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How did LinkedIn use knowledge graphs to solve the job title problem?

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Drawn from Lutz Finger's Forbes column, LinkedIn writing, and Cornell teaching. Sources are cited inline so you can read the originals.

Structuring 150 million job titles required more than simple categorization.

LinkedIn had over 150 million unique job title strings, which was impossible to model effectively. We decomposed titles into four facets: seniority, employment status, role, and specialty. For example, Senior Software Engineer contains seniority information, a role, and a specialty. We then built knowledge graphs for each dimension to show how values interrelated. This helped us understand completeness and vagueness of titles. A VP at a bank means something completely different than a VP at a tech company. Taxonomies alone were not expressive enough to capture these nuances.

The synergy between LLMs and knowledge graphs · The Edge


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