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).

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