How does semantic search differ from full-text search?

Full-text search only finds documents that contain the search term letter for letter. Semantic search compares meanings, so it also finds paraphrases, synonyms and passages that are worded differently – which removes the most common reason internal searches come up empty.

Does semantic search also find content in scanned documents?

Yes, as long as the scans have first been turned into searchable text by text recognition (OCR). Good systems do this automatically on import – only then can the content of a scan be included in a search by meaning.

Does semantic search need a tidy folder structure?

No. The search works on what the documents say, not on folder names or how they are filed, so folder structures that have grown up over the years are no obstacle. A little housekeeping is still worthwhile, above all marking or removing out-of-date versions.

Does semantic search also understand Austrian terms?

Largely, yes, because it works on meanings rather than strings of letters. A search for “Karenz” (the Austrian term for parental leave) will usually also find documents that describe the same thing in other words – such as documents from German suppliers or parent companies.

How does semantic search work in documents?

Semantic search looks for documents by meaning rather than by exact wording. If you search for the “holiday policy”, you will also find the document that talks about “leave entitlement” – even though your search term appears nowhere in it. That is what sets it apart from conventional keyword search, which only finds letter-for-letter matches, and it is why semantic search sits at the core of modern AI knowledge bases.

In brief

Semantic search compares meanings, not strings of letters – so it finds synonyms and paraphrases.
Technically, it relies on a kind of map of meanings on which similar content sits close together.
It removes the most common reason internal searches fail: the person searching and the person who wrote the document use different words.
As the first stage of RAG, semantic search supplies the passages the AI uses to write its answer, together with the source.
There are still limits: the search only finds what has been written down, and it cannot tell which of several versions applies.

How does semantic search differ from keyword search?

Conventional keyword search – the kind you find in Windows folders, email programs and many document systems – checks just one thing: does the search term appear in the document or not? That works as long as everyone uses the same terms. In practice, they don’t.

The author of a service report writes “leak at the heat exchanger”; three years later a colleague searches for “heating leaking” and finds nothing. The HR manager files the “annual leave policy”; the new employee searches for “holiday entitlement” and again finds nothing. The knowledge is there – the words simply don’t match.

Search query	Keyword search finds	Semantic search also finds
“holiday policy”	only documents containing this exact term	“leave entitlement”, “annual leave”, “leave request”
“heating leaking”	only exact matches	“leak at the heat exchanger”, “water escaping from the boiler”
“notice period”	only exact matches	“end of employment”, “termination of the contract”

Semantic search closes this gap: it judges how closely a question and a passage match in content, whatever words were used.

How does semantic search work technically – without the maths?

The easiest way to picture it is as a map. On import, each document is split into manageable sections, and each section is given a place on a huge map of meanings. Where a section lands depends on what it says: everything to do with holidays, time off and absence ends up in the same area, whether the text says “holiday”, “vacation” or “time off”.

Technically, this position comes from converting the text into long strings of numbers, known as vectors or embeddings. An AI model that has learned from large amounts of text which terms appear in similar contexts calculates these numbers so that similar content gets similar numbers. The one thing to remember: similar meaning puts content next to each other on the map.

A search then works like this: the question is also given a place on the map, and the system looks at which document sections lie nearby. Those neighbours are the sources, whatever words they happen to use. This also explains why a search takes only a fraction of a second: the map is built once on import, and after that it is simply a matter of looking things up.

What does this look like in everyday work?

Staff who don’t yet know the internal vocabulary benefit most. A new colleague asks about “forklift training” and finds the “instruction for industrial trucks”. Someone covering for a colleague on parental leave searches for the “holiday policy” and gets the document on “annual leave under the collective agreement” – without knowing that is what it is called internally.

Searching by meaning also pays off in day-to-day technical work. A technician describes a problem in his own words – “pressure drop in the underfloor heating” – and finds the service report in which a colleague recorded a “gradual loss of pressure in the heating circuit” years ago. Keyword search would have failed at “pressure drop” versus “loss of pressure”.

A third case is regional variation in language: Austrian terms such as “Karenz” and the wording used in Germany in documents from suppliers or parent companies often mean the same thing. Because semantic search compares meanings, it usually bridges these differences without difficulty.

What does semantic search have to do with AI answers?

Semantic search is the first stage of the method AI knowledge bases use to answer questions: RAG (retrieval-augmented generation). First, the system picks out the sections of the company’s documents that match the question in content – that is the semantic search. Then a language model writes a clear answer from those passages and names the source documents.

So the quality of the search determines the quality of the answer: if the first stage finds the wrong sections, or none at all, even the best writing stage cannot turn them into a correct answer. If you want to understand why free-answering chatbots without this search stage have to guess at company-specific questions, the guide Using ChatGPT with your own data explains how it all fits together.

What are the limits of semantic search?

The most important limit is a mundane one: it can only find what is in the documents. Experience that was never written down stays invisible, and no search technology can change that. Scanned paper documents also have to be turned into searchable text by text recognition (OCR) before their content reaches the map of meanings.

Second, the search finds what is similar, but it does not decide what is valid. If three versions of a work instruction sit side by side, it finds all three – which one currently applies has to be clear from the documents themselves. Marking or removing out-of-date versions is still the company’s job.

Third, the approach has a clear area of use: it excels at looking things up, not at calculating. Questions whose answer only emerges from analysing many documents – such as an overview of all maintenance work in a year – are better handled by structured systems. The tried-and-tested division of labour applies: the ERP knows what happened – the knowledge base knows how it’s done.

Do I need to reorganise my document storage first?

No – and that is one of its underrated advantages. Because semantic search works on content, folder names, folder depth and file names hardly affect what it finds. Folder structures that have grown up over the years, as they have in every company, are no obstacle, and there is no need for a months-long tidy-up before you start.

A little housekeeping still makes sense: mark or archive clearly outdated versions so that the search does not present current and superseded material as equals. You can do this step by step – ideally wherever the first test questions turn up contradictory sources.

The quickest way to see how semantic search works is in a demo, using sample documents from your industry; you then test it on your own documents and real everyday questions in the first weeks after go-live. junova is built on this principle – with the source for every answer and data stored on EU servers. The How it works page explains how the introduction runs, including reviewing the answers to real questions from your team.