Semantic search is search driven by meaning. Instead of matching the exact words in your query, a semantic search engine understands what you are asking for and returns results that mean the same thing, even when they use different words. Keyword search, by contrast, returns only the results that contain your exact terms. The difference sounds subtle. In practice it decides whether you find the moment you need or scroll past it.
This guide explains the difference in plain terms, clears up how semantic search relates to vector search and lexical search, and shows what all of this means for video, where the thing you are searching for is often something nobody ever wrote down.
What keyword search does
Keyword search, also called lexical search, matches the literal words in your query against the words in a document, a transcript or a tag. Search for couch and you get results that contain the word couch. It is fast, predictable and easy to build, which is why it has powered search boxes for decades.
This is the engine behind most search boxes, including the ones inside MAM and DAM systems, typically built on an inverted index like Elasticsearch or Lucene. Fuzzy matching softens the edges, but fuzzy search fixes spelling, not meaning. A search for Volvvo still finds Volvo. A search for couch still misses sofa.
The weakness is that language rarely cooperates. If the transcript says sofa, a keyword search for couch returns nothing. If a video shows a couch but nobody says the word, keyword search cannot find it at all. Teams compensate by tagging content manually, which is slow, inconsistent and never finished.
That tagging dependency is an operations problem in its own right. Keyword search is only as good as the index behind it, and for video that index is built by manual or semi-manual logging. Someone generates the metadata, someone else runs quality control, and a governance layer keeps the controlled vocabulary consistent, often across several passes over the same footage. The cost compounds as the library grows, because every new naming rule has to be applied backward to everything already tagged, and the backlog is never really finished. Most of that labeling also happens at asset level, one set of tags for an entire file, so even a perfectly tagged asset tells you nothing about where inside it the moment actually lives.
Consistency is the harder part. Different teams and business divisions name the same moment differently. A promo team’s key moment is an editor’s selects and a rights team’s usage event. Inter-annotator agreement is low even within a single team, and it drops further when teams work in different languages and cultural contexts, where the same scene is described with entirely different words. Synonym rings and thesauri patch some of this, but they are one more asset to govern. All of that infrastructure exists to feed keyword search a vocabulary it can match against, which is why replacing the matching model changes the economics of the whole pipeline.
What semantic search does
Semantic search converts both your query and the content into mathematical representations of meaning, often called embeddings. Instead of asking whether the words match, it asks whether the meanings are close. A search for couch also surfaces sofa. A search for celebration finds the confetti drop, the champagne toast and the team jumping on the field, because all of them sit near celebration in meaning.
Done right, semantic understanding also starts at shot level and works its way up, shot to scene to asset. That means a match points at the exact moment inside the file, not at a whole two-hour asset that contains it somewhere.
That gives you three things keyword search cannot deliver. You get exact matches plus results that are semantically close to your query. You get topic-level results, where searching for a theme finds moments that discuss it in different words. And you get resilience to phrasing, so two people asking the same question differently find the same answer.
Semantic search vs vector search vs lexical search
These terms overlap and are often used loosely. Lexical search is the formal name for keyword matching. Vector search is a retrieval technique, the mechanics of finding items whose embeddings sit close together in mathematical space. Semantic search is the user-facing capability, search by meaning, and vector search is the most common way to implement it. In other words, vector search is how, semantic search is what. Many production systems are hybrid, running lexical and semantic retrieval together and blending the results, so exact matches always rank and meaning fills in the rest.
Keyword search vs semantic search at a glance
| Keyword search | Semantic search | |
|---|---|---|
| Matches on | Exact words | Meaning |
| Synonyms | Missed unless tagged | Found automatically |
| Topics and themes | Only if the words appear | Found by conceptual closeness |
| Phrasing changes | Different results | Same results |
| Setup for video | Manual tags or transcripts | Indexing, no manual tagging |
| Best for | Known exact strings, IDs, names | Finding moments, ideas and topics |
Five examples: keyword search vs semantic search
Take a simple query: red Volvo. Keyword search treats it as two independent words. It happily returns a red dress, because red matched, and a blue Volvo, because Volvo matched. Semantic search understands that red describes the Volvo, so it returns the red car and nothing else. Here is how that plays out across the footage real teams work with.
Query: CEO announcing the partnership on stage
Clips where the transcript contains the word partnership, including a hallway interview and the word on a slide. If the CEO says joining forces instead, the announcement itself never comes up.
The moment on stage, even when the phrasing is different, because the speaker, the setting and the meaning of what is said all match the query.
Query: contestants celebrating a win
Moments where someone literally says celebrate or win, which is often people talking about winning rather than the win itself.
The cheering, the group hug and the confetti drop, including scenes with no dialogue at all.
Query: tense dinner scene
Every scene where a character mentions dinner, whatever the mood. Tension is invisible to a word match.
The argument at the dining table, found through the setting, the mood and the audiovisual language of the scene.
Query: goalkeeper save in the closing minutes
Only the moments where the commentator says save. In a tight finish, commentary usually shouts names instead.
The dive itself, recognized visually as an action and ranked alongside other late-game moments.
Query: witness describing the storm damage
Any clip containing storm or damage, including the weather forecast and the anchor reading the intro.
The street interview where a person describes what happened to their home, in their own words.
Why this matters more for video than for text
Text search has one modality, the words on the page. Video has at least three. What is said, what is shown and what is heard all carry meaning, and most of it never appears in any transcript or tag. A drone shot of a coastline at sunset contains no words at all. A CEO giving a speech at a conference is defined as much by the stage and the audience as by the sentences spoken.
Semantic video search indexes all of it. At Imaginario, speech analysis, meaning transcriptions, captions, chapters and much more, covers more than 100 languages and dialects with automatic language detection, and search is available natively in 17 languages, so you can search for phrases, words or topics in Spanish, for example. Queries can also combine modalities, like a person plus an action plus a phrase, to pinpoint one exact moment in thousands of hours of footage.
Natural language search is the interface
Natural language search means typing the way you speak. Instead of guessing the right tag, you write two people arguing in a kitchen or our founder talking about pricing, and the engine does the interpretation. Semantic search is what makes natural language search work, because a conversational query almost never matches the literal words in the content. Together they remove the last piece of training every search box used to require, learning how the system wants to be asked.
How Imaginario runs semantic search on video
Imaginario runs two AI engines. The first is contextual understanding, which powers temporal semantic search across dialogue, visuals, people, and audio with no tags or metadata needed. The second generates enriched, shot-level metadata and descriptions for teams whose workflows run on it, mapped to their own taxonomy and schema. You can read more about both on the two engines and our AI systems pages.
Frequently asked questions
What is semantic search?
Semantic search is search based on the meaning of a query rather than its exact words. It represents queries and content as embeddings, compares them by conceptual closeness, and returns exact matches plus results that mean the same thing in different words.
Is semantic search the same as vector search?
No. Vector search is the retrieval technique, finding items whose embeddings are close together. Semantic search is the capability it enables, search by meaning. Most semantic search systems use vector search under the hood, often blended with lexical matching in a hybrid setup.
What is the difference between lexical and semantic search?
Lexical search matches literal words, so results must contain the query terms. Semantic search matches meaning, so synonyms, paraphrases and related topics are found even when the words differ.
Does semantic search replace metadata?
Not necessarily. Semantic search removes the need for manual tagging to make content findable. Many teams still want structured metadata for rights, compliance and MAM or DAM workflows, which is why Imaginario pairs semantic search with an enrichment engine that generates shot-level metadata mapped to your taxonomy.
Does semantic search work in languages other than English?
Yes. Imaginario supports speech analysis in more than 100 languages and dialects with automatic language detection, and search is available natively in 17 languages.