Textkernel’s technologiesLearning to Rank Ontology Mining

Learning to RankHigh 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 RankHow 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 MiningSearching for things not strings

Introduction to Ontology MiningWhat 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