Textkernel's technology - Learning to Rank and Ontology Mining

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High quality semantic search and match means highly relevant results and smart recommendations. The modern approach towards tuning a complex ranking function is called Learning to Rank (LTR).

Business

Textkernel’s technologies
Learning to Rank
Ontology Mining

Learning to Rank
High quality semantic search and match means highly relevant
results and smart recommendations. The modern approach
towards tuning a complex ranking function is called Learning to
Rank (LTR).

Introduction to Learning to Rank
How can you give a match on location a higher score when searching for
construction workers, compared with when searching for IT professionals?
Can you use user’s feedback to improve the matching results?
Can you personalize the result for every single users?
Learning to rank (LTR) refers to training a reranking model. Machine
learning can discover elaborate and non-linear dependencies in the data
and use them to generate models that can improve the relevance of search
results beyond what can be conceived by human inspection.

LTR - The result
20% - 50% improvement
Customizable to the
customer’s system
For more information read the full blog post

Ontology Mining
Searching for things not strings

Introduction to Ontology Mining
What exactly is a knowledge graph and what form and content does it have
in the case of Textkernel?
What approach is used to construct it in a scalable, time-effective way that
keeps the quality high?
What benefits does it bring to the technology and, consequently, business
and customers of Textkernel?

For more information read the
full blog post

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Maakt Artificial Intelligence het werk van de recruiter straks overbodig? In tegendeel! In een krapper wordende arbeidsmarkt wordt recruitment en tijd voor de kandidaat steeds belangrijker. In deze presentatie geven we een korte introductie over AI en laten we zien waarom het juist voor recruitment belangrijk is en hoe het je helpt beter te sourcen en te matchen. We sluiten af met interessante klantcases van o.a. USG People en CERN.

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This presentation was held by Martin le Vrang at Textkernel's conference Intelligent Machines and the Future of Recruitment on 2 June in Amsterdam. The European Commission is developing a multilingual classification of European Skills, Competences, Qualifications and Occupations (ESCO). This common reference terminology will enhance the functioning of the labour market, help to build an integrated European labour market and bridge the communication gap between work and education/training. ESCO is part of an emerging Semantic Web in the labour market and the education and training sector. Job vacancies, CVs and training curricula would no longer just be documents, but standardised sets of data which can be reused in job matching, HR systems, for career guidance tools or in statistical applications.

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Pablo de Pedraza's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016. The number of job openings, or vacancies, is an important indicator of the state of the economy and the labour market. They are extensively used by institutions and in academic papers to calculate the Beveridge Curve or estimate the matching function, center pieces of macroeconomic models studying labor markets. Vacancies can be measured using administrative registers, surveys to employers, advertisements in printed press or using online advertising. This presentation is divided into two sections. In the first one we study the Dutch Beveridge curve and the matching function using the number of vacancies inferred from a survey to employers conducted by the Dutch Central Bureau of Statistics (CBS) from 1997 until the end of 2014. We obtain conclusion about matching process before and after the Great Recession. In the second section we compare number of vacancies inferred from CBS vacancy data with the number of vacancies posted online. According to CBS data, the number of vacancies increases during positive shocks and goes down during negative ones. We can observe the number of web vacancies posted online from 2006 until today and compare them with CBS data during a complete economic cycle. Results show a positive time trend in the number of online vacancies and negative time trend in the number of vacancies inferred from a survey. We show that both series reflect very similar economic reality once we account for both trends. We settle our future research lines focusing on exploring the sources behind both trends and how they compare across sectors.

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Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important: 1) Learning to interact with the world through reinforcement learning, 2) Learning while respecting everyone's privacy, and 3) Learning the causal relations in data (as opposed to discovering mere correlations). Together, they represent the "power tools" of the future machine learner.

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Presentation from Prof. Dr. Armin Trost, Author, Consultant and Professor at Furtwangen University, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Human resource management in the 21st century will have little to do with what has been promoted in recent years or decades and written in the text-books. Instead of finding “the right people, at the right time and at the right place” we will make the employees and their individual preferences, talents, life plans, and ambitions the focus of attention. We will say goodbye to mechanistic, technocratic, and often bureaucratic approaches. They fit in a past that was stable and predictable. If you regard your employees as your most valuable asset, you will give them freedom, trust, and responsibility. Moreover you will appreciate individuality and individual life-plans. Human resources management will therefore deal less with hierarchical processes, systems, responsibilities, KPIs, etc., in the future. Rather, it will be about how to empower teams to think on their own responsibility, communicate, collaborate, learn, and develop their talent in the long term. HR-Technology will be there to make the life of managers and employees easier instead of supporting the HR-function only. For instance, in the area of recruiting all this will lead to a more intense usage of social networks, artificial intelligence, big data, data mining etc.

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Textkernel's technology - Learning to Rank and Ontology Mining

1. Textkernel’s technologies Learning to Rank Ontology Mining

2. Learning to Rank High quality semantic search and match means highly relevant results and smart recommendations. The modern approach towards tuning a complex ranking function is called Learning to Rank (LTR).

3. Introduction to Learning to Rank How can you give a match on location a higher score when searching for construction workers, compared with when searching for IT professionals? Can you use user’s feedback to improve the matching results? Can you personalize the result for every single users? Learning to rank (LTR) refers to training a reranking model. Machine learning can discover elaborate and non-linear dependencies in the data and use them to generate models that can improve the relevance of search results beyond what can be conceived by human inspection.

5. LTR - The result 20% - 50% improvement Customizable to the customer’s system For more information read the full blog post

6. Ontology Mining Searching for things not strings

7. Introduction to Ontology Mining What exactly is a knowledge graph and what form and content does it have in the case of Textkernel? What approach is used to construct it in a scalable, time-effective way that keeps the quality high? What benefits does it bring to the technology and, consequently, business and customers of Textkernel?

8. For more information read the full blog post

Textkernel's technology - Learning to Rank and Ontology Mining

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Textkernel's technology - Learning to Rank and Ontology Mining