What is Semantic Search? #

Semantic search is a search technology that uses natural language processing algorithms to understand the meaning and context of words and phrases in order to provide more accurate search results. It aims to better understand the user’s intent and query content, not just based on keyword matching, but by analyzing the semantics and context of the query to deliver more accurate and relevant search results.

Traditional keyword searches primarily rely on matching keywords, ignoring the meaning and context of the query. However, the advantage of semantic search is that it can better satisfy the user’s intent, especially for complex queries and questions. It can understand the context of a query, handle ambiguous or incomplete queries, and provide more relevant and useful search results. For example, when a user searches for “nearest restaurant”, semantic search can provide a list of nearby restaurants based on the user’s location information and context, rather than just matching the keywords “nearest” and “restaurant”.

History of Semantic Search #

The concept of semantic search dates back to the early days of computer science, with attempts to develop natural language processing systems as early as the 1950s and 1960s. However, significant progress in the field of semantic search was not made until the 1990s and 2000s, partly thanks to advancements in machine learning and artificial intelligence.

One of the earliest examples of semantic search is the Cyc project, created by Douglas Lenat in 1984. This project aimed to build a comprehensive common sense ontology or knowledge base to understand natural language queries. Although the Cyc project faced many challenges and ultimately did not achieve its goal, it laid the foundation for future research in semantic search.

In the late 1990s, search engines like Ask Jeeves (now Ask.com) began experimenting with natural language queries and semantic search technologies. These early efforts were limited by the technology of the time, but they showcased the potential of more sophisticated search algorithms.

The development of the Web Ontology Language (OWL) in the early 2000s provided a standardized way to represent knowledge and relationships in a machine-readable format, making it easier to develop semantic search algorithms. Companies like Powerset, acquired by Microsoft in 2008, and Hakia, launched in 2007, began using semantic search technologies to deliver more relevant search results.

Today, many search engines and companies are using semantic search to improve the accuracy and relevance of search results. This includes Google, which introduced the Knowledge Graph in 2012, and Amazon, which uses semantic search to support its Alexa virtual assistant. As the field of artificial intelligence continues to evolve, semantic search may become more complex and applicable to a wide range of applications.

Latest Improvements in Semantic Search #

The latest improvements in semantic search help further advance the field. Some of the most notable include:

Transformer-based models: Transformer-based models, such as BERT (Bidirectional Encoder Representations from Transformers), have revolutionized natural language processing and semantic search. These models are better at understanding the context of words and phrases, making it easier to deliver more relevant search results.

Multimodal search: Multimodal search refers to the ability to search for information across multiple modes, such as text, images, videos, etc. The latest advancements in machine learning make it possible to develop more accurate and complex multimodal search algorithms.

Conversational search: Conversational search involves using natural language processing and machine learning to provide more accurate, personalized responses to user queries. This technology has already been used in virtual assistants, such as Amazon’s Alexa and Apple’s Siri.

Personalization: Personalization refers to the ability to tailor search results based on a user’s preferences and past search history. As the amount of data available online continues to grow, this becomes increasingly important.

Domain-specific search: Domain-specific search involves using semantic search technology to search within specific fields or industries, such as healthcare or finance. This helps provide more accurate, relevant search results for users in these industries.

Overall, the latest developments in semantic search make finding information online easier and pave the way for future, more complex search algorithms.

What is the Relationship Between Semantic Search and Knowledge Graphs? #

Semantic search and knowledge graphs are closely related, as both involve using semantic technologies to improve search results.

A knowledge graph is a graphical structure used to organize and represent knowledge, showing the semantic associations between entities and relationships through connections of nodes and edges. For example, a knowledge graph may contain information about a specific company, including its location, products, and employees, and the relationships between these entities.

On the other hand, semantic search is a search technology that uses natural language processing and machine learning to better understand the meaning of words and phrases in search queries. Semantic search algorithms use knowledge graphs and other semantic technologies to analyze the relationships between entities and concepts and provide more relevant search results based on this analysis.

In other words, knowledge graphs provide a rich knowledge background for semantic search, helping to understand query intent and deliver accurate search results. Meanwhile, semantic search can help build and expand knowledge graphs, improving the accuracy and semantic understanding capabilities of searches.

For example, Google’s Knowledge Graph uses a vast structured data database to support its search results and provide additional information about entities appearing in search results, such as people, places, and things. This makes it easier for users to find the information they are looking for and explore related concepts and entities.

Vector Databases, Knowledge Graphs, and Semantic Search #

Vector databases are another technology that can be combined with semantic search and knowledge graphs to improve search results. They are primarily used for processing and analyzing data with vector characteristics, such as images, audio, text, time series, etc.

Traditional relational databases are mainly used for storing structured data, while vector databases focus on storing and processing high-dimensional vectors. Their design goal is to efficiently perform operations such as vector similarity search and clustering to support complex data analysis and machine learning tasks. Vector databases use machine learning algorithms to represent data as vectors, a mathematical representation of data that can be used for various computational tasks. For example, vectors can be used to represent entities such as people, places, and things, and their relationships. By comparing these vectors, search algorithms can identify relationships and patterns that may not be immediately apparent in the data itself.

In the context of semantic search and knowledge graphs, vector databases can improve the accuracy of search results by better understanding the relationships between entities and concepts.

For example, when a user searches for “London”, a semantic search algorithm can use knowledge graphs and vector databases to understand that the user might be referring to the city of London in the UK, rather than other entities with the same name.

By using vector databases to represent and compare entities and concepts, search algorithms can provide more relevant and accurate search results.

Overall, vector databases, semantic search, and knowledge graphs are technologies that collectively improve the accuracy and efficiency of search algorithms. By leveraging these technologies, search engines and other applications can better understand the relationships between entities and concepts, making it easier to find the information users are looking for.