Understanding Semantic Search
Written by Team Timescale
Semantic search is a key component of natural language processing (NLP). It aims to improve search accuracy by grasping the searcher's intent and the contextual meaning of terms. This approach goes beyond simple keyword matching: semantic search considers various factors like the user's location, search history, and global search trends. It also considers synonyms, homonyms, and the relationships between different concepts.
NLP-based AI systems are expanding every day, and semantic search has become a focal point for many developers. Its applications range from improving web search engines to enhancing document retrieval in enterprise systems. Many tech companies are investing heavily in semantic search capabilities to provide more relevant results to their users. This technology is instrumental in fields like e-commerce, where understanding user intent can significantly improve product recommendations and search results.
The broad scope of semantic search can make it challenging to define precisely. It encompasses various techniques and approaches, each with strengths and use cases. These include knowledge graphs, vector embeddings, and inference engines. Each method has its own algorithms and data structures, making it a multifaceted field that requires a deep understanding of linguistics and computer science.
This article will:
Provide a detailed explanation of semantic search, including its core principles and how it differs from traditional keyword-based search.
Examine different strategies used in semantic search implementation, such as knowledge graphs, vector embeddings, and machine learning models.
Explore tools for developing semantic search systems, including open-source libraries, cloud services, and commercial solutions.
What Is Semantic Search?
Semantic search is a method of ranking text content responses to a search query. It evaluates responses based on their relevance to the query's content. The more closely related a response is to the query, the higher it ranks in the results. This approach aims to understand the intent behind the search, not just the literal words used.
Unlike lexical search, which looks for exact text matches, semantic search considers context and meaning. For instance, a query for "database optimization" in a lexical search might only return results containing that exact word. However, a semantic search could return results about query tuning, data storage types, or compression techniques—topics relevant to databases but not necessarily containing the word itself.
How does semantic search connect with LLM systems?
Semantic search is closely tied to large language model (LLM) systems. These AI models organize information according to its content, learning to associate meanings within the dataset. This aligns well with semantic search, which structures data by its meaning rather than by keywords. For example, an LLM trained on semantically organized data can better understand relationships between concepts, leading to more accurate and contextually relevant responses.
This connection between semantic search and LLMs allows teams to organize their text data more effectively. Semantic search enables more intelligent data retrieval and processing by structuring information based on meaning rather than literal text. This is particularly valuable in applications like question-answering systems, where understanding the intent behind a query is crucial for providing accurate responses.
Semantic Search Strategies
Semantic search uses various methods to understand and process search queries effectively. These approaches aim to connect user intent with relevant information retrieval. Advanced algorithms and data structures help semantic search strategies interpret context, understand relationships between concepts, and provide accurate results.
Three fundamental approaches are central to many semantic search implementations:
Knowledge graphs
Inference engines
Vector embeddings and vector search
Each strategy offers unique capabilities in capturing and using semantic information. Depending on the search application's needs, they can work alone or together.
The following sections will examine each approach, covering its core principles, semantic search role, and practical application use. This overview will give you a thorough understanding of the tools for building effective semantic search systems.
Knowledge graphs
Knowledge graphs are data structures that represent information as a network of interconnected entities. They consist of nodes and edges, where nodes typically represent concepts or entities, and edges represent their relationships.
In natural language processing (NLP), nodes often correspond to words or phrases found in text. Edges connect these nodes based on various criteria, such as words appearing next to each other, in the same sentence, or within the same paragraph.
This structure enhances raw textual data with relational context. By organizing information this way, knowledge graphs enable machines to understand and process information in a more human-like way, grasping connections and relationships between different pieces of information.
Knowledge graphs are handy for semantic search:
They provide context to data points.
They allow for the inference of indirect relationships.
They support multi-hop reasoning.
They enable more accurate entity disambiguation.
Here's a simple example of a knowledge graph:
A query about "database performance" could lead to results about optimization and indexing, even if those exact phrases don't appear together.
The system can infer that "SQL queries" are related to "execution plans" and "performance," providing more comprehensive search results.
It captures the relationship between "storage" and "data types" in the context of databases.
Inference engines
Inference engines are programs that use context-relevant datasets to answer specific questions. They operate on a knowledge base, a structured information collection compiled by subject experts. The design of these engines follows expert reasoning patterns to draw conclusions or make predictions.
Key features of inference engines:
They work with a predefined knowledge base
They apply logical rules to derive new information
They can handle uncertainty and incomplete data
They often use if-then rules or decision trees
In semantic search, inference engines can expand query understanding by making logical connections between concepts.
Examples of inference engines:
1. Medical diagnostic systems: These systems take patient data as input and provide a list of likely diagnoses. A real-world example is MYCIN, an early expert system developed for diagnosing blood infections. MYCIN used a knowledge base of about 600 rules to identify bacteria-causing infections and recommend antibiotics.
2. Galaxy classification programs: These systems evaluate astronomical data to categorize galaxies based on mass, rotation, and shape characteristics. They use inference rules based on astrophysical principles to classify newly observed galaxies.
Vector embeddings and vector search
Vector embeddings and vector search are techniques that transform text into numerical representations in a high-dimensional space. This approach allows efficient and semantically meaningful comparisons between words, phrases, or documents.
Key characteristics of vector embeddings and search:
Capture nuanced relationships between words
Handle synonyms and related concepts effectively
Perform well in multilingual contexts
Scale to large datasets efficiently
These characteristics make vector-based methods highly effective for many semantic search applications, enabling more intelligent and context-aware information retrieval.
Key aspects of vector embeddings and vector search:
Vector embeddings:
Convert words or phrases into vectors of real numbers
Capture semantic relationships in the positioning of vectors
Similar concepts are represented by vectors close to each other in the vector space
Vector search:
Uses distance metrics (like cosine similarity) to find vectors closest to a query vector
Enables finding semantically similar content, even without exact keyword matches
Example of vector embedding and search: Word2vec
Word2vec is a popular method for creating word embeddings. It uses neural networks to learn vector representations of words based on their context in a large corpus of text.
Process:
Training: the model learns from a large text dataset and understands word relationships.
Embedding: each word is assigned a vector in a high-dimensional space.
Search: queries are converted to vectors and compared with the corpus vectors.
Semantic search implementation relies on various specialized tools. These tools help you create, manage, and utilize semantic data structures effectively. Three main categories of tools are particularly important in this field:
1. Open-source knowledge graphs
These are publicly available, community-driven knowledge bases. They provide structured data about a wide range of topics, which can be integrated into semantic search systems to enhance understanding of relationships between concepts.
2. Inference engine architectures
These frameworks or platforms facilitate the creation and deployment of inference engines. They often include tools for rule management, reasoning algorithms, and integration with other systems.
3. Vector databases
These are specialized database systems designed to store and query vector embeddings efficiently. They enable fast similarity searches in high-dimensional spaces, crucial for vector-based semantic search.
Each of these tool categories serves a specific purpose in building semantic search systems:
Knowledge graphs provide the foundational semantic relationships.
Inference engines apply logic and rules to these relationships.
Vector databases manage and query the numerical representations of semantic data.
In the following sections, we'll explore specific examples of tools in each category. We'll discuss their features, use cases, and how they contribute to building effective semantic search solutions. This overview will help you choose the tools for your semantic search projects.
Open-source knowledge graphs are valuable resources for semantic search applications. They provide vast, structured datasets that can enhance the understanding and contextualization of information.
Here are some prominent examples:
DBpedia is a structured knowledge base derived from Wikipedia content. It offers data in multiple languages and covers a wide range of topics from general knowledge. DBpedia's structured format is helpful for semantic search applications, allowing for more precise and context-aware queries across diverse subjects.
Wikidata is a collaborative knowledge base providing structured data that complements Wikipedia. It supports multiple languages and offers machine-readable data, making it ideal for integration into semantic search systems. Wikidata's community-driven approach ensures a constantly evolving and expanding knowledge base.
KBpedia integrates several large public knowledge graphs into a single comprehensive resource. It offers a unified knowledge structure useful for various AI and semantic technology applications. KBpedia's integration of multiple sources provides a rich, interconnected web of information that can significantly enhance semantic search capabilities.
It's important to note that these knowledge graphs are not plug-and-play solutions. They often require significant customization and adaptation to fit specific use cases. You must be prepared to invest time in understanding the graph's structure, mapping it to your domain, and integrating it effectively with their semantic search system. This process often involves filtering, transforming, or extending the data to meet the application's needs.
Inference engines come in various forms, each designed to address specific problems. These engines apply logical rules to a knowledge base to derive new information or make decisions. The diversity of inference engines reflects the breadth of applications they can use.
OpenPPL is an example of an inference engine specialized for image classification. It provides a framework for deploying deep learning models for visual recognition tasks. OpenPPL's inference capabilities allow for efficient image data processing, making it suitable for applications ranging from autonomous vehicles to medical imaging analysis.
Infermedica offers an inference engine focused on medical data evaluation. This specialized engine processes patient symptoms and medical history to assist in diagnosis and triage. By leveraging a vast medical knowledge base, Infermedica's engine can provide valuable insights to healthcare professionals, potentially improving patient outcomes and streamlining medical processes.
Ideas for using inference engine resources
Finding the correct engine for your specific application is essential when considering the use of inference engine resources. This requires deep engagement with the particular knowledge domain you are working in. An inference engine's effectiveness depends on how well it aligns with your data and problem space.
Ensure that the engine you build or utilize is designed around the specific data you have access to. This might involve customizing an existing engine or developing a new one tailored to your needs. The quality and structure of your data will significantly impact the performance of the inference engine.
Testing your engine on critical cases is essential to avoid costly or dangerous results. This is particularly important in high-stakes domains like healthcare or finance. Rigorous testing helps identify potential biases, errors, or limitations in the engine's reasoning process. It's advisable to start with well-understood test cases and gradually expand to more complex scenarios.
Remember, while inference engines can be powerful tools for semantic search and decision-making, their effectiveness is only as good as the knowledge base and rules they operate on. Regular updates and refinements to the engine and its underlying data are often necessary to maintain accuracy and relevance.
Vector databases play an important role in vector search systems. Traditional databases often struggle with vector embedding and search operations due to the high dimensionality of vectors and the poor scaling of metric searches in these spaces. Vector databases address these challenges by implementing specialized data types for storing vectors and optimized search algorithms for standard metrics.
Several embedding algorithms are available for converting text or other data into vector representations. For example:
Introduced in a 2013 paper by Mikolov et al., Word2vec is a widely used method for creating word embeddings. It employs shallow neural networks to learn vector representations of words based on their context in large text corpora. Word2vec can capture semantic relationships between words, allowing for operations like finding similar words or analogies. The resulting word vectors can be used in various NLP tasks, including semantic search.
OpenAI embeddings offer another approach to vector representations. These models, developed by OpenAI, can generate embeddings for words, sentences, or entire documents. Unlike Word2vec, which focuses on individual words, OpenAI embeddings can capture context at various levels of granularity. They are trained on diverse datasets and can be fine-tuned for specific tasks. OpenAI embeddings are useful for applications requiring an understanding of longer text sequences or more nuanced contextual information.
Timescale Cloud, Timescale’s vector database system, provides developers with a managed PostgreSQL cloud platform and offers an open-source PostgreSQL stack for AI applications (which includes the open-source extensions pgvector, pgvectorscale, and pgai).
Through these extensions, and because of its PostgreSQL foundation, Timescale Cloud offers support for vector embeddings while maintaining the flexibility and familiarity of a relational database. This approach can be beneficial for several reasons:
Reduced learning curve: developers familiar with PostgreSQL can leverage their existing knowledge.
Extensibility: the PostgreSQL foundation allows easy integration with other data systems and types.
Performance: Timescale optimizes vector operations, offering production-level vector database performance.
Versatility: users can combine vector search capabilities with traditional relational data models, enabling complex queries involving vector similarity and standard SQL operations.
By offering vector support within a robust relational database system, Timescale aims to provide a solution that balances specialized vector search capabilities with broader data management needs. This approach can be particularly useful for projects that require both vector search and traditional data operations, allowing you to work within a unified database environment.
With PostgreSQL and pgvector, pgai, and pgvectorscale, it’s easy to build retrieval-augmented generation (RAG) applications, create powerful apps that leverage similarity search, and generate AI agents on the cloud without leaving your database. Both pgai and pgvectorscale (which build upon the pgvector extension) are open source under the PostgreSQL License and available for use in your AI projects today.
You can find installation instructions on the pgai and the pgvectorscale GitHub repositories (GitHub ⭐s welcome!). You can also access these extensions on any database service on Timescale’s Cloud PostgreSQL platform.
Wrap-Up
This article examined semantic search from its basic concepts to its practical applications. We started by explaining semantic search as a method that considers context and user intent, going beyond simple keyword matching. We also looked at the strategies and tools to drive and implement semantic search to improve search capabilities, giving users more relevant results.
Plus, we highlighted how platforms like Timescale Cloud simplify vector search implementation within familiar database environments. Adding optimized vector operations to PostgreSQL's flexibility allows Timescale to incorporate semantic search without completely changing existing systems.
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