Semantic Matching of candidates’ profile with job data from L inkedln

1. SemanticMatchingofcandidates’profilewithjobdatafromLinkedlnSemanticMatchingofcandidates’profilewithjobdatafromLinkedln PRESENTED BY: TING XIAO SARABPREET KAUR DHILLON

2. Introduction • Research Questions • Literature Survey • System Architecture • Project Scope • Work division • Future Goals Table of Contents

3. The Internet has already evolved into the primary medium for recruitment and employment processes. Most of recently filled positions in US are the result of online job posting and according to most forecasts this share is likely to increase further. Introduction

4. General websites(e.gFreeResumeWeb) say they collect the hot jobs from different job sites provide more job information. The International Association of Employment Web Sites reports that there are more than 40,000 employment sites serving job seekers, employers and recruiters worldwide. • Many professionals use social network LinkedIn as a job-hunting tool, and many companies use it for hiring. Introduction-Today

5. Introduction-Future

6. But, the amount of job data returned in the results is huge, which means that the job-hunters need more time to scan through those jobs. • The “Big Data” is too powerful for individual to use? Research Questions

7. Example: “software developer” searched on LinkedIn • Job searching • Job matching CurrentSituation

8. Latest posted Job vacancies will be found first, what about the “older” ones? • So much of the job information after searching in different job portals, how to filter those? • Just adding more search constraints does not help. • How to deal with the overlapping information amongst the different datasets (LinkedIn, Glassdooretc)? • If you have no professional connections in LinkedIn, the search results will be different, which one is good for the jobseeker? Problems

9. "People are impatient, even lazy, " Dickey-Chasins observes. "They don't want to spend 10, 20 or even 30 minutes filling out forms for an uncertain return.”http://articles.chicagotribune.com/2012-04-29/jobs/sns-201204251700--tms--careersntp--h-a20120429apr29_1_job-matching-job-seeker-job-boards • Different matchmaking approaches exist in the literature which can be used for matching individuals to job requirements. • For example, typical text-based information retrieval techniques such as database querying and similarity between weighted vectors of terms have been used in previous works (Veit et al, 2006). • (Liu and Dew, 2004) presents a system which integrates the accuracy of concept search with the flexibility of keyword search to match expertise within academia. • (Colucci et al, 2003) proposes a semantic based approach to the problem of skills finding in an ontology supported framework. They use description logic nferences to handle the background knowledge and deal with incomplete knowledge while finding the best individual for a given task or project, based on profile descriptions sharing a common ontology. • Approaches for calculating the structural similarity between instances on the basis of ontologies have also been considered. (Bizer et al, 2005) and (Mochol et al, 2007), for example, present a scenario for supporting the recruitment process with semantic web technologies which uses (Zhong et al, 2002)’s similarity measure to evaluate the degree of match between job offers and applicants. • Semantic Matching for Job Requirement: An ontology based Hybrid approach http://users.soe.ucsc.edu/~darrell/tmp/search_subm06.pdf • The Impact of Semantic Web Technologies on Job Recruitment Processes http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.203.725&rep=rep1&type=pdf Literature Survey

10. Our Idea

11. System details - Data Set

12. Ontologies

13. Sequence Diagram

14. Problem in hand: the calculation of the  degree of semantic similarity  between an applicant's profile and job requirements. • We use the term competence to refer to a skill along with a level of competency. • We do not include skills related to specific tools and technologies. • We define a competency level in terms of the required level of knowledge and years of experience. • We distinguish between four levels of knowledge: basic, intermediate, advanced, and expert knowledge. • The years of experience is specified as the minimum number of years required. • The similarity between two concepts is determined by the distance between them, which reflects their respective positions in the concept hierarchy. • Based on set of rules: Give a high score for sibling instances, a smaller score for cousin instances,  with plus or minus points if the required competency level is satisfied (extra points for experience at or above required competency level etc) SemanticMatching

15. Building an ontology representing user’s job profile. • Getting the job postings data from LinkedIn and other websites. • Using Yago2 to integrate the job posting data with the location information for visualization. • Using Google Maps API for map visualization. • Using Jena as the Java Framework to build the semantic web application. • Implementing the Semantic Matching Engine. Project Scope

16. Ontologies Design - Sarabpreet, Ting • Dataset building - Ting • Yago2 integration. - Sarabpreet • Designing and implementing the Web Interface - Sarabpreet, Ting • Matching Engine - Sarabpreet, Ting Work Division

17. Integrating various datasets – Linkedin, Glassdoor, DawgLink, Monster etc and removing the information overlap. • Adding the Community feature. • Extend the functionality to provide similar matching capabilities to employers looking for suitable candidates. FutureGoals

18. Thank You! Questions?