7 Interpreting query intent through semantic search

 

This chapter covers

  • The mechanics of query interpretation
  • Implementing an end-to-end query intent pipeline to parse, enrich, transform, and search
  • Tagging and classifying query terms and phrases
  • Augmenting queries using knowledge graph traversals
  • Interpreting the semantics of domain-specific query patterns

In chapters 5 and 6, we used content and signals to interpret the domain-specific meaning of incoming user queries. We discussed phrase identification, misspelling detection, synonym discovery, query intent classification, related-terms expansion, and even query-sense disambiguation. We mostly discussed these techniques in isolation to demonstrate how they each work independently.

In this chapter, we’ll put all those techniques into practice, integrating them into a unified query interpretation framework. We’ll show an example search interface that accepts real queries, interprets them, rewrites them to better express the end user’s intent, and then returns ranked results.

7.1 The mechanics of query interpretation

 
 
 
 

7.2 Indexing and searching on a local reviews dataset

 
 
 
 

7.3 An end-to-end semantic search example

 
 
 

7.4 Query interpretation pipelines

 
 
 
 

7.4.1 Parsing a query for semantic search

 
 

7.4.2 Enriching a query for semantic search

 

7.4.3 Sparse lexical and expansion models

 
 

7.4.4 Transforming a query for semantic search

 
 
 
 

7.4.5 Searching with a semantically enhanced query

 

Summary

 
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