Glossary
Semantic Search

Semantic Search

Semantic Search.png

What is Semantic Search?

Semantic search is a used search engine optimization technique that uses natural language processing (NLP) and machine learning (ML) to understand the context of a search query. Here's some terminology:

Natural Language Processing (NLP)

A part of artificial intelligence (AI) that enables computers to understand and process human language. NLP search is used in semantic search to understand what a user is searching for.

Machine Learning (ML)

A type of AI that trains computer algorithms to learn from data and get better over time. ML is used in semantic search to understand the context and search intent, of a search query.

Semantic Understanding

The ability of google search of a computer to understand what a user is searching for. Semantic understanding is the core of semantic search.

What is a Semantic Search Engine?

A Semantic Search Engine (sometimes called a Vector Database) is designed to do a semantic similarity search. Semantic Search Engines will build an index using a specific algorithm and a set of vector embeddings. Milvus has 11 Index options but most Semantic Search Engines only have one (usually HNSW). With the Index and semantic similarity of metrics, you can query for similar items with the Semantic Search Engine.

How to Implement a Semantic Search Engine?

There are several ways to implement Semantic Search:

Python Semantic Search Engine. Build a custom Semantic Search on your own data using Python, a machine learning model and a Vector Index Algorithm like FAISS, HNSW or ANNOY. Here is a tutorial to walk you through how to implement Semantic Search with Facebook AI Similarity Search (FAISS).
Traditional keyword based Search Engines. Solutions like ElasticSearch or AWS OpenSearch have added Vector Search capabilities. The benefit is that you can easily add vector search to a solution already using Elasticsearch.
Database extensions. Popular database solutions like PostgreSQL have added extensions like Pgvector to support vector search. Here is a tutorial to walk you through how to get started using Pgvector.
Vector Databases Use a vector database to implement Semantic Search with various index types and efficient vector embedding querying. With most vector databases, provide FLAT and HSNW to generate the index.

Implementation Challenges and Solutions:

Semantic search has challenges:

Data quality: Clean and well-structured data is key to semantic understanding.
Computational resources: Semantic search uses more resources than keyword search.
Model selection: Choosing the right NLP model for your use case can be tricky.

Solutions: invest in deep learning for data cleaning, use cloud for scalability and test different models to find the best fit for you.

Benefits of a Semantic Search Engine

There are several advantages to performing a semantic search:

Concept-based searching: Semantic search enables you to search for concepts or ideas instead of specific words or phrases, eliminating the need for guesswork in your search queries.
Better query intent understanding: Semantic search can better understand query intent, resulting in more relevant search results for the user.
Enhanced user experience: Users need fewer queries to find what they're looking for, and the results are more accurate.
Personalization: Semantic search can offer personalized search experiences based on user context and behavior.

These benefits contribute to a more efficient and effective search process. In this case study from Lucidworks, you can learn how to build a semantic search solution to see for yourself how this can make search experience for your solution even better.

Keyword Search vs Semantic Search

Typically, keyword search utilizes tools like Elasticsearch to search and rank queried items. When a user conducts a search for keywords, Elasticsearch is queried to rank the outcomes based on the query. Each word in Elasticsearch is stored as a sequence of numbers representing ASCII (or UTF) codes for each letter. Elasticsearch builds an inverted index to identify which documents contain words from the user query quickly. It then uses various scoring algorithms to find the best match among these documents, considering word frequency and proximity factors. However, these scoring algorithms do not consider the meaning of the words but instead focus on their occurrence and proximity. While ASCII representation can convey semantics, there is currently no efficient algorithm for computers to compare the meaning of ASCII-encoded words to search results that are more relevant to the user.

On the other hand, Semantic Search converts unstructured data (emails, images, videos, audio files, web pages, etc) into vector embeddings using Machine Learning models to represent data points as vectors in a high-dimensional space. The next step is to index these embeddings using one of many available algorithms (HNSW, FAISS, ANNOY, etc.). Then, you can conduct a nearest-neighbor search to find items or data points similar or closely related to a given query vector. Unlike a keyword search, semantic search aims to efficiently search and retrieve the most relevant vectors that are similar or nearest to a query vector.

Lexical Search vs Semantic Search

These terms refer to different facets of semantic meaning of language. "Semantic" relates to meaning, while "lexical" refers to vocabulary.

Semantic: It's common for films to be based on popular books, and often, people judge these movies by how well they line up with the images that pop into their heads while reading.
- Semantic refers to understanding the relationships between words and how language interprets meaning.
- It's all about digging into the deep meanings and connections that words or parts of a sentence have with each other.
- In semantics, one might explore how the word "bank" can refer to a financial institution or the side of a river, depending on the context.
Lexical: Lexical relates to the vocabulary or words of a language. It's all about digging into single words - their shapes, what they mean, and how we throw them around when we talk.
- Lexical analysis examines the structure, usage, spelling, pronunciation, and meaning of individual words within a language.
- Example: In lexical analysis, one might examine the variations of a word (e.g., "run," "ran," "running") and how these variations contribute to the overall meaning.

Let's break this down. Think of "semantic" as the big picture guru - it tackles language in a way similar to understanding the story behind an art piece. Now, "lexical"? That's your detail detective; it is natural language understanding zeroes in on every word like each one is a unique brushstroke that adds depth to the masterpiece. This dance between semantics and lexical makes us savvy conversationalists and powers cool tech advancements such as natural language processing.

Semantic Search vs Cognitive Search

Semantic search and cognitive search are both advanced search technologies that aim to enhance the accuracy and relevance of search results, but they differ in their approaches and capabilities:

Semantic Search:
- Focus: Semantic search focuses on understanding the meaning of words and the intent behind a search query.
- Technology: It utilizes natural language processing (NLP) and machine learning algorithms to comprehend context, semantics, and user intent.
- Capabilities: Semantic search goes beyond keyword matching and considers the relationships between words, synonyms, and the overall context of the query. Its goal is to provide results that hit the mark by fully grasping what you're saying and considering all its nuances.
Cognitive Search:
- Focus: Cognitive search extends beyond semantic understanding to incorporate additional cognitive capabilities, often involving artificial intelligence (AI) technologies.
- Technology: AI-like machine learning and knowledge graphs help cognitive search understand language and connections better over time.
- Capabilities: Cognitive search understands language semantics and can learn and adapt over time. It's like a brainiac on steroids - it pulls in info from all over, breaks down the messy stuff, and spits out some pretty intelligent takeaways.

Cognitive search is the big picture, and semantic search is just one piece of that puzzle. Think of cognitive search as a high-tech Sherlock Holmes, using AI and other brainy skills to crack the code of intricate questions, juggle various data types, and serve richer knowledge nuggets. While semantic search is all about understanding language, cognitive search takes it up a notch by understanding user intent and grasping not just the info but also how users interact with it.

How to measure Semantic Search?

Key metrics:

Relevance score: How well do search results match the user intent.
Mean Reciprocal Rank (MRR): How high is the first relevant item in the results list.
Click-through rate (CTR): How often do users click on search results.
Time-to-click: How fast do users find what they are looking for.

Industry:

E-commerce: Product discovery by understanding customer intent.
Healthcare: Medical literature search for researchers and clinicians.
Legal: Simplify case law research by understanding complex legal queries.

Future of Semantic Search:

Integration with conversational AI for more natural search.
Multilingual semantic search.
Visual semantic search for image and video content.

Ethical considerations:

As semantic search engines process and show relevant results and interpret user queries more deeply, privacy concerns arise. User data protection and transparency in how search results are generated is key.

Cost considerations:

Implementing semantic search requires upfront investment in technology and expertise but often leads to long term savings through efficiency and user satisfaction. Do a cost-benefit analysis considering reduced support queries and increased user engagement.

Does Zilliz Offer Semantic Search Tools?

Zilliz Cloud is a Vector Database with Open-source Milvus at its heart. A Vector Database’s core functionality is to provide semantic search capabilities. In addition to semantic search work, a purpose-built vector database like Zilliz Cloud provides the following advanced capabilities:

CRUD support, data consistency, and filter search
System availability with strong data persistency and better disaster recovery
System scalability with load balancing support, a distributed architecture that separates computing and storage, and better usability
RBAC with support for multi-tenant SDKs of various programming languages (Python, Javascript, C, Ruby, Go) and a monitoring system.

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