Beyond Keywords: Semantic Search for Smarter Information Retrieval
Introduction
In the not-so-distant past, search engines looked through enormous amounts of material primarily using keywords. Nevertheless, semantic search turned out to be a game-changer, surpassing keyword-based algorithms (lexical search) as the need for more contextually correct results increased. This blog post covers the basics of semantic search, exploring its benefits, implementation, and applications.
Types of Search Algorithms
Lexical search and Semantic search are the two main categories of keyword search.
Lexical search is a basic search method that searches the data based on the words provided to it. It doesn’t consider synonyms or variations of the words, making it simple but potentially limited in capturing complex user intent.
Semantic search is a more sophisticated approach that tries to understand the meaning behind a search term, rather than merely matching keywords. It provides more relevant and accurate results by considering the context and relationship between words, thereby enhancing the user experience.
The Role of NLP in Semantic Search
Natural Language Processing (NLP) techniques help machines understand and interpret human language. NLP empowers semantic search to comprehend the deeper semantic interpretation of the text. thereby empowering search engines to deliver more accurate and contextually relevant results.
Introducing Elastic Search
ElasticSearch is a powerful search and analytics engine that analyzes huge volumes of data quickly. It is faster since it searches for the index in place of the actual text.
Note: An index is a collection of documents that have similar characteristics. For example, an e-commerce store can have separate indices for Customers, products, and Orders.
How it works
Below are the steps involving the creation of indices, storing data and then fetching the relevant data based on the user queries. As part of the setup process, the ElasticSearch index is created where the data will be stored.
- Data Preprocessing: Cleaning and preprocessing of the text data, encompassing operations like tokenization, stopword removal, and stemming is done as the first step.
You can read more about text data preprocessing here:
- Embedding Generation: The preprocessed text is then converted into embeddings using Word2Vec, BERT, or other pre-trained models.
Word embeddings are dense vector representations of words in a continuous vector space, enabling machines to understand and process language by capturing semantic meaning of words.
- Indexing Documents: The preprocessed data is then indexed along with their corresponding embeddings.
- User Query Processing: When a user makes a search query, the query is processed in the same way as the indexed documents and is then converted into an embedding.
- Similar Document Retrieval: Using a nearest neighbor search algorithm (such as Hierarchical Navigable Small World graphs or HNSW) on the query embedding similar documents are identified from the index(where the base data was stored previously).
- Ranking and Scoring: The results are retrieved along with a relevance score based on the relevancy of the matched results. This is based on term frequency, document popularity, and similarity of the document with the user query.
Real-world Applications
Semantic search has opened a new pathway and has a lot of potential. Some of its applications are :
- E-commerce and Product Recommendations
- Healthcare and Medical Information Retrieval
- Customer Support and Chatbots
- Legal Research and Document Analysis
Conclusion
Semantic search surpasses the constraints of keyword-based search and provides more accurate and relevant results to the users by understanding the relationship between the words. Embracing this opens up a world of possibilities for more intelligent and sophisticated search experiences in the world of information retrieval.
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