Semantic Search vs Keyword Search: What You Need to Know

These two well known search methodologies have importance in defining user experience in ecommerce and search engine optimization (SEO) strategies. In this article, we will explore these different search approaches in detail, discuss differences between them, provide insights on their benefits and limitations, and explain some real-world use cases; with a special focus is on the semantic search vs keyword search technologies.

What is Semantic Search?

Semantic search is the application of artificial intelligence (AI), natural language processing (NLP) and machine learning (ML) to better grasp a user’s search intent as well as contextual meaning behind queries. Semantic search seeks to understand meaning and context, and the spirit of user queries rather than simply matching the words in the query. Whereas a traditional search engine relies on matching keywords to return results, a semantic search engine deduces relationships between words and how they relate in a wider context. Decent keyword search results through Search and text-based search are still relevant. Here's an example:

They know that someone who searches for cheap laptop probably wants to see a mobile, laptop-sized product with a price tag of less than 500 USD. Conversely, keyword-based search only considers product titles explicitly designated as a laptop, and items with the word "cheap" in their description might be given higher priority thereby improving rankings. But it will also bring back all laptops, no matter how much they cost.

How Semantic Search Works

A semantic search engine employs advanced systems such as Natural Language Processing or NLP which allows machines to understand human language by analyzing meaning, context, and intent of words enabling it to respond more accurately to queries.

It uses technology that converts words into sets of numbers to capture the meaning of these words and finds the best matches for your search intent (Vector Search).

Machine Learning or ML, allows computer systems to learn from data and improve over time by analyzing patterns and making predictions, aiding in delivering smarter and relevant search results. Machine learning algorithms may learn from key performance indicators as well. Machine learning is another option for increasing user satisfaction, because it also provides relevant results.

For instance, when a user asks “What is semantic search? “ the search engine understands through NLP that what the end-user is looking for is a definition or explanation of the term, so it returns results that fulfill that intent.

Semantic Search Examples

Semantic Search: Powerful understanding of the meaning and intention of search queries can greatly enhance user experience, because it allows the search engine to be able to better understand vague or concept-based searches.

Semantic search comes truly into its element where a straight keyword match clears the user’s real intent. Semantic search work is focused on understanding user queries in order to return more relevant and context-aware results. Consider these examples with different context for this type of search technology:

Voice search: The user asks, “Where’s the best Italian restaurant near me?” semantic search leverages location data and contextual information to provide results specific to their query.

E-commerce search: Inputting “ running shoes” throws up results of perfect-sized running shoes, for the perfect activity and color according to previous search data, even if the words don’t appear explicitly in product descriptions.

Song lyrics: Searching for “Hello, it’s me” returns results for Adele’s song “Hello,” demonstrating the engine’s matching capability for phrases, even when the words don’t match literally.

What is Keyword Search?

Keyword search, also known as lexical search or traditional search, has underpinned search engine technology for decades. The process used here is the exact matching of words or phrases in the web pages. The search engine finds pages and orders them based on how often and in what context users search for those downloadable documents.

How it works

In lexical search, the engine has a list of which web pages have what words. If someone searches for “best running shoes,” the engine identifies pages mentioning those words and ranks them according to how frequently those words appear and how unique the choice of words is in the content.

Strengths and Weaknesses

Pros:

• Simple and really powerful for retrieving documents with specific terms.
• Easy to implement and low power requirements.
• Interpretability — populating results with explanations for why a document matched a user’s query;

Cons:

• Lack of contextual understanding — does not account for synonyms and the overall meaning of queries
• Vulnerable to irrelevant results: Can pull up documents that have the query terms yet do not match the searcher’s goal.
• Easily gamed: The classic keyword stuffing trick can sometimes game the engine into ranking less relevant content higher.

Main Differences Semantic Search Engine Vs. Traditional Search Engines

Semantic Search vs Lexical Search: Which one to choose? Semantic Search or Lexical Search? Although both serve as search engines, their functions differ significantly and you should understand these differences before picking the one for yourself. Semantic search makes sense of the intent behind what people are looking for by understanding what a phrase actually means. Here’s how they differ:

1 - Understanding User Intent

Semantic Search: Focuses on the user’s search intent, leveraging NLP and ML to comprehend context and related terms. NLP allows recognition and processing of the language spoken by humans, which revolutionizes how semantic search works, since it allows to juggle with the complexity behind human language.

Keyword Search: Only works on exact matches of the query words, no understanding of what the words really mean.

2 - Dealing with Synonyms and Alternatives

Semantic Search: Identify synonyms and related phrases, making searches comprehensive, and relevant results.

Keyword Search: You can’t find results without keyword match with the words typed by the users.

3 - Handling Complex Queries and Natural Language Processing

Semantic Search: Being able to understand natural language queries, including vague searches such as “how do eagles hunt?” it could apply to both avian behavior and metaphorical uses.

Keyword Search: Has a hard time with complex queries that do not match exact keyword strings.

4 - Relevance and Precision

Semantic Search: Semantic Search results are more accurately aligned with context.

Lexical Search: Can produce a large amount of web pages, but the relevance might still be all over the place.

Applications and Use Cases

When to Use Keyword Search

Such a form of search experience still works well in higher precision-required scenarios, such as:

• Sourcing data: When you need precise matches
• Simple product catalogs: For users who already know specific names.
• Technical documentation — where the language is so technical.

When to Use Semantic Search

Semantic search is best suited for improving the relevance and accuracy of search results based on user intent in the following ways:

• Customer service portals: User search in natural language, enhancing user satisfaction.
• E-commerce platforms: creates better product discovery internationally across different terminology used in descriptions.
• Voice search and virtual assistants: Contextually relevant answers to spoken questions.
• Patent searches: Discovers related concepts and earlier work even when terminology differs.

Semantic Search vs Keyword Search in E-commerce

For an e-commerce business, being able to choose between semantic search and keyword search can have a direct impact on customer experience as well as sales. Semantic search offers precisely what e-commerce platforms are built for: intuitive, accurate product discovery. Semantic Search refers to the process of understanding what individuals mean in a certain context rather than conducting a search based on exact keywords, as is the case with many traditional search engines that can offer returns based on exact matches, offering a more semantic-oriented product search.

Semantics Search Engine in E-commerce is the best way to deliver benefits.

Improved Product Discovery Input: Users often search, in, sentences or the informal language. For instance, someone searching for "gym sneakers" may be shown products even if they are branded "training shoes" or "sports footwear."

Enhanced User Experience: Semantic search caters to the way users naturally formulate their queries, offering a more seamless and intuitive search experience.

Relevance and Personalization: Semantic search can utilize previous search history, location, and user preferences to tailor results to individual users.

Traditional Search for E-commerce: The Drawbacks

When does lexical search fail in e-commerce?

Synonyms and Variations: A search for “evening dresses” may overlook products classified as “gowns” or “formal attire.”

Because lexical search is restrictive, if a user does not use a keyboard exact term their results are likely to be not what they wanted.

Real World Example: Semantic Search In Action

For example, if the user types “best phone for photography” on an e-commerce site. For example, a semantic search engine might return smartphone models that are highly rated for their camera quality, even the search product descriptions do not contain the exact phrase. Such highly contextualized search can increase both user satisfaction and conversion rates.

Vector search vs Semantic search

I'm afraid we can not discuss semantic search and traditional search without mentioning vector search.

How Vector Search Works

This kind of search plots words and phrases as points in a multi-dimensional space, in which the distance between them indicates how closely they relate in meaning. So if you were to search for something like “affordable cars,” your system recognizes content related to budget-friendly vehicles, regardless of whether or not the exact word “affordable” was used. Through the comparison of these closely located points, vector search enables semantic search to return relevant results based on the intent and context of your query, not simply results that have common keywords. This results in more relevant, accurate search results. This is how we can think these Keywords as points in a multi dimensional space.

For example, a search for 'kitten' in a fuzzy or vector-based engine would return related results about other content related to cats, regardless of whether they use the phrases 'feline' or 'cat' or 'kitten' or 'tomcat'.

Vector search itself is a foundational technology that enables semantic search but doesn’t by itself take searcher intent or other insights from NLP into account.

Semantic search is based on vectors, but includes layers of context analysis and entity recognition. In other words, it has a better understanding of the subject, as well as of the genuine goal of the user.

Semantic Search in SEO

With the evolution of semantic search, the old SEO game has changed. Semantic, in contrast to keyword-centric SEO (which is all about exact keywords), is the practice of covering a topic thoroughly in your web pages, so that it reflects the actual queries people enter and the content corresponding to their intent. That means producing insightful content that fulfills users’ needs comprehensively for a topic.

SEO Changes to Implement for Semantic Search

Content Depth: To establish yourself as an authority on given topic you must cover it and related topics on the page.

Be natural way: phrases that form long tails and way to search as people (speech, question or when they searched)

Entity Focused Content: Have key subjects in mind and implement some special coding (schema) to indicate how these concepts correlate with each other. This allows them to identify your content better.

User Intent Alignment: Matches the query intent of the user, whether it is informational, navigational, or transactional, to make sure the content fulfills the user’s demand.

Content Groups: Group related content together and link them to a main page to show your knowledge and make your website easier to navigate.

Readability and on-page experience: “Make content easy to read with clear formatting and subheadings; this helps with engagement and retention.

Wrap-up: Moving Forward to The Search of Tomorrow

Sematic search vs keyword search — which one should be used ultimately depends on your business requirements and the type of experience you want to create for the users on your platform. Semantic search is quite popular in e-commerce platforms where this type of search significantly improves how the various products are discovered and how the user are being engaged by grasping the queries in a contextual manner. In SEO, applying semantic strategies leads to more comprehensive, context-related content so it matches user’s search intent that helps to gain higher ranks and visibility.

Traditional search engines have a place when doing basic searches or when exact matches are needed. The future of search, however, is moving heavily towards semantic and vector-based approaches which place their emphasis on meaning, context, and user satisfaction.

Combining both approaches in a hybrid model or shifting towards semantic search engines are crucial moves to remain competitive and cater to modern user needs and evolving search patterns.

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