A Deep-Dive into the Fundamentals of Semantic Search
Semantic search - The definition, its workings in detail, how it differ from traditional keyword based search and the use cases.
Introduction
The vast amount of information available online has made it increasingly difficult for search engines to deliver accurate and relevant results based solely on keyword matching. This has led to the development of semantic search, a more advanced search technique that aims to understand the context and intent behind a search query. In this article, we will take a deep dive into the fundamentals of semantic search, exploring its workings, importance, and applications.
What is Semantic Search
Semantic search is an advanced search technique that goes beyond keyword matching to understand the context and intent behind a search query.
It employs natural language processing (NLP) and machine learning algorithms to understand the meaning of words and phrases in a query and provide more accurate and relevant results.
For example, if you search for "apple," a traditional search engine might return results related to the fruit, the tech company, or a variety of other topics containing the word "apple." However, a semantic search engine would analyse the context of your query and previous searches to determine that you are looking for information about the tech company, and prioritize results related to Apple Inc.
How Semantic Search Works
Semantic search involves several key components and steps to convert data and queries into vectors, index the vectors, find the distance metric, and return close matching vectors. Here's a step-by-step breakdown:
STEP 1 : Convert Data to Vectors
The first step in semantic search is to convert the text data into numerical vectors. This is typically done using word vector embeddings, which are vector representations of words or phrases in a multi-dimensional space.
Some of the popular embedding models are Google’s Word2Vec, BERT, GloVe, OpenAI’s text-embedding-ada-002
For example, if you have list of products from different categories electronics, fruits, apparel, those products would be converted into numerical vectors as placed in vector space as below":
STEP 2 : Convert Query to Vector
The next step is to convert the search query into a vector. In this case, the search query is "Apple gadget". This query would also be converted into a numerical vector that represents the meaning and context of the query.
STEP 3 : Calculate Similarity Metrics
In this step, the similarity distance between the query vector and each of the data vectors is calculated.
This is often done using a method such as Cosine Similarity or L2 Euclidean distance, which measures the angle between two vectors. A smaller angle means the vectors are more similar.
For example, the cosine similarity between the 'Apple gadget' query vector and each of the data vectors in the three clusters would be calculated. The 'Apple gadget' query vector would likely have a smaller angle (and therefore higher similarity) with the vectors related to Apple Inc. products than with the vectors related to Apple fruit varieties or vectors related to Apple-themed apparel.
STEP 4 : Identify the Most Similar Matches
The final step is to identify the most similar matching vectors to the query vector, which are the data vectors with the highest similarity to the query vector.
Some of the common algorithms to find the similar vectors are Locality-Sensitive Hashing (LSH), Approximate Nearest Neighbors (ANN), Hierarchical Navigable Small World (HNSW)
In this case, the algorithm would likely return the data vectors from Apple Inc. products related vectors as they would have the highest similarity to the 'Apple gadget' query vector. Therefore, the search results would include 'iPhone', 'Watch', 'iPad Pro', and 'Macbook Pro'.
Keyword-Based Search vs Semantic Search
Traditional keyword-based search and semantic search represent two different approaches to information retrieval. Let's take a closer look at how they compare:
In summary, traditional keyword-based search focuses on exact keyword matching, often leading to less accurate and less relevant results. Semantic search, on the other hand, analyzes the context and intent behind a query to provide more accurate, relevant, and personalized results.
Use Cases of Semantic Search
Semantic search has a wide range of applications, from improving search engines and chatbots to enhancing content recommendations and personalizing user experiences.
E-commerce: Semantic search can be used to improve product search and recommendations in e-commerce applications by understanding the user's intent and preferences.
Search Engines: Semantic search can be used to improve the relevance and accuracy of search engine results. For example, Google uses a form of semantic search in its Knowledge Graph, which aims to understand the relationships between different entities and provide more relevant results.
Chatbots and Virtual Assistants: Semantic search can be used to develop chatbots and virtual assistants that can understand and respond to user queries more accurately and naturally.
Content Recommendations: Semantic search can be used to improve content recommendations by understanding the context and preferences of the user and recommending content that is more likely to be of interest.
Conclusion
Semantic search represents a significant advancement in the field of information retrieval, offering more accurate and relevant search results by understanding the context and intent behind a query. With its ability to handle natural language queries, provide personalized results, and handle ambiguity more effectively, semantic search promises to revolutionize the way we find and use information online.