Times are changing. Search engines are becoming way smarter than their human counterparts, moving beyond basic keyword searches to truly understand the meaning behind our queries. This transformation is powered by artificial intelligence (AI), which allows search engines to comprehend context and intent. As a result, finding information online is becoming more intuitive and accurate, even if the exact keywords aren’t used.
AI technologies like natural language processing and machine learning are spearheading the shift. So, In this blog, we’ll discuss how these developments are changing our search experiences and making it simpler than ever to locate what we are actually looking for. Now, let’s take a look at how intelligent search is changing the way we interact with information.
What is Semantic Search?
Semantic search is a way for search engines to understand the meaning behind your words, not just the exact words you type. Instead of matching keywords, it looks at the context and intent of your query to find more relevant results. ( this includes rich results too.) Google implemented semantic search in 2013 with its Hummingbird update, with an aim to enhance the search engine’s ability to interpret the meaning behind queries.
To put it in simple words, the concept of semantic search is derived from the work of Tim Berners-Lee, (the inventor of the World Wide Web) He envisioned a web where computers could understand and process information more like humans ( in a better way ). This makes finding what you need on the internet faster and more accurate, even if you don’t use the perfect keywords.
Four Pillars Of Semantic Search AI
Now let’s take a look at how semantic search differs from other search engines.
User Intent Recognition: Have you ever looked for “Java” and discovered that, despite your search being about an Indonesian island, the search engine somehow knew you were looking for programming tutorials? Semantic search is doing that, calculating your intent from your search history and context.
Understanding Context: Consider looking up “how to fold a shirt.” Semantic search engines recognize that you are probably seeking for a visual instructional, so instead of merely returning text-based results, they will show you videos and step-by-step instructions.
Customized Results: It becomes more intelligent the more you search. Semantic search makes each search more effective and relevant than the last by using your search history to customize results just for you.
The Technologies Behind Semantic Search
Semantic search uses several advanced technologies to understand and interpret the meaning behind search queries. One of such key technology is natural language processing (NLP). It helps computers understand and generate human language. NLP also allows search engines to grasp the context and nuances of queries, making results more relevant and informational.
Another key technology is machine learning. It’s where algorithms learn from large amounts of data to recognize patterns and improve search accuracy over time. These algorithms can identify various factors including ( synonyms related concepts etc ), and the intent behind a query, providing better search results even when exact keywords aren’t used.
Knowledge graphs are also very important. They organize information ( about people, places, and things ) into interconnected databases, enabling search engines to draw connections and provide more comprehensive answers. For example, Google’s Knowledge Graph, introduced in 2012, helps answer queries directly by understanding relationships between different pieces of information.
Lastly comes the vector search. It uses mathematical vectors to represent words and their meanings, helps search engines find results based on semantic similarityThese combined technologies make semantic search better and smarter.
How Can AI Semantic Search Be Used In Different Industries?
AI semantic search can greatly benefit various industries by making information retrieval more efficient and accurate.
Healthcare
These searches help doctors and researchers to find relevant medical literature, patient records, and clinical trial data quickly. For instance, if a doctor searches for treatment options for a specific condition, the search engine can understand the context and provide comprehensive information about the latest therapies, case studies, and related conditions.
E-Commerce
Improves the shopping experience by understanding customer queries better. If a customer searches for “comfortable summer shoes,” the search engine can interpret this to show results for breathable, lightweight shoes suitable for summer, rather than just matching the words “comfortable” and “shoes.” This leads to more satisfied customers and higher sales.
Legal Industry
The search helps lawyers and legal researchers find relevant case laws, statutes, and legal opinions more efficiently. It understands the nuances of legal language, making certain that searches yield the most accurate results, saving time and improving accuracy.
Education Sector
Semantic search aids students and educators by providing precise information from vast online resources. For example, a student looking for information on “photosynthesis” can get detailed, contextually relevant material from textbooks, articles, and research papers, making study and research more effective.
Overall, AI semantic search enhances productivity and accuracy across various fields by understanding and processing complex queries in a human-like manner.
What’s The Difference Between Semantic Search And Keyword Search?
Semantic search and keyword search are two different approaches to finding information online.
Keyword search is the traditional method where the search engine matches the exact words you type with the words in its database. If you search for “best pizza in New York,” the search engine looks for pages with those specific words. It’s straightforward but often misses the context or deeper meaning behind the query.
Semantic search, on the other hand, goes beyond just matching words. Using technologies like natural language processing and machine learning, semantic search considers the context of the words. For example, if you search for “best pizza in New York,” a semantic search engine understands that you’re looking for highly-rated pizza places in New York City, not just any mention of those words.
It understands synonyms, related concepts, and the overall context.
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Course Highlights:
- Foundations of AI: AI significance, applications, and ethics.
- Generative AI and LLMs: Advanced models and business uses.
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- Hands-on Labs: Python libraries for semantic search, embeddings, dense retrieval, reranking, and answer generation.
Eligibility:
- Gen AI researchers
- Machine learning engineers / Data scientists / Tech professionals in NLP projects
