What is Semantic Search & Why It Matters for SEO
Businesses can provide more value to their target audience’s needs by using modern semantic search techniques.
You’ve meticulously created your web of data so that it’s rich in internal and external customer information. But to truly be effective, their contextual signals need to be carried from one to another, with a consistent topic hierarchy. When the entity is not well known and there are missing facts, open information retrieval (Open IR) finds unidentified entities. This information is then clustered with semantically related themes. Understanding candidate question answers and how the questions are ordered is part of semantic SEO expertise. This is where Semantic search involves machine learning. We’ll start by providing definitions and background concepts.
Table of Contents
- What is Semantic Search?
- What is Semantics?
- What is the Semantic Web?
- Do Semantics Matter in SEO?
- How Does Semantic Search Work?
- How to Use Semantic SEO to Build your Cloud of Web Data
- 1. Semantic SEO success begins with marketing research
- 2. Compose text based on semantic analysis and co-occurrence
- 3. Take the view of search engines
- 4. Use Google Search Console Insights
- 5. Adapt a multimodal approach to SEO
- 6. Answer questions using Knowledge Graphs & Google Business Profile
- 7. Add structured data for text, images, products, and videos
- 8. Stay current with Google updates
- How are Traditional SEO and Semantic SEO Different?
- Summarizing the History of Semantic Search
- Semantic Topic Models in Semantic Search
What is Semantic Search?
Semantic Search is searching by meaning. It represents search with connotation, which is different from lexical search where the search engine seeks literal matches of the query words or variants of them, without understanding the overall gist of the query. It refers to a search engine’s endeavor to produce the most relevant and trusted SERP results possible that align with searchers’ intent.
Semantic Search is also termed Neural Search and leverages state-of-the-art deep learning algorithms to display contextual and relevant results to user queries. It involves entity-oriented search which relies on connected content that is clustered correctly as a knowledge base.
What is Semantics?
“Semantics”, according to Oxford Languages, is “the branch of linguistics and logic concerned with meaning. There are a number of branches and sub-branches of semantics, including formal semantics, which studies the logical aspects of meaning, such as sense, reference, implication, and logical form, lexical semantics, which studies word meanings and word relations, and conceptual semantics, which studies the cognitive structure of meaning.”
What stands out to me is the difference between the “meaning of queried words” (semantic search) and the “matching of words in a query” (lexical search)”.
In the E-Commerce space, lexical search identifies products by matching words and their variants; in contrast, semantic search identifies products by meaning and context.
When we really want to understand a person that we are talking to, we seek to grasp what we believe they are trying to say. The concepts, ideas, and needs conveyed by their words combined with a “feeling” created by the way they are expressed or the context of the conversation are foundational to having a wise response. Similarly, semantic analysis is making any topic (or search query) easier for machine learning to decipher intent and respond more as a human would.
Here is where sentence semantics (sentential semantics), as well as phrasal semantics, have a role in structuring content semantically.
Sentence semantics (sentential semantics), as well as phrasal semantics, deals with the meaning of syntactic units larger than words, such as phrases, clauses, and sentences, and the semantic relationships between them. Prepositions play an important role in analyzing the meaning relations among sentences and within them. For example, a person’s location and footwear may be entirely different if they say “best shoes going to the gum” versus ”best shoes at the gym”. At LifeTIME fitness, we’re to always change out of our street shoes before entering the workout area. Here the small words “going to” and “at” change the context of which shoes are best.
Prepositions show direction, time, place, location, spatial relationships, or introduce an entity. Semantic search is a data searching technique that takes big clues from prepositions and statements before and after a keyword phrase. In semantic SEO every word in a sentence can have more value.
What is the Semantic Web?
The Semantic Web offers a widely accepted framework that empowers data sharing and refining across application, enterprise, and community boundaries.
Google has been relying on semantic analysis to build a better knowledge base of what each topic consists of, its connected nodes, and how relevant each web document or passage is to each search query. Topical authority and the semantic web are discussed more frequently as SEO’s refine their concept of Structured Search Engines. Since it continually evolves, definitions do too.
In a recent 2021 report, Gartner emphasized that semantic search is essential to effective knowledge and content management. The reasoning identified its ability to “(amplify search) performance by analyzing the underlying meaning of documents/records, as well as the queries posed to retrieve them.” Auto-tagging capabilities permit applications consuming content to leverage rich metadata – a nice improvement in customers’ semantic web search experiences through personalization and intent-based recommendations.
The adage “You shall know an object by the company it keeps” expands on how Firth in 1957 summarised this concept as “you shall know a word by the company it keeps”. Semantic SEO expands to incorporate entity relationships enriched by image and audio as well. Further study of semantic representations derived from object co-occurrence in visual scenes utilizes image metadata labeling that provides image-based information more directly into representational models.
Do Semantics Matter in SEO?
Before semantic SEO was implemented, Internet systems were less optimal for people to find answers and products.
Semantic search assists a better prominence in relevant search results. It improves the quality and quantity of organic traffic to a website or a web page from search engines. At the same time, it adds another layer of complexity to search engine optimization. More advanced tasks like topic research, managing your data layer, visual search, podcasting, and content optimization in new ways. A semantic layer is a business representation of overall company data and vocabulary that helps end-users access data autonomously using ordinary business terms.
The visual presentation of your brand, products, services, and concepts has become a larger player in this form of search. This expands both the tasks and reach of your business.
Voice search assistive technologies can increase the semantic elements of a web page. Semantic HTML doesn’t take any longer to write than non-semantic content. Both HTML and semantic markup need to follow standard guidelines. This is one way an SEO optimizes your content. Additionally, one of the best accessibility aids a screen reader user can have is client-side methods leveraging accessibility APIs to retrieve and handle the information displayed by browsers.
How Does Semantic Search Work?
Semantic search works by driving relevant web traffic and answers people’s questions by understanding the meaning of their queries. Semantic markup assists by providing information in data formats that search engines understand.
People often do not always search by using the exact match words that a web page uses. Their personal search history plays in. Your web content can be more useful than a word-for-word answer to their query. Search has become far more conversational than ever before. Google, Bing, and other search engines need to return relevant results to please users. This means the continual need to evolve and adapt to this way of searching and machine learning management of search responses.
In the Ahrefs’ What Is Semantic Search? How It Impacts SEO July 29, 2021, article, Michal Pecánek expounds on how around 40% of English words are polysemous. In the United States English language, many words have two or more meanings. This creates a huge challenge for semantic search to solve.
For example, you may need a fan on a hot day; or you may be a fan of the Vikings football team. Michal used the example that the keyword “python” has 533,000 monthly searches in the US alone! This makes it clearer why search engines needed to evolve semantically.
How to Use Semantic SEO to Build your Cloud of Web Data
Semantic SEO is completely revolutionizing modern SEO techniques. Here are steps you can take:
1. Semantic SEO success begins with solid marketing research.
2. Compose text for your content marketing based on semantic analysis and co-occurrence.
3. Take the view of how search engines understand and rate your content.
4. Let your Google Search Console Insights inform your SEO strategy.
5. Adapt a multimodal approach to SEO.
6. Answer questions using Knowledge Graphs and your Google Business Profile
7. Add structured data for text, images, products, and videos.
8. Stay current by knowing what Google is saying about its search updates.
Here are 8 steps to take that will improve how your business shows up in semantic search. Let’s advance your semantic SEO skills by looking at each step in more detail.
1. Semantic SEO success begins with marketing research.
Market research and the study of how words help businesses reach the right audience are foundational. It helps to identify word meanings, relationships, and their connections in text. Evaluating search snippets returned for a particular search query helps us build entire sentences that can be richer with meaning and value.
Semantic SEO has become a needed marketing technique to improve the traffic of a website by providing search engines with meaningful data. But you need to start with solid research and clear goals.
2. Compose text based on semantic analysis and co-occurrence.
Semantic analysis is a niche of linguistics that discovers connections among concepts and entities (i.e. names of places, people, events, brands, etc.). Google’s algorithm “thinks” bigger than words and evaluates more human-like context. It’s called Natural Language Processing (NLP) which is used by BERT and MUM’s information retrieval processes that try and determine what a searcher really wants, and then match it to web content that provides that. It have revolutionized what it takes to achieve success in content marketing in competitive markets.
Google has engaged semantic analysis to construct an unparalleled, so far, comprehension of what each subject’s composition and therefore how important each net document is to every search query. This means that before writing a new content piece or revising an existing one, research can help to gain an overview from automated textual analysis. The details provide marketing insights that are useful for semantic writing.
3. Take the view of how search engines understand and rate your content.
This will inform how to create SEO themes for your content that match what Google is looking for that meets searchers’ needs. This goes well beyond keywords and takes us into “entity vocabularies”. Create content clusters that are semantically grouped into topics instead of keywords. Search engines may potentially leverage applications like classification, semantic similarity, semantic clustering, whitelist applications, which involves selecting the right response from many alternatives.
Each search query is an individual query that has a tangible entity. Google identifies what the entity is and how that person’s query is requesting information about the entity. Acceptable content is cataloged by search engines and organized around each context in such a way that machines can understand, value its uniqueness, and then match it to queries.
Artificial Intelligence (AI) teaches machines to think and—more importantly—interpret and take action as human beings do. This area of computer science is rapidly expanding in ways affecting all areas of our lives: how we gain healthcare-related advice, how we find the right product, how businesses communicate who they are. In our context, this greatly influences content marketing.
4. Use Google Search Console Insights to inform your SEO strategy.
Google Search Console User data reports help to identify where and when a specific web page about a topic is ranking for multiple related search queries. Natural language understanding has evolved remarkably in recent years. The development of word vectors makes it easier for algorithms to learn about word relationships, trained on examples of real human language usage. The Performance report shows important metrics that reflect how well your text and semantic relationships influence Google Search results. You can learn how often a query comes up; average position in search results; click-through rate; and about special features (such as rich results) that you’ve gained in SERPs.
The Google Search Console lets you filter reports by Queries, Pages, Search Apprearance, and more. You can use these insights to improve your semantic SEO so that you can write content with improved topical depth.
5. Adapt a multimodal approach to SEO.
Multimodal search includes imagery that we see, what we hear, and the words we read.
This includes using contextual and conceptual taxonomies and hierarchies along with Topical Graphs. It requires a step-by-step process for semantic SEO writing to satisfy every macro and micro search intent. Make your content rich with various modalities like video, audio, tables, infographics, and such. This helps a multimodal search engine have more relevant content by which it can verify your topic authority.
Modern Semantic SEO includes schema markup implementation that can increase SERPs clicks. There are essential eCommerce structured data types that increase search engines’ understanding of products. This offers a way for shoppers to find answer information as they conduct product research.
As people try collecting facts about entities and possible purchases, retailers can leverage schema to gain visibility in Google’s SERP real estate. Google is surprising us with its multimodal search and how often new SERP features are being tested and rolled into its search core.
6. Answer questions using Knowledge Graphs and your Google Business Profile
Both Knowledge Graphs and a company’s Google Business Profile are easy ways to provide answers to common questions. You can influence your knowledge graph so that the answers you provide are displayed prominently. In this way, a site can support queries and predictively anticipate consumers’ needs.
Search will collect information about you from the Web of Data and use it to answer questions that are relevant. Semantic SEO holds great promise. Just like smart devices create smarter cars, security systems, home appliances, and global email connections, every brand should remain alert and of key technologies that will drive change and have agile responses.
Take identifying consumer queries one step further and align with consumer goals at each step of the purchase journey. Semantic query parsing can move to route queries to concept-specific models. Answer the questions most relevant in each phase of relationship-building. Your business can consistently provide answers and solve problems through your Knowledge Graph. Also, semantically related nodes may connect the products featured in your Google Business Profile to your product pages. The user can select a link associated with one of your listed products.
7. Add structured data for text, images, products, and videos.
Structured data adds valuable data points that are easily understood by search engines. The more high-quality metadata and structured content that is provided, the more help is given to semantic search engines. It assists your data footprint and the process of matching content to user intent. Using structured data contributes to a semantically enhanced web. Given the rapid growth and development of semantic markup and related technologies, it’s best to have a skilled person assigned to audit, fix, and implement broken or new product structured data markup.
Schema.org (often referred to as “schema”) is a semantic vocabulary of tags (or microdata) that when added to your HTML code improves how search engines read and understand it. When you become a recognized and trusted entity in Google’s Knowledge Graph it boosts Google’s understanding of who you are and your expertise. It can correct misalignments and also have an immediate impact on image and video query filtering that refers to the entity.
Schema is not only recognized by Google, Bing, Yahoo!, and Yandex, in addition, this semantic vocabulary is maintained by them. Additional search engines are likely following the practice of using this markup to change how they display search results.
8. Stay current with Google updates.
We certainly know this step isn’t easy! However, when you love the learning process, so that you can “think” like a semantic search engine, the results are so rewarding.
Google Posts, articles, and forum answers involving Semantic SEO commonly include information about structured data, schema, ontology, knowledge graphs, and document retrieval. Here are a few examples below that offer a lot of insight:
“Sometimes Google Search will show special boxes with information about people, places, and things. We call these knowledge panels. They’re designed to help you quickly understand more about a particular subject by surfacing key facts and to make it easier to explore a topic in more depth. Information within knowledge panels comes from our Knowledge Graph, which is like a giant virtual encyclopedia of facts.” — Danny Sullivan, May 2020
“Natural language understanding has evolved substantially in the past few years, in part due to the development of word vectors that enable algorithms to learn about the relationships between words, based on examples of actual language usage. These vector models map semantically similar phrases to nearby points based on equivalence, similarity or relatedness of ideas and language.” – Rachel Bernstein, Product Manager in Google Research, April 2018 in Introducing Semantic Experiences with Talk to Books and Semantris
We can also learn a lot about Semantic Search technology by reading Google patents.
For example its Semantic search engine patent WO2008027503A3 says “An ontology is parsed to determine a plurality of keywords. A string-based search engine is utilized to perform a search of documents on a network based on the determined keywords, and at least one document is retrieved. A relation is established between the retrieved document and the ontology, and it is determined if at least one document is to be stored in the database based on the established relation. If so, the document is stored in the database. The database can be used as part of a standalone or plug-in search engine for retrieving online documents.”
System and Method for Semantic Search in an Enterprise Application US20100070517A1 is another patent of interest. The application was granted in 2012 and states: “An ontology for the application that describes semantic relationships among data associated with the application may be generated from the searchable data definitions. The ontology may be used to execute search queries and provide search results that include or result from semantic relationships among the searched data.”
Google patents help us discover details about how Google may collect information about entities sourced from web pages. We can learn how it may apply natural language processing for information retrieval and entity recognition to build triples (Subject/Verb/Object) for specific entities.
How are Traditional SEO and Semantic SEO Different?
SEO has always embodied the Web ecosystem. When done correctly, it has always helped connect site owners to consumers. In traditional SEO, web pages gain a ranking position in search results based upon information retrieval scores that are calculated on relevance and expertise, authoritativeness, and trustworthiness signals (E-A-T).
Semantic SEO is more advanced and goes even further. It relates better to real-world objects or entities. An article about entities provides information about the multiple elements of those entities, such as facts about the different attributes that expound on those entities, and identifiers that may assist a person in learning about a specific entity to gain more knowledge about it. Even though this is accomplished by a machine, Semantic SEO thereby triggers ongoing communication and relationship-building opportunities that benefit both the consumer and the business.
An effective Semantic SEO page may gain knowledge panels, answer cards, search carousels compromised of entities, question-answer featured snippets, a presence in People Also Ask boxes, People Also Search For, and more. Where people may find related questions is to crowdsource them by studying query logs for related questions in a question graph. Studying Google SERPs reveals that Semantic SEO helps find real-world objects in queries that searchers use. You can identify featured snippets that answer questions many people ask about those entities.
Advancement in the field of artificial intelligence and related areas helps with visualizing semantic relationships. Visualization tools, like a topic browser, summarize a document collection and output inter-topic and document-topic relations.
What is a Semantic Network?
A semantic network, also known as a frame network is a knowledge base that connects semantic relations between concepts in a network. It is an entity of knowledge representation. A Semantic Network conveys the relationships within a knowledge base having the purpose of relating real-world node details. It may consist of thousands of varients with billions of relational entities, and trillions of factual information.
If you are feeling overwhelmed while reading this, know that the Semantic Web isn’t a different Web. Rather, it expands on the existing one, so what you already know is probably a good knowledge base. The Semantic Web is where information obtained by search has a well-defined meaning. The end result is that search engine technology serves people in a way that reduces friction and gets them better search results faster.
Summarizing the History of Semantic Search
A review of Wikipedia’s entry about semantic search reveals how it is progressing. Earlier, the semantic web seemed to focus more on linked data.
“Semantic search attempts to augment and improve traditional research searches by leveraging XML and RDF data from semantic networks to disambiguate semantic search queries and web text in order to increase relevancy of results.”
Come 2009 Wikipedia’s entry was updated to include ontology and the semantic web reference:
“Other authors primarily regard semantic search as a set of techniques for retrieving knowledge from richly structured data sources like ontologies as found on the semantic web.”
Only a year later the initial sentence updated to include entities of searcher intent and contextual meaning:
“Semantic search seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the web or within a closed system, to generate more relevant results.”
Then in 2019 the first paragraph reflects advances with BERT and neural matching:
“Semantic search denotes search with meaning, as distinguished from lexical search where the search engine looks for literal matches of the query words or variants of them, without understanding the overall meaning of the query. Semantic search seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Content that ranks well in semantic search is well-written in a natural voice, focuses on the user’s intent, and considers related topics that the user may look for in the future.”
Contributing authoritative authors align with the evolution of the meaning of semantic search. It seems to be progressing in a more conversational manner that simplifies search for users.
Semantic Topic Models in Semantic Search
Google has also won multiple patents covering various aspects of phrase-based indexing. We can learn how it may use thematic topic modeling of related words in documents and link anchor text to understand what web pages are about. Topic modeling is part of a class of text analysis techniques that analyze a document’s probability of association with a given theme or topic.
Years ago, the 2013 NIH Phrase Based Topic Modeling for Semantic Information Processing in Biomedicine article grounded my research of semantic topic models. Zhiguo Yu explains “In general, single words convey less information than phrases. Some verbs or prepositions are even meaningless without related words. For example, the meaning of ‘magnetic resonance imaging’ cannot be completely determined from any one of these three words in isolation, ‘magnetic’, ‘resonance’ or ‘imaging’.”
Named entity and relation-based topic models are used more often today.
“Semantic search is the search based on meaningful and reasonable behavioral patterns that come from thoughts, concepts, and things from the real world. Jeannie Hill created one of the most comprehensive documents for semantic search. If you want to learn how those meaningful patterns on the open web can be seen, gathered, clustered and used in a relevant understanding process for ranking purposes, I recommend you to bookmark the document, and read from time to time for keeping yourself sharp for Semantic SEO.” – Koray Tuğberk GÜBÜR
Advance Your Semantic SEO Today!
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Hill Web Marketing helps retailers use advanced semantic search for product discovery. Our specialty is also helping healthcare-related sites meet the critical needs of YMYL queries by creating, structuring, and publishing content that aligns with the rigors of semantic search.