The heliocentric model of the solar system, which challenged the idea that the sun orbited the Earth. Germs not being caused by “bad air” . The theory of relativity. These and other paradigm shifts have changed the world.

In ecommerce, there’s a smaller, though arguably just as key, paradigm shift quietly taking place right now: moving from traditional keyword search functionality to advanced semantic search. Gone are the days when search engines merely matched keywords to content containing those words and called it good. Semantic search engines are something new entirely: the latest artificial intelligence–aided versions can “understand” the context in language and the underlying intent in people’s search queries.

Semantic search represents such a colossal leap because it allows for a focus on searcher intent and contextual meaning, resulting in a comprehensive understanding of word relationships. In other words, it can understand search terms in a way that mirrors human thought processes. For businesses, that’s huge because it means by using semantic search, they can give online shoppers extremely relevant results on their search engine results pages (SERP), potentially leading to more high-volume sales.

Let’s look at what so authoritatively equips semantic search to empower both consumers who are seeking products and businesses that are seeking profits.

What is semantic search?

Semantic search is a breakthrough technology that can grasp the true meaning and context of words and phrases typed in user search queries, as opposed to just matching up keywords with corresponding content on web pages. It does this thanks to natural language processing (NLP), machine learning, and other AI techniques.

In terms of ecommerce, by semantically analyzing words and phrases, a semantic search engine infers a shopper’s authentic intent and responds with accurate search results.

To understand how semantic search works, it helps to consider the broader field of semantics (the study of meaning in language). Semantics takes into account:

• Word meaning
• Sentence meaning and structure
• Context
• Relationships between words
• Synonyms
• Cultural idioms

How a semantic search engine works

To process queries, semantic search engines employ a suite of advanced techniques, including:

Vector search

The secret sauce of semantic search is undeniably vector search, which makes the process possible at a practical and efficiency level. With vector search, text is converted into vectors — numerical representations of data. Then, the K-Nearest Neighbor (KNN) algorithm matches the vectors that represent items most similar in content or meaning to the query. Based on context and intent relevance, this method enables high-quality ranking and delivery of content.

Entity recognition

Entity recognition involves identifying and classifying the key components of a query or text in predefined categories such as people, places, organizations, products, and dates. Entity recognition allows semantic search engines to understand the specific subject of a query, facilitating more-accurate and relevant search results. For example, based on context, semantic search can distinguish between the company Apple and an apple.

Contextual understanding

Contextual understanding refers to ascertaining meaning and intent in a query. It looks at the words used, the shopper’s search history, and trends. By grasping context, a semantic search engine can discern the meaning behind ambiguous phrasing and terms that have multiple meanings, thereby enhancing the accuracy of search results.

Natural language processing

Semantic search engines use NLP, a field of AI, to understand the ways humans naturally write search queries, which may at times be conversational or complex. The process involves breaking sentences down into words and phrases, interpreting the language being used, and generating appropriate natural-language responses.

Knowledge graphs

Knowledge graphs are databases that rely on a network of interconnected entities and their relationships in order to understand and organize information. They represent real-world entities and the connections between them in a graph format. Semantic search engines utilize these graphs to understand the relationships between entities mentioned in a query. The result is provision of more-nuanced search results that reflect how concepts are related in the real world. For example, by examining the connections between entities, a knowledge graph can help a search engine know that the entry “Barack Obama” would be connected to the entries for “The President of the United States” and “Michelle Obama”.

Machine learning

Machine learning, a subset of AI, enables systems to learn from data, identify patterns, and make decisions with minimal human intervention. With semantic search, machine-learning algorithms analyze shopper search, interactions, and feedback in order to not only piece together what’s wanted but continuously improve the relevance of search results. By learning from shoppers’ past searches, a semantic search engine can better predict what people want, even if they’re entering vague-sounding or incomplete queries.

Semantic search: A history

Key events that have shaped the development of semantic search include:

1990s: Birth of Internet search engines

The invention of the World Wide Web catalyzed the development of the world’s first search engines, including AltaVista, Excite, and Yahoo, which gave shoppers a taste of navigating the vast expanses of the Internet. To reference web pages, these early engines primarily used keyword matching and link-analysis algorithms such as PageRank (introduced by Google).

2000s: Introduction of semantic web concepts

Tim Berners-Lee, the inventor of the World Wide Web, introduced the concept of the Semantic Web. He envisioned a more structured network in which data is interconnected and machine readable, enabling computers to understand and process the semantic aspects of information. This era saw the development of standards such as RDF (Resource Description Framework) and OWL (Web Ontology Language), laying the groundwork for representing and querying semantic data across the Web.

In 2007, Wolfram Alpha emerged as a computational knowledge engine that went beyond traditional search to answer factual questions through computation over curated datasets. By interpreting natural language queries semantically, it generated specific answers rather than lists of links. This showcased the potential of semantic processing in search technology.

2010s: Advancements in NLP

This time frame witnessed breakthroughs in deep learning and neural networks. These advancements enabled delving into the semantics of language, improving search engines’ abilities to understand intent and nuances in queries and web content, facilitating more contextually relevant search.

In 2012, Google unveiled Knowledge Graph, marking a shift from keyword-based search to a more nuanced understanding of queries and their contexts. This knowledge base used semantic information to enhance search results, providing shoppers with direct answers to queries, insights on related entities, and a richer search experience.

Google’s introduction of the Hummingbird algorithm in 2013 also represented a significant update that streamlined semantic search capabilities. Google was able to process more-complex questions and provide answers that more accurately matched user intent, further enhancing the search experience.

However, Hummingbird wasn’t to be the final tweak from Google that would lay the groundwork for semantic search:

• In 2015, RankBrain was released. This machine-learning component of the search algorithm enhanced Google’s proficiency in discerning the context and intent of complex search queries.
• The BERT update in 2019 helped decipher intent and the context of conversational searches with better precision.
• Launched in 2021, MUM was 1,000 times stronger than BERT and excelled at interpreting a wide array of data types (images, videos, audio).

Driven by ongoing advances in AI, NLP, machine learning, and knowledge representation, semantic search is evolving. These technologies are deepening semantic understanding of human queries, leading to more-nuanced interpretations and increasingly personalized search results.

Impact on ecommerce

Ecommerce sites big and small are leveraging semantic search to improve the online shopping experience. By assimilating the semantics of a search query like “wireless headphones with noise cancel”, Amazon.com, for instance, can not just infer specific product features the shopper might want but suggest alternatives, accessories, and bundles that reflect the intent.

Advanced semantic search technology has far-reaching implications across online commerce because it:

Improves search relevance

Semantic search’s ability to comprehend the context and intent behind queries means delivering search results that align closely with what shoppers are looking for, potentially increasing conversion rates and satisfaction.

Consider the user experience of an athlete preparing for a marathon. When they enter “best running shoes for marathons”, a traditional search engine might return a broad spectrum of results related to running shoes, with items suited for casual athletic pursuits and short distances. However, a semantic search engine, equipped with the capability to parse and understand the nuances of the query, recognizes the specific context. The shopper is not looking for just any old running shoes but a pair optimized for the unique demands of marathons.

Enhances natural language understanding

The effectiveness of semantic search lies in enhanced natural language understanding (NLU), which makes ecommerce search engines more user friendly. A platform can accurately interpret user queries regardless of complexity and use of conversational language. This improvement is invaluable for online interactions, such as those in customer service.

Consider customer service for a large online retailer. Traditionally, if a shopper queried something like “How do I return a gift without the receipt?”, the system might have struggled to provide the most accurate answer, focusing on keywords like “return” and “receipt” and have come back with generic information about the store return policy. With NLU, the platform can interpret the context and intent behind the shopper’s question. It recognizes that a gift is an item received by someone and understands the potential complication posed by the absence of a receipt. Consequently, the retailer can direct the shopper to guidance on returning their gift without a receipt.

Enables personalized recommendations

Semantic search engines go a step further for ecommerce vendors by analyzing customer behavior and preferences to deliver personalized recommendations. This capability is also a boon for entertainment media sites. Personalized recommendations in any industry mean shoppers are more likely to find products and services that resonate with them. Personalization improves the customer experience and boosts revenue and loyalty, with 76% of shoppers more likely to buy from companies that supply it.

For example, let’s say an online comic book and merchandise store utilizes semantic search to enhance the customer experience through personalized content recommendations. A shopper has a history of buying Batman comics and related merchandise. Using semantic search, the platform can ensure that contextually relevant products are recommended. When the shopper next visits the site, they’re welcomed with a section titled “Because you like Batman”, with curated recommendations from comics to Batmobiles, DVDs, and T-shirts. The search has understood not just the keywords gathered from the shopper’s site viewing history but the shopper’s preferences.

Affords better SEO opportunities

The shift toward semantic search also impacts SEO content creation, as content can be optimized to go beyond keywords to reflect deeper intent and context in queries. Businesses and content creators can align with the ways semantic search engines interpret and prioritize content, developing material that genuinely addresses user needs and questions, which can lead to better visibility and ranking in search results.

Consider a wellness-product retailer that’s been focused on keyword optimization around terms like “weight loss” and “healthy eating”. With the shift toward semantic search, the functionality begins to incorporate semantic SEO strategies, aiming to cater to the broader intent and context behind searches. So the site’s management starts assigning freelancers articles and guides that address concerns such as “How to maintain a healthy lifestyle with a busy schedule” and “Nutritious meal plans for weight loss”. Instead of sprinkling text with high-volume keywords, the content is structured to provide comprehensive help on these topics. This increases the content relevance, and, as a result, the retailer sees significant improvement in its search engine rankings for a broader range of health-related queries.

Goes beyond web search

The principles of semantic search also apply in terms of refining content for question-answering systems, chatbots, and virtual assistants. In this realm, too, semantic search can provide accurate, contextually relevant responses to inquiries, making digital assistants more efficient.

Powered by semantic search, a customer-service bot can comprehend a complex query such as “I received the wrong size shoes, how do I exchange them?”. Unlike with a traditional bot, the issue (wrong size) and request (exchange) are discerned so that the shopper can be easily guided through the exchange process. This improves the customer experience and boosts operational efficiency.

Applications of semantic search

Ways to use semantic search are spanning multiple domains beyond ecommerce. Here’s how that’s playing out:

Google search

Google’s search engine is the most prominent example of semantic search applied on a massive scale. It uses sophisticated AI and NLP algorithms to parse, understand, and respond to queries in a nuanced manner. For instance, when someone searches for “best Italian restaurants nearby”, Google doesn’t merely look for keywords; it analyzes the underlying semantics. This involves factoring in the searcher’s location, the qualitative meaning of “best” in the context of restaurants, and even people’s reviews. With that comprehensive input, Google search results can deliver intuitive recommendations that match the searcher’s implicit criteria.

Virtual assistants

Virtual assistants such as Amazon’s Alexa, Apple’s Siri, and Google’s Assistant can effectively process natural language queries because of semantic search. For example, asking a semantic-search-equipped assistant “What movies are playing nearby?” would prompt it to note the time of day, your physical location, and even your movie preferences, if available.

Enterprise search solutions

Semantic search facilitates efficient information retrieval across corporate repositories. Enterprise search solutions use it to comprehend the context and nuances of employee queries. Whether someone is searching for a report or a policy document or they’d like to tap a colleague’s expertise, semantic search makes it easy, facilitating knowledge sharing and productivity.

Semantic search challenges

Like other new technologies, semantic search has a few technical challenges. These issues include:

Language ambiguity

Some words and phrases have multiple meanings. For example, “bank” could refer to a financial institution, the side of a river, or tilting a plane. To determine the correct meaning of a given query, a semantic search system must discern the context, a task that requires sophisticated NLP algorithms and contextual analytical capabilities.

Cultural and linguistic variability

Semantic search systems must understand queries across languages. This challenge involves not just translating words but grasping cultural nuances, idiomatic expressions, and regional variations. For example, when a shopper from the UK searches for a jumper, they expect to get a selection of sweaters, but in Australia, a jumper is what Americans would call a jumpsuit.

Data quality and availability

Semantic search needs high-quality, structured data, which acts as the foundation for understanding relationships between words, concepts, and entities. However, sourcing reliable and comprehensive data sets, particularly in niche and specialized domains, poses a challenge. Plus, the dynamic nature of language and information means continual updates and maintenance are needed to keep data sets relevant.
Scalability

As digital information stores grow at an unprecedented rate, semantic search systems must efficiently scale to handle all the data and maintain high performance and accuracy in search results. The challenge lies in developing algorithms and infrastructure capable of processing and analyzing large data sets in real time, ensuring that search stays responsive and up to date.

Privacy and security

Because semantic search involves analyzing user data to understand intent and preferences, privacy and security concerns must be prioritized. Semantic search providers must implement robust data protection measures, and the collection, storage, and processing of personal data must be handled with care.

The future of semantic search

Semantic search is pointing us toward a future in which technology not only better understands human language but can discern people’s preferences and anticipate human needs. Here are a few predictions about where semantic search is headed:

Enhanced personalization and context awareness

The evolution of semantic search will bring unprecedented levels of personalization and context awareness to search experiences. For example, imagine a search engine that, beyond recognizing your query, understands your long-term projects and goals. For instance, if you’re planning a trip to Japan, your query “best time to visit” might return results not just for general tourist season information but for events specifically tailored to your interests, such as cherry-blossom festivals.

Integration with augmented reality (AR)

Augmented reality technologies, when combined with semantic search, will overlay our physical world with rich, interactive, context-sensitive information, which will undoubtedly transform tourism and education. For instance, while touring a historic site, simply by scanning the surroundings with your smartphone, you’ll be able to get real-time information about the area’s history, architecture, and cultural artifacts.

Specialized applications

Semantic search is expected to revolutionize specialized domains by offering more nuanced and accurate information-retrieval capabilities. This will impact:

• Healthcare: Semantic search will simplify access to the latest research and quicker diagnoses by letting doctors and researchers query for medical records and literature using complex natural-language questions. A semantic search system will accurately interpret a query like “the most effective treatments for early-stage melanoma considering patient age and comorbidities”, then provide customized treatment options.
• Legal research: The legal domain, with its vast repositories of case law, statutes, and other literature, will benefit significantly. With queries phrased in natural language or that contain specific nuances, lawyers will be able to quickly locate precedents and relevant arguments.
• Financial services: Semantic search is slated to transform how analysts and investors access and interpret market data, regulatory updates, and economic research. By understanding the semantics of financial terminology and concepts, search tools will provide more-relevant results for queries such as “impact of interest rate changes on tech stock performance in Q3”.
  Tap semantic search for higher revenue

Want to get your revenue to new territory? At Algolia, we offer semantic search as part of our AI Search product, and we can’t wait to show you how it works. Contact us and let’s get you started.