Presented at Malaga, Spain, on 13th Nov, 2017 @ICSOC (The 15th International Conference on Service-Oriented Computing) 2017

1. RISE: Resolution of Identity
Through Similarity Establishment
on Unstructured Job Description
Authors: Rakesh Rameshrao Pimplikar1, Kalapriya Kannan2, Abhik Mondal3, Joydeep Mondal1, Sushant
Saxena3, Gyana Parija1 and Chandra Devulapalli4
1 IBM Research Lab, India
2 Hewlett Packard Enterprise, india
3 IBM Research Intern
4 IBM Software Lab, India
Presenter: Joydeep

2. What exactly it is trying to solve?

3. Business
Administrator
Software
Engineer Research
Staff
Member
IBM
Business
Development
Manager
Software
Developer Researcher
Microsoft
Identity Resolution
JobC
JobA
JobB
Unstructured Job Descriptions
are available corresponding to
the jobs

4. Business Application (Where & Why
it is needed?)

5. • IBM Talent Management (https://www.ibm.com/talent-management)
1. Job Data Normalization/sanitation
2. Automated Creation of Job Description with
context preferences
3. Semantic classification of jobs
4. Candidate routing to the appropriate jobs across
the organizations

6. How the problem has been solved?

7. Attribute
Identification
Phase (AIP)
Classifier
Training
Phase (CTP)
Attribute
Association
Phase (AAP)
Extraction
Phase (EP)
Similarity
Phase
Identity
Resolution
Phase (IRP)
RISE

8. AIP
Depth of knowledge, Process, Tools and Technologies, Skills, Domain Knowledge,
Experience, Business Knowledge, Efficiency of communication, Roles, expected to
perform, Performance expected, Project Management, Schedule Management, Training
undergone, Education level, Education streams, Responsibilities
PCA
Qualitative Analysis
by domain experts
Depth of knowledge, Process, Tools and Technologies, Skills, Domain Knowledge,
Experience, Business Knowledge, Efficiency of communication, Roles, expected to
perform, Performance expected, Project Management, Schedule Management, Training
undergone, Education level, Education streams, Responsibilities

9. CTP & AAP
• Multi-Label Classifier Training for labelling every lines of the Job
Descriptions. Apply the classifier on new JD.
• Feature Used : Taxonomy of Categories

10. EP
Text
Standardization
Education
Extraction
Experience
Extraction
Roles, Skills &
Responsibilities
Extraction
Dictionary
Enrichment

11. Similarity Phase
• Jaccard Similarity
• Experience Similarity:
Skills, Roles, Education level, Responsibilities
Overall Similarity :

12. System Overview

13. Experiments
• 27K JDs from 28 different organizations
• Total 0.18 Millions of lines
• Manually tagged 5K lines
• Total ~6K phrases tagged manually
• 10 people built the ground truth
• 86% agreement on total annotations (every line was annotated by 2
people)

14. Results
• Classifier Evaluation:
F1 Score Based Comparison

15. Results
• Keyword Extractor Evaluation:
• Precision: 0.954
• Recall : 0.842
• F1 Score: 0.896
• Overall Similarity Evaluation:
• Precision: 0.88
• Min AU-ROC: 0.642
• Max AU-ROC: 0.9
• Mean AU-ROC: 0.779