[[{"@type":["BlogPosting"],"@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/#BlogPosting","@context":{"@vocab":"http:\/\/schema.org\/","kg":"http:\/\/g.co\/kg"},"url":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/","publisher":[{"@id":"https:\/\/www.schemaapp.com\/#Organization"}],"audience":"https:\/\/schema.org\/PeopleAudience","inLanguage":[{"@type":"Language","@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/#BlogPosting_inLanguage_Language","name":"English"}],"mentions":[{"@id":"https:\/\/www.schemaapp.com\/entity#Thing3"},{"@id":"https:\/\/www.schemaapp.com\/entity#Thing19"},{"@id":"https:\/\/www.schemaapp.com\/entity#Thing13"},{"@id":"https:\/\/www.schemaapp.com\/entity#Thing5"},{"@id":"https:\/\/www.schemaapp.com\/entity#Thing6"}],"dateModified":"2023-09-28T18:41:37+00:00","headline":"Semantic SEO: What You Need to Know","datePublished":"2023-06-23T20:01:27+00:00","image":[{"@type":"ImageObject","@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/#BlogPosting_image_ImageObject","url":"https:\/\/www.schemaapp.com\/wp-content\/uploads\/2023\/06\/What-is-semantic-SEO.png"}],"mainEntityOfPage":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/","name":"Semantic SEO: What You Need to Know","articleBody":"In the past, publishers would optimize content for keywords to please search engines and improve rankings. As a result, the search engine results page (SERP) returned results containing poor-quality content that often failed to answer user queries.\nFast forward to today, search engines now prioritize positive user experience and \u2018people-first\u2019 content. Search engines consider content depth, meaning (aka semantics), and how it answers user questions by providing the desired information.\nBusinesses must adapt to this evolution of search. As search engines become more sophisticated, incorporating semantic understanding into your search engine optimization (SEO) strategy is crucial to keep up with the changing landscape. This will help ensure your content remains relevant and visible to your target audience.\nUnderstanding Semantic SEO\nThe word \u2018semantic\u2019 is all about understanding the meaning of language.\nWhen people use the term \u2018arguing about semantics\u2019, they\u2019re usually debating the interpretation (or misunderstanding) of words or phrases. Semantics is a field that examines how language conveys meaning and follows certain rules for effective communication.\nSemantic SEO is the process of giving more meaning and context to your web content to help search engines gain a better understanding of your content.\nWhy is Semantic SEO important?\nThe way that search engines understand your content has changed\nHistorically, Google solely used keywords to evaluate a web page\u2019s topic and relevance to a search query. As of Google\u2019s algorithm changes made in 2013, however, instead of only looking at keywords to understand what the page is about, search engines now read and understand a page\u2019s overall topic.\nThis change allowed search engines to provide users with a better search experience and ensure that the results presented are providing users with the answers they are looking for.\nTo improve your ranking and web traffic\nBy utilizing semantic SEO, search engines can better understand your content and more accurately relate it to search queries. In return, your pages can rank higher on relevant searches, leading to more impressions and, ideally, more clicks. \nBecause it presents users with the most relevant information based on their queries, those who do visit your pages are more easily converted into customers. This is because it\u2019s more likely to be exactly the information\/product\/service they were seeking. \nTo keep up with generative AI search\nSemantic SEO is the future of search, and that future has already begun. The emergence of powerful generative AI search engines like Google\u2019s Search Generative Experience, has propelled semantic technology to unprecedented heights.\nIn this transformative era with the AI revolution and search generative experience, search engines are gaining an unprecedented ability to interpret the nuances and meaning of human language. As a result, search queries are now returning dynamic and tailored results with the potential for conversational follow-up answers.\nWhile traditional SEO practices, including keyword research, remain valuable in digital marketing, integrating semantic technologies like Schema Markup into your strategy can provide a competitive advantage.\nBy doing so, your pages become more visible and comprehensible to the intelligent systems that bridge the gap between your content and human users.\nPreparing for Generative AI Search: Essential Strategies and InsightsLearn about the benefits and challenges of generative AI search engines, and three key strategies that you can take to prepare for AI search. \nDownload eBook\nHow is Semantic SEO Different From Traditional SEO?\nWhere traditional SEO prioritizes content that is keyword-based, semantic SEO is a topic-based approach that increases the likelihood of connecting users to information that is most relevant to their search query. \nIt accomplishes this by focusing on both the meaning behind queries and the contextual information and relationships in the content being retrieved. This results in a better user experience which can lead to a lower bounce rate, as those who end up on your page from search have a higher intent to consume the information presented.\nSemantic SEO is the bridge between your content and users\u2019 intent. This is the biggest difference between Traditional SEO and Semantic SEO. \u2013 WeDevs\nMoving from a keyword-based to a topic-based approach with your content can seem a bit abstract at first. After all, it\u2019s simple enough to do some keyword research, find a list of terms, and then write content to string the terms together.\n\nThese same skills are still essential when it comes to semantic SEO, with one key difference: entities.\nWhat are Entities?\nTo put it plainly: entities are things, and things have dimensions! \nThey take up space (be it physical, digital, or conceptual). They also have attributes (like colour, size, duration) and, most importantly, they are understood in relation to other things.\nTake, for example, \u201cbestgihrtie\u201d. This is a string of characters and it means nothing to a human brain, so it won\u2019t mean anything to a search engine either. But if I decide it\u2019s the name of my new album, snackfood, or generative AI tool, this jumble of letters now becomes an identifiable entity. In other words, the string becomes a thing.\nHowever, that entity needs to be described for it to have any meaning. \u201cChatGPT\u201d didn\u2019t mean anything until we started hearing about it in relation to generative AI, chatbots, and productivity. \nThis same entity took on a different meaning when we heard about it in relation to hallucinations, misinformation, algorithmic bias, and plagiarism. The word \u201crelation\u201d is doing the heavy lifting in this example since what it\u2019s providing is context. \nWe as humans use context clues to make sense of new things and search engines are doing the same thing.\nThat being said, machines, including search engines, aren\u2019t good at understanding in the same way that human brains can. Search engines use natural language processing (NLP) to analyze the proximity and frequency of certain terms, phrases and entities.\nThere are ways, however, to make statements about entities more explicit for search engines.\nElevating Search with Entities\nAs previously stated, semantic search goes beyond traditional keyword matches and focuses on delivering topically relevant search results.\nInstead of simply providing \u201cplain blue links\u201d to web pages, it can present information in various formats, such as Knowledge Panels, Featured Snippets, and Rich Results, all centered around the primary entity being searched.\nThis approach aims to provide users with more comprehensive and contextually relevant information related to their search query. Let\u2019s look at an example of how a search for \u201cGibson Les Paul\u201d yields results about this particular entity.\n\nUnder the \u201cPeople also ask\u201d section, we can see queries that don\u2019t blatantly name the type of guitar, like: \u201cHow much did Kirk Hammet pay for Greeny?\u201d. \nGreeny is a 1959 Gibson Les Paul Standard, named after its original owner, Peter Green. It happened to be purchased by Kirk Hammet, the guitarist of Metallica, which also explains the inclusion of the question \u201cWho is the richest member of Metallica?\u201d, which has nothing to do with guitars at all.\nBut if we think about this information as being derived from entities that are related to one another, the inclusion of these \u201cPeople also ask\u201d queries make sense. \n\nAnd if we search for \u201cGreeny guitar\u201d, we\u2019ll get a Knowledge Panel conveying some of the attributes of this particular guitar, including the fact that its manufacturer is \u201cGibson\u201d.\n\nLeverage Schema Markup to Improve Your Semantic SEO \nThere are many things you can do to implement semantic SEO. A lot of it involves creating clusters of content surrounding the topic that you want to be known for.\nHowever, in addition to this, you need to ensure search engines understand what your content is about and how the entities in your content are connected. Implementing Schema Markup allows you to categorize entities and explicitly relate them to each other, providing search engines with helpful contextual information about your content.\nSchema Markup, also known as structured data, is a standardized vocabulary that search engines analyze to understand the content on your web pages. By implementing Schema Markup through code, such as JSON-LD, search engines can contextualize your content and present it to users searching for relevant and related topics.\nWhile machines don\u2019t interpret information like humans do, Schema Markup helps bridge the gap. It does this by providing explicit details about the content on your pages, ensuring search engines accurately comprehend the topics of information your website offers.\nOne of the most common uses of structured data is the application of the Schema.org vocabulary expressed in JSON-LD. It\u2019s usually found under the \u201ctechnical SEO\u201d umbrella, and most would know it as the \u201cThing\u201d responsible for rich results.\n\nRich results can drive higher click-through rates with their engaging visuals, but if that\u2019s the extent of your Schema Markup application, your semantic SEO strategy is missing out!\nSo how can you leverage Schema Markup to improve your semantic SEO?\n1. Implement more specific Schema Markup to clearly explain what your page is about\nTo be semantic, search engines need to clearly understand your content.\nContent publishers often use generic Schema Markup plugins to add default Schema Markup on certain pages like blog articles, product pages, home page, etc. However, the downside of doing this is the lack of control over your Schema Markup.\nGeneric Article markup autogenerated by plugins won\u2019t give your content the richly descriptive Schema Markup that best supports the search engines.\nPlugins are usually CMS-specific and tend to map more general properties to available metadata (like author, or datePublished). While these properties are still helpful, they don\u2019t describe the content with as much depth as more specific properties like about or mentions, which can be used to call out topics and entities in an Article.\nYour markup will also often be disconnected. Each page may have Schema Markup describing the content, but not necessarily how that content relates to other pages across your website.\n2. Add @ids in your Schema Markup\nYour Schema Markup can be generated and authored without including identifiers (@id). Search engines like Google will still read it and make it eligible for rich results.\nIn the JSON-LD syntax, @id is used to provide URIs (uniform resource identifiers) to entities in your Schema Markup. These identifiers allow you to refer back to entities as you build your knowledge graph.\nIn the example below, the Organization entity created for Schema App\u2019s homepage has the @id \u201chttps:\/\/www.schemaapp.com\/#Organization\u201d. If a blog post on another page wants to say that it was published by the Organization Schema App, the Schema Markup for that page would say the publisher is \u201chttps:\/\/www.schemaapp.com\/#Organization\u201d.\n\n@ids give the entities in your markup unique identifiers.\nThink of it like your social insurance number! There may be 10 different people named \u201cJane Doe\u201d in your organization, but each of them will have a unique ID that differentiates them. Schema App auto-generates @ids for every entity, so you can link the unique entities across your website.\n\nTherefore, if you want to improve your semantic SEO, you should add @ids to your JSON-LD Schema Markup.\n3. Connect your Schema Markup to develop your knowledge graph\nEstablishing a connection between your Schema Markup elements is crucial for developing a comprehensive knowledge graph. Knowledge graphs are necessary for describing how things on your site are related to each other, as well as other things on the internet.\nIt makes your content more semantic and provides search engines with contextual knowledge about your content.\nConnect Your Entities On Your Website\nOn your website, you can connect different entities to one another.  For instance, if you have a law firm with multiple service pages, it\u2019s important to connect those service pages to your organization. This indicates that your organization provides all of those services despite them being on separate pages.\nTo ensure accurate representation, it\u2019s vital to describe the relationships between marked-up entities in detail. For example, you need to clarify if an article is about a specific topic or if it simply mentions it.\nSchema App offers a free Schema Path tool that helps identify available properties to connect your entities effectively.\nConnect Entities to External Authoritative Knowledge Bases\nYou can also connect entities on your site to external authoritative knowledge bases such as Wikidata or Wikipedia. By doing so, you are clearly explaining what your entity is about.\nFor example, let\u2019s say your page talks about football. Football can mean two different sports to different readers. In America, football is American football while in Europe, football is soccer.\nSo if your page is about American football, you can link it to the Wikidata entity (https:\/\/www.wikidata.org\/wiki\/Q41323) for American football in your Schema Markup using the sameAs property. This will help search engines understand that your page is referring to American football and reduces the risk of misinterpretation.\nBy connecting entities on your site to other entities and external knowledge bases, you are forming your own knowledge graph. The @ids that we mentioned earlier clearly identify the entities in your content, allowing you to connect them and build context.\nWith Schema App, you have the flexibility to add these entities either manually through our Editor or automatically through the Highlighter, utilizing the Linked Entity Recognition feature. For WordPress users, our WordPress plugin can automatically identify and link entities that you have included in your tags and categories.\nDownload our Guide to Connected Schema Markup to learn how to connect the entities on your site and build your knowledge graph. \nThe Future is Semantic\nWhen creating website content for SEO, it\u2019s important to prioritize semantic SEO that focuses on topics rather than just keywords. Search engines now understand context, relationships, and user intent better than ever before.\nTo stay competitive on SERPs, you need to create relevant, high-quality content that targets specific topics and use connected Schema Markup to help search engines understand how your content relates to user intent, search queries, and other information on the internet.\nBy embracing semantic SEO, you align your strategy with search engines\u2019 evolving understanding. This leads to better visibility on the SERP and the delivery of highly-tailored content to your target audience.\nIf you\u2019re looking to implement connected Schema Markup at scale for your site, get in touch with our team to learn about our solution.","description":"Semantic SEO is the process of giving meaning and context to your web content to help search engines better understand your content."},{"@context":"http:\/\/schema.org","@type":"Thing","name":"Knowledge Graph","sameAs":["https:\/\/en.wikipedia.org\/wiki\/Knowledge_graph","http:\/\/www.wikidata.org\/entity\/Q33002955","kg:\/g\/11jtynfm6d"],"description":"information repository structured as a graph","@id":"https:\/\/www.schemaapp.com\/entity#Thing13"},{"@context":"http:\/\/schema.org","@type":"Thing","sameAs":["http:\/\/www.wikidata.org\/entity\/Q180711","kg:\/m\/019qb_","https:\/\/en.wikipedia.org\/wiki\/Search_engine_optimization"],"name":"SEO","alternateName":"search engine optimization","description":"practice and strategies of increasing online visibility in search engine results pages","@id":"https:\/\/www.schemaapp.com\/entity#Thing19"},{"@context":"http:\/\/schema.org","@type":"Thing","name":"Structured Data","sameAs":["kg:\/m\/05p2j70","http:\/\/www.wikidata.org\/entity\/Q26813700","https:\/\/en.wikipedia.org\/wiki\/Structured_data"],"description":"information with a formal data model","alternateName":"Schema Markup","@id":"https:\/\/www.schemaapp.com\/entity#Thing6"},{"@context":"http:\/\/schema.org","@type":"Organization","address":{"@type":"PostalAddress","streetAddress":"201 - 412 Laird Road","postalCode":"N1G 3X7","addressRegion":"Ontario","addressLocality":"Guelph","addressCountry":"https:\/\/www.schemaapp.com\/#Country","name":"Schema App Address","@id":"https:\/\/www.schemaapp.com\/#PostalAddress"},"logo":{"@type":"ImageObject","width":"290","height":"93","url":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2020\/07\/SA_Logo_Main_Orange_w300-1.png?strip=all&lossy=1&ssl=1","@id":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2020\/07\/SA_Logo_Main_Orange_w300-1.png?strip=all&lossy=1&ssl=1"},"potentialAction":{"@type":"ScheduleAction","name":"Schedule a Demo","url":"https:\/\/www.schemaapp.com\/book-a-demo\/","@id":"https:\/\/www.schemaapp.com\/#ScheduleAction"},"image":{"@type":"ImageObject","width":"1350","height":"650","url":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2021\/04\/Schema-App-Featured-Image.png?strip=all&lossy=1&ssl=1","@id":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2021\/04\/Schema-App-Featured-Image.png?strip=all&lossy=1&ssl=1"},"description":"Schema App is an end-to-end Schema Markup solution that helps enterprise SEO teams develop a knowledge graph and drive search performance.","knowsAbout":["http:\/\/www.wikidata.org\/entity\/Q1891170","https:\/\/www.wikidata.org\/wiki\/Q6108942","https:\/\/www.wikidata.org\/wiki\/Q26813700","https:\/\/www.wikidata.org\/wiki\/Q180711","http:\/\/www.wikidata.org\/entity\/Q33002955"],"keywords":["Structured Data","Knowledge Graph","Rich Results","Semantic Search","Search Engine Optimization","Schema Markup","Semantic Technology"],"location":"http:\/\/www.wikidata.org\/entity\/Q504114","sameAs":["https:\/\/www.instagram.com\/lifeatschemaapp\/","https:\/\/www.linkedin.com\/company\/2480720\/","https:\/\/twitter.com\/schemaapptool","https:\/\/www.youtube.com\/channel\/UCqVBXnwZ3YNf2BVP1jXcp6Q"],"legalName":"Hunch Manifest Inc","name":"Schema App","telephone":"+18554448624","url":"https:\/\/www.schemaapp.com\/","email":"support@schemaapp.com","knowsLanguage":"http:\/\/www.wikidata.org\/entity\/Q1860","areaServed":"http:\/\/www.wikidata.org\/entity\/Q13780930","@id":"https:\/\/www.schemaapp.com\/#Organization"},{"@context":"http:\/\/schema.org","@type":"Thing","name":"AI","sameAs":["http:\/\/www.wikidata.org\/entity\/Q11660","kg:\/m\/0mkz","https:\/\/en.wikipedia.org\/wiki\/Artificial_intelligence"],"description":"field of computer science that develops and studies intelligent machines","@id":"https:\/\/www.schemaapp.com\/entity#Thing5"},{"@context":"http:\/\/schema.org","@type":"Thing","sameAs":["https:\/\/www.wikidata.org\/wiki\/Q117246174","https:\/\/en.wikipedia.org\/wiki\/Generative_AI","kg:\/g\/11vyrwhhl2"],"name":"Generative AI","description":"Generative artificial intelligence is artificial intelligence capable of generating text, images, videos, or other data using generative models, often in response to prompts.","@id":"https:\/\/www.schemaapp.com\/entity#Thing3"}],{"@context":"https:\/\/schema.org\/","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Schema Markup","item":"https:\/\/www.schemaapp.com\/schema-markup\/#breadcrumbitem"},{"@type":"ListItem","position":2,"name":"Semantic SEO: What You Need to Know","item":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/#breadcrumbitem"}]}]