology and Skills Profiler
Skills Ontology and Skills ProfilerRamu Govindan
 
Employee skills management - It's Your Skills
Employee skills management - It's Your SkillsEmployee skills management - It's Your Skills
Employee skills management - It's Your SkillsRamu Govindan
 
Skills Gap Aanalysis
Skills Gap AanalysisSkills Gap Aanalysis
Skills Gap AanalysisRamu Govindan
 
Skills Gap Analysis Using Skills Profiler from Its Your Skills
Skills Gap Analysis Using Skills Profiler from Its Your Skills Skills Gap Analysis Using Skills Profiler from Its Your Skills
Skills Gap Analysis Using Skills Profiler from Its Your Skills Ramu Govindan
 
Enterprise Skills Management
Enterprise Skills ManagementEnterprise Skills Management
Enterprise Skills ManagementRamu Govindan
 
ESM - skills based talent management
ESM - skills based talent managementESM - skills based talent management
ESM - skills based talent managementRamu Govindan
 
ESM - helping you manage skills effectively
ESM - helping you manage skills effectivelyESM - helping you manage skills effectively
ESM - helping you manage skills effectivelyRamu Govindan
 
Map and manage skills for better talent management
Map and manage skills for better talent managementMap and manage skills for better talent management
Map and manage skills for better talent managementRamu Govindan
 

More from Ramu Govindan (9)

Skills Ontology and Skills Profiler
Skills Ontology and Skills ProfilerSkills Ontology and Skills Profiler
Skills Ontology and Skills Profiler
 
It's Your Skills
It's Your SkillsIt's Your Skills
It's Your Skills
 
Employee skills management - It's Your Skills
Employee skills management - It's Your SkillsEmployee skills management - It's Your Skills
Employee skills management - It's Your Skills
 
Skills Gap Aanalysis
Skills Gap AanalysisSkills Gap Aanalysis
Skills Gap Aanalysis
 
Skills Gap Analysis Using Skills Profiler from Its Your Skills
Skills Gap Analysis Using Skills Profiler from Its Your Skills Skills Gap Analysis Using Skills Profiler from Its Your Skills
Skills Gap Analysis Using Skills Profiler from Its Your Skills
 
Enterprise Skills Management
Enterprise Skills ManagementEnterprise Skills Management
Enterprise Skills Management
 
ESM - skills based talent management
ESM - skills based talent managementESM - skills based talent management
ESM - skills based talent management
 
ESM - helping you manage skills effectively
ESM - helping you manage skills effectivelyESM - helping you manage skills effectively
ESM - helping you manage skills effectively
 
Map and manage skills for better talent management
Map and manage skills for better talent managementMap and manage skills for better talent management
Map and manage skills for better talent management
 

Skills ontology

  • 1. SKILLS ONTOLOGY FROM IT’S YOUR SKILLS A s m a r t l a r g e m e n u c a r d o f s k i l l s i t s y o u r s k i l l s . c o m
  • 2. itsyourskills.com WHAT’S THIS SKILLS ONTOLOGY Skills Ontology is a rich database of skills with the skills being organized in a manner whereby it becomes easy to get an understanding of the context of skills and relationships between skills
  • 3. BEFORE WE START itsyourskills.com Domain or contextual experience Functional/ Technical skills Behavioral Skills Knowledge (of say concepts, methods, standards etc.) Activities Certifications By the word skills we refer to terms covering:
  • 4. Facets of IYS Skills Ontology NORMALIZATION OF TERMS Acronyms – Supply Chain Management = SCM Synonyms – Internet Marketing = Web Marketing Spelling Differences – Dot Net = .Net As of 25 Jan 2020 there are 130,000 terms in the skills ontology Terms that are Acronyms or Synonyms or Used with a different spelling are normalized for common interpretation when same term is used differently by different people itsyourskills.com E X A M P L E S
  • 5. Facets of IYS Skills Ontology CATEGORIZATION Skills terms are categorized into logical groups or same family E X A M P L E S C++, Java and others are categorized under Programming Languages Workflow Study, Productivity Analysis and others are categorized under Industrial Engineering As of 25 Jan 2020 there are 3,647 categories of skills terms itsyourskills.com
  • 6. Facets of IYS Skills Ontology HIERARCHICAL ASSOCIATIONS Skills terms and skills categories are hierarchically associated (similar to states under countries and cities under states). Hierarchical associations give a good understanding when drilled down from higher to lower level Machine Learning Algorithm Bayesian Algorithms Naïve Bayes The hierarchical levels range from 3 to 7 for different skills itsyourskills.com E X A M P L E S
  • 7. Facets of IYS Skills Ontology GRAPHICAL ASSOCIATIONS Skills terms and skills categories are graphically associated (Roger is friend of Peter and Peter is friend of Julia so there is probability that Julia is a friend of Peter). Graphical networks are very useful in creating holistic skills profile itsyourskills.com Salesmen activities include customer relationship management and thus CRM tools are associated with Salesmen There are over 50,000 graphical associations in the skills ontology E X A M P L E S
  • 8. itsyourskills.com Facets of IYS Skills Ontology RATING SCALE LEGENDS Behavioral skills are assigned rating legend – Needs Improvement, Acceptable, A Strength, Stands Out Using of Graphic Design Tools is assigned rating legend – Aware, Familiar, Proficient, Expert In order to bring out proficiency levels in skills a four scale rating is used. The rating scale legend is different for different categories of skills as would be appropriate for the skills E X A M P L E S
  • 9. itsyourskills.com Facets of IYS Skills Ontology PROXY TERMS Under Playing Musical Instrument category we may have Guitar and Drums. But the proxy terms used in search for these are Playing Guitar and Playing Drums and not just Guitar or Drums Skills space is unique in the sense the way a skill is written maybe different from the way it is used in conversations (or searched in a database). Skills are assigned proxies to enable proper meaning to the skill in search E X A M P L E S
  • 10. Facets of IYS Skills Ontology CONTEXTUAL REFERENCES True sense of skills come from their contextual reference. Same word can refer to different skill based on the context often derived from use of a preceding or following word or term itsyourskills.com Driving cars, Designing cars, Repairing cars are different skills derived around cars derived from the prefixes used with cars E X A M P L E S
  • 11. DATA MODEL OF SKILLS PROFILE Associations Where terms are associated hierarchically as well as graphically. Also different properties such as rating legend are assigned to terms here Unique Terms Where all the terms are captured and inter related if they are acronyms or synonyms or spelling variations of same term Categorization Where terms are grouped into categories The Skills Ontology database has three layers: itsyourskills.com
  • 12. FINALLY, CONSTANT UPDATION OF SKILLS ONTOLOGY The Skills Ontology is constantly updated in three ways User Contribution + IYS Governance – Users of Skills Profiler can contribute skills not in skills ontology. IYS will screen them before including them in the skills ontology Curation using Data Mining, Screening and recommendations using Machine Learning and NLP, Inclusion of terms in Ontology using Human Intelligence Secondary Research itsyourskills.com
  • 13. i t s y o u r s k i l l s . c o m THANK YOU