Semantic Search
Accelerating insight with AI-powered search and knowledge retrieval
Justice system staff frequently rely on large volumes of unstructured information, from operational guidance to complex case files, but traditional search tools often struggle to surface what's most relevant.
Semantic search uses AI to understand the meaning, context, and relationships within text. Unlike basic keyword search, it can interpret synonyms, abbreviations, and misspellings to provide results that match user intent.
This helps frontline staff find what they need faster, reducing delays, increasing decision quality, and improving service delivery.
We are now scaling semantic and hybrid search tools across justice services to reduce inefficiencies, enhance operational confidence, and improve outcomes for users and the public.
Traditional vs. Semantic Search
Traditional Search
- •Matches exact keywords only
- •Misses synonyms and related concepts
- •Sensitive to spelling and abbreviations
- •Often requires multiple search attempts
Semantic Search
- •Understands meaning and context
- •Recognizes synonyms and related concepts
- •Tolerant of spelling errors and abbreviations
- •Delivers relevant results from first search
Impact
Better Results
More relevant information from the first search attempt
Informed Decisions
Access to complete information improves decision quality
Time Savings
More time for high-value work like risk management
Case Study: Smarter searches for probation staff
Probation officers were spending excessive time trying multiple keyword combinations to find relevant information, a process known as "search flurries."
To solve this, MoJ Data Science deployed a semantic search tool powered by a Large Language Model (LLM) within the Probation Digital System. The tool understands context, language variation, and abbreviations, returning much more relevant results from the start.
The result: faster, more accurate access to information, improving the ability of probation officers to manage risk and support rehabilitation.
Case Study: Knowledge assistant for courts and tribunals staff
Frontline court staff need to access detailed operational procedures quickly, but with hundreds of guidance documents available, information is often buried.
We piloted a generative AI knowledge assistant that allows staff to ask natural-language questions. The tool searches over 300 documents and returns a concise summary with a source citation.
Evaluation showed court staff accessed key information more quickly, improving the speed and accuracy of case administration. We are now exploring how to scale the solution.