What is semantic search?

Semantic search is a sophisticated approach to information retrieval that goes beyond simple keyword matching to understand the meaning and context behind a user's query. Unlike traditional search engines that rely solely on finding exact keyword matches, semantic search leverages advanced language understanding and machine learning techniques to grasp the searcher's true intent and surface the most relevant results.

At its heart, semantic search is about interpreting queries the way a human would – by considering the relationships between words, their relative importance, and the implied meaning that extends beyond the literal text. This allows semantic search engines to handle complex, natural language queries and deliver results that align with the user's actual information needs.

The shift toward semantic search reflects the growing expectations we have for search technology. In an age of voice assistants and conversational AI, users increasingly expect search engines to engage with them on human terms. By extracting rich meaning from both queries and content, semantic search marks a significant step toward that ideal.

Fundamentals of semantic search

Semantic search is guided by two core concepts that allow it to interpret queries more like a person would:

1. Semantic meaning - Rather than treating search queries as a collection of keywords to be matched, semantic search engines analyze them through the lens of semantics – the study of meaning in language. They consider queries holistically and identify the most important concepts and entities, even if the searcher doesn't explicitly mention them. For example, in the query "what to do in New York", a semantic engine understands that the user is looking for tourist information related to a specific location.

2. Search intent - Semantic search engines also aim to discern the searcher's underlying intent – the reason why they are searching and what they're hoping to accomplish. Are they trying to navigate to a particular website? Research an unfamiliar topic? Compare products to make a purchase decision? By categorizing intent, search engines can prioritize different types of content in the results. A query like "best laptop for college" would surface product reviews and roundups, recognizing the implied commercial investigation intent.

By deeply analyzing meaning and intent, semantic search engines build a multi-dimensional understanding of the searcher's needs. This allows them to find relevant results that a traditional keyword-based approach would miss, and to rank those results in a way that reflects the searcher's priorities.

How does semantic search work?

Under the hood, semantic search engines rely on a technique called vector search to match queries with relevant content at a conceptual level.

In a vector search system, every piece of searchable content is converted into a numerical representation called a vector. These vectors encode the semantic meaning of the text, with each element representing a particular word or concept. Queries are similarly converted into vectors.

When a search is conducted, the query vector is compared against all the content vectors using a mathematical similarity function. This allows the search engine to assess the semantic relatedness of each piece of content and identify the best matches, even if they don't contain the exact same keywords as the query.

The power of vector search lies in the dense semantic encodings that underpin it. These vector representations are generated by advanced machine learning models that have been trained on vast amounts of text data to understand language at a deep level. Models like Word2Vec, GloVe, and BERT learn to map semantically similar words and phrases to nearby points in the vector space, effectively capturing their meaning.

By searching in this semantic vector space, search engines can understand that a query like "how to fix a flat tire" is conceptually related to content about "patching a punctured bike wheel" or "repairing a tire blowout", even without direct keyword overlap. This allows them to surface relevant results that keyword-based approaches would likely miss.

Semantic search vs keyword search

To appreciate the benefits of semantic search, it's helpful to examine the limitations of the traditional keyword-based approach that preceded it.

In a keyword search system, the search engine essentially looks for direct matches between the words in the query and its index of web page content. Pages are ranked by factors like the frequency and prominence of exact keyword matches, as well as the general popularity and authority of the page.

While this approach works reasonably well for simple navigational queries, it tends to fall short for more open-ended searches where the user may not know the exact terminology to look for. Some key drawbacks of keyword search include:

1. Lack of flexibility - Keyword search engines place undue importance on the specific words used in a query, ignoring potentially relevant pages that use slightly different terminology. For example, a search for "dog breeds" might not surface articles about "canine species" or "types of dogs", even though they cover the same topic. Users are forced to try multiple keyword variations to find comprehensive results.

2. Failure to understand intent - Because keyword engines focus narrowly on term matching, they often struggle to distinguish different intents behind queries. A search for "apple" could refer to the fruit, the technology company, or any number of other entities. Without understanding intent, keyword engines can't appropriately prioritize the most relevant meanings.

3. Susceptibility to manipulation - The ranking algorithms of keyword search engines are fairly easy to reverse engineer and exploit. Techniques like keyword stuffing - the gratuitous repetition of popular keywords on a page - can be used to artificially inflate search rankings and attract undeserved traffic. This adversely impacts result quality and user experience.

Challenges of keyword search

The shortcomings of keyword search can create real friction and frustration for users trying to find information online. Most of us can relate to the experience of searching for something, only to be inundated with irrelevant results that happen to contain our query terms.

This usually stems from the search engine's inability to truly understand what we're looking for. It gets hung up on the specific keywords we use, even if they're not the best representation of our actual intent. As a result, it fails to find content that discusses our topic using different terminology.

Keyword search engines also tend to be easily misled by pages that are deliberately stuffed with keywords to game the ranking algorithms. This leads to search results that might superficially seem relevant, but don't actually contain the substantive information the user is seeking.

The cumulative effect is that users have to work harder to find what they need, often trying multiple searches with different keywords to eventually arrive at relevant content. It's an inefficient and annoying process that feels increasingly archaic in an era of sophisticated language AI.

As our expectations for search continue to rise, the brittleness and inflexibility of keyword search will become ever more apparent. Users will increasingly demand the more intuitive and adaptable search experience that semantic search provides.

Benefits of semantic search

The shift toward semantic search is being driven by the significant benefits it offers over keyword-based approaches, for both users and businesses.

From a user perspective, semantic search makes it much easier to find relevant information with minimal effort. Because semantic engines grasp the meaning behind queries, users can express their needs in natural, conversational language without worrying about the "right" keywords to use. The search engine meets them where they are, accommodating their terminology and intent to surface on-point results.

This reduces the need for users to repeatedly rephrase their queries, as semantic search engines can find conceptually relevant content even when it uses different keywords. Users can also worry less about choosing overly broad search terms and getting drowned in irrelevant results, as the engine will focus on the most semantically relevant interpretations.

For businesses, semantic search offers a powerful tool to better understand and serve their audience. By analyzing the meaning and intent behind user queries, businesses can gain insight into what their customers are actually interested in and tailor their content and offerings accordingly.

This is especially valuable in contexts like e-commerce, where understanding the criteria a user cares about (e.g. price, features, etc.) can help highlight the most relevant products. Semantic search can also enable more personalized experiences by considering each user's individual context and preferences when ranking results.

There's also a strong SEO benefit to semantic search. As search engines get better at understanding meaning and prioritizing truly relevant content, businesses are incentivized to focus on creating high-quality, semantically rich content rather than just chasing keywords. This promotes a more authentic and user-friendly search experience.

Why is semantic search important for ecommerce?

Online shopping is an area where semantic search can be truly transformative. E-commerce search is rife with challenges that stem from the diversity of products, the varying terminology people use to describe them, and the different criteria shoppers have in mind.

Semantic search helps cut through that complexity by identifying the most salient product attributes for each query. For example, a search for "summer dress" might surface results prioritized by style, material, and occasion, recognizing the implicit criteria the shopper likely cares about. The semantic engine understands that "summer" in this context isn't just a literal season, but shorthand for a host of related concepts like light fabric, bright colors, and casual cuts.

Better Relevance

This semantic understanding can power more relevant and diverse product results. Instead of just showing items that match the query keywords, semantic search can surface products that address the underlying need or desire. For the "summer dress" query, it might include things like sundresses, beach cover-ups, and light formal dresses – items the shopper may be interested in even if they don't explicitly match the search terms.

Product Discovery

Semantic search can also enable powerful product discovery experiences by identifying conceptual product relationships. When a shopper is looking at a particular item, the search engine can recommend semantically similar or complementary products, even if they're in different categories or described differently. This helps shoppers find what they need and can drive cross-sell and up-sell opportunities for retailers.

Personalization

Personalization is another key application of semantic search in e-commerce. By understanding the meaning behind each user's queries and browsing behavior, search engines can tailor results to their individual tastes and needs. Two different shoppers searching for "blender" might see different products prioritized based on their past interest in either professional-grade kitchen equipment or quick-and-easy smoothie makers.

As e-commerce continues to grow and shoppers expect ever-more-relevant digital experiences, semantic search will be an increasingly important tool for connecting customers with the right products at the right times. Retailers that invest in robust semantic search capabilities will be well-positioned to drive sales and customer loyalty.

How to Implement a Semantic Search Engine

For organizations looking to build or integrate semantic search capabilities, there are a few key considerations and approaches to explore:

1. Do it yourself - Getting started with semantic search doesn't necessarily require building language understanding models from scratch. There are a number of powerful pre-trained models available, like Word2Vec, GloVe, and BERT, that can be used out-of-the-box or fine-tuned for specific domains. These models encapsulate semantic understanding learned from massive amounts of text data.

2. Integrate with existing search platforms - Many popular search platforms, like Elasticsearch and Algolia, now offer vector search capabilities or integrations. This allows organizations to incrementally add semantic search to their existing keyword-based search systems without ripping and replacing their current infrastructure.

3. Explore end-to-end semantic search solutions - For organizations looking for a more complete solution, there are end-to-end semantic search platforms like Vantage Discovery that provide a suite of tools for ingesting, analyzing, and searching content. These platforms handle much of the underlying complexity and provide higher-level interfaces for managing and tuning the search experience.

Ultimately, the right approach to implementing semantic search will depend on an organization's specific needs, resources, and existing infrastructure. But with the rapid advancement of language AI and semantic retrieval techniques, the barriers to entry are lower than ever. Semantic search is quickly becoming an essential capability for any organization that wants to deliver highly relevant, intuitive information access to its customers or employees.