Introducing Semantic Capability in LinkedIn's Content Search Engine

TL;DR

This paper presents the design of LinkedIn's new content search engine with semantic capabilities, enabling it to better understand and respond to complex natural language queries, improving search effectiveness.

Contribution

The paper introduces a semantic extension to LinkedIn's content search engine, enhancing its ability to handle complex natural language queries compared to traditional keyword-based search.

Findings

Improved search relevance metrics

Enhanced understanding of natural language queries

Positive impact on user engagement

Abstract

In the past, most search queries issued to a search engine were short and simple. A keyword based search engine was able to answer such queries quite well. However, members are now developing the habit of issuing long and complex natural language queries. Answering such queries requires evolution of a search engine to have semantic capability. In this paper we present the design of LinkedIn's new content search engine with semantic capability, and its impact on metrics.