[[{"@type":["Article"],"@id":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/#Article","@context":{"@vocab":"http:\/\/schema.org\/","kg":"http:\/\/g.co\/kg"},"url":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/","publisher":[{"@id":"https:\/\/www.schemaapp.com\/#Organization"}],"audience":"https:\/\/schema.org\/PeopleAudience","inLanguage":"en-CA","image":[{"@type":"ImageObject","@id":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/#Highlight20251010191959150-393688_0_Article_image_ImageObject","url":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2025\/01\/Nodes-and-Edge-600x300.png?strip=all&lossy=1&ssl=1"},{"@type":"ImageObject","@id":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/#Highlight20251010191959150-393688_1_Article_image_ImageObject","url":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2025\/01\/Mark-van-Berkel-Triple-Example-600x300.png?strip=all&lossy=1&ssl=1"},{"@type":"ImageObject","@id":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/#Highlight20251010191959150-393688_2_Article_image_ImageObject","url":"https:\/\/ezk8caoodod.exactdn.com\/wp-content\/uploads\/2025\/12\/Dawson.png?strip=all&lossy=1&resize=100%2C100&ssl=1"}],"dateModified":"2026-07-09T14:57:45+00:00","datePublished":"2025-01-20T20:58:33+00:00","mainEntityOfPage":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/","timeRequired":"PT10M","potentialAction":[{"@type":"ScheduleAction","@id":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/#Article_potentialAction_ScheduleAction","url":"https:\/\/www.schemaapp.com\/book-a-demo\/"}],"headline":"Relational Databases vs. Graph Databases","name":"Relational Databases vs. Graph Databases","description":"Learn the defining characteristics of graph vs. relational databases, their strengths and limitations, and use cases for your organization.","keywords":["Explainers","Graph Database","Knowledge Graph","Relational Database"],"articleBody":"Data management has evolved dramatically over the past few years. Relational databases have long been the cornerstone of how organizations store and organize information, offering a structured and reliable system for handling data.\nHowever, as the digital landscape demands greater interconnectivity and data becomes increasingly complex, more dynamic approaches to data modeling have emerged.\nGraph databases, especially those utilizing Knowledge Graphs, excel in capturing not just data but the intricate relationships and contexts that define it. They shine in scenarios where meaning, connectivity, and semantic richness are prioritized.\nSo, what sets graph databases apart from relational databases? And how do you know which one is right for your needs?\nThis article explores the defining characteristics of each system, examines their strengths and limitations, and provides real-world examples to illustrate how they can work independently or even in tandem.\nFirst, let\u2019s clarify what both relational databases and graph databases are.\nWhat Is a Relational Database?\nA relational database is a type of database designed to store and organize data in structured tables consisting of rows and columns. These tables are a key feature of relational databases, with each table representing a specific aspect of the data being modeled.\nFor example, one table might represent individuals (e.g., customers or employees), while another might represent related data such as addresses or orders.\nHow Data is Structured Within a Relational Database\nEach row in a table represents a single record or entry, while columns define the attributes or details of that entry. For example, a table for blog content might include columns for the post ID, title, author, publication date, and category. A row in this table could look like this:\n\n\n\nPost ID\nTitle\nAuthor\nPublication Date\nCategory\n\n\n\n\n1\n\u201cWhat is SQL?\u201d\nJohn Doe\n2025-01-01\nDatabase\n\n\n\nIn this setup, the Post ID serves as a unique identifier for each blog post, ensuring that no two posts share the same ID. Let\u2019s dive deeper into how these unique identifiers are created using keys.\nHow Keys Within Relational Databases Model Relationships\nRelational databases use keys to establish and maintain relationships between tables. The two key types are:\n1. Primary Keys: These are unique identifiers for each row in a table. They ensure that every record can be uniquely identified, which is critical for maintaining data integrity. For example, in a \u201cPerson\u201d table, the primary key might be a unique \u201cPerson ID.\u201d\nNote: While including a primary key is not mandatory, it is considered a best practice to include one in every table. For instance, in a \u201cPerson\u201d table, a primary key might be included, while an \u201cAddress\u201d table might rely solely on a foreign key to connect to the \u201cPerson\u201d table.\n2. Foreign Keys: These are columns (or sets of columns) in one table that reference the primary key in another table. Foreign keys establish relationships between tables, such as one-to-one, one-to-many, or many-to-many. For example, a \u201cPerson\u201d table might be linked to an \u201cAddress\u201d table through a foreign key:\nPerson Table:\n\n\n\nPerson ID\nName\nDate of Birth\n\n\n\n\n1\nJohn Doe\n1990-01-14\n\n\n\n\nAddress Table:\n\n\n\nAddress ID\nPerson ID\nCity\nStreet Address\nPostal Code\n\n\n\n\n101\n1\nNew York\n123 Sesame St.\n10001\n\n\n\n\nIn this example, the \u201cPerson ID\u201d in the \u201cAddress\u201d table acts as a foreign key, linking it to the \u201cPerson\u201d table.\nData Integrity and Querying Relational Databases\nRelational databases operate using schemas.\nSchemas refer to predefined structures that ensure consistency and organization within the database. It is important to note that this schema is different from Schema Markup.\nA relational database\u2019s schema-based approach helps maintain data integrity, making it easier to manage the relationships between tables.\nMoreover, relational databases enable users to perform advanced queries through SQL (Structured Query Language). SQL is the primary tool for retrieving, updating, and managing data efficiently within a relational database.\nUse Cases of Relational Databases\nRelational databases excel in applications that require structured data and transactional consistency. Common examples include:\n\nAccounting Software: Managing financial transactions and ledgers.\nInventory Management: Tracking products, stock levels, and orders.\nCustomer Relationship Management (CRM): Organizing customer data, interactions, and histories.\n\nBenefits of Relational Databases\nThere are many organizational benefits to utilizing a relational database:\n\nMature Ecosystem and Tooling: Relational databases are supported by a vast range of tools and best practices.\nEfficiency for Structured, Tabular Data: Their design optimizes performance for simple queries and well-defined data relationships.\n\nLimitations of Relational Databases\nHowever, relational databases are not without their challenges:\n\nFixed Schema: The rigid structure can make it difficult to adapt to changing data requirements or add new relationships without significant schema updates.\nLimited Handling of Unstructured Data: They are less effective for managing unstructured or highly interconnected and complex data, such as social networks or multimedia files.\n\nBy leveraging their structured design and robust querying capabilities, relational databases remain a cornerstone of data management in many industries while being complemented by other database types for more flexible or complex use cases.\nWhat is a Graph Database?\nA graph database is a type of database designed to model and store data as nodes, edges, and their properties. This structure excels at representing relationships between data points, making it particularly well-suited for complex, interconnected datasets like Knowledge Graphs.\nKnowledge Graphs: The Core of Graph Databases\nOne of the most powerful applications of graph databases is in building Knowledge Graphs. A Knowledge Graph organizes information as entities (nodes), their attributes, and the relationships (edges) between them. This allows for a more dynamic and context-aware representation of data than traditional databases.\n\nNodes: Represent entities such as \u201cPerson,\u201d \u201cCompany,\u201d or \u201cProduct.\u201d\nEdges: Define relationships like \u201cworks at,\u201d \u201cpurchased by,\u201d or \u201cis a subsidiary of.\u201d\nProperties (Attributes): Include specific details about nodes or edges, such as a person\u2019s name, a company\u2019s revenue, or the date of a transaction.\n\n\nFor example, in a Knowledge Graph for a Schema App directory:\n\nA node might represent an employee (e.g., \u201cMark van Berkel\u201d).\nAnother node could represent a company (e.g., \u201cSchema App\u201d).\nAn edge would connect these nodes with a relationship like \u201cworks at.\u201d\n\nRDF Graph Databases: Building Knowledge Graphs\nKnowledge Graphs are often implemented using Resource Description Framework (RDF) Graph Databases, also known as Triple Stores. These databases represent data as RDF triples, a foundational element of Knowledge Graphs:\n\nSubject: The entity (e.g., \u201cMark van Berkel\u201d).\nPredicate: The relationship (e.g., \u201cworksAt\u201d).\nObject: The related entity (e.g., \u201cSchema App\u201d).\n\n\nThese triples are highly flexible and can grow dynamically as new relationships or data types emerge.\nSemantic Understanding and Flexibility of Graph Databases\nKnowledge Graphs apply semantic meaning to their entities and relationships, enabling systems to infer context and draw deeper insights. This dynamic nature makes them more flexible than relational databases, which rely on rigid schemas.\nAdding to this flexibility, relationships between data points can be queried directly, revealing connections that may not have been apparent before.\nStandards and Query Languages of Graph Databases\nTechnologies like RDF (Resource Description Framework) and SPARQL (SPARQL Protocol and RDF Query Language) power many Knowledge Graphs. These standards enable:\n\nSemantic Querying: Allowing users to ask complex questions like \u201cWhich employees work at companies in New York specializing in AI?\u201d\nInteroperability: Seamless integration across different systems and datasets.\n\nAnother important note about the use of RDF within Knowledge Graphs, is that RDF properties allow data models to evolve organically, as relationships can be added without altering the existing structure. Unlike relational databases, where schema changes often require significant restructuring, RDF properties enable dynamic schema evolution.\nUse Cases of Graph Databases\nGraph databases containing Knowledge Graphs are great in applications that require semantic reasoning, such as:\n\nRecommendation Systems: Suggesting products, content, or services based on user behavior and relationships.\nPersonalized Search Engines: Delivering results tailored to the user\u2019s context and preferences.\nAI-Driven Insights: Powering natural language processing and reasoning in AI applications. Helps ground Large Language Models (LLMs) in factual information.\n\nIn industries such as healthcare, eCommerce, and social media, Knowledge Graphs help:\n\nDiscover hidden connections in vast datasets.\nSupport strategic content marketing initiatives like content inventory management.\nGround internal AI systems, such as organizational chatbots or large language models (LLMs).\n\nLearn how to drive your content marketing strategy using Content Knowledge Graphs by downloading our eBook!Download the eBook\nLimitations of Graph Databases\nWhile powerful, graph databases are not without challenges:\n\nComplex Setup: Building and maintaining a graph database requires specialized expertise, especially for large-scale implementations.\nTooling Maturity: Compared to relational databases, graph database tools and integrations are still evolving.\nUse Case Specificity: For simpler or highly structured data needs, a relational database might be more practical.\n\nWhy Choose a Knowledge Graph?\nKnowledge Graphs offer an expressive and flexible way to model data, unlocking deeper insights and fostering innovation in complex domains. Their ability to dynamically represent relationships and infer meaning makes them an essential tool in modern data management and AI-driven solutions.\nKey Differences Between Relational Databases and Graph Database\n\n\n\nDifference\nRelational Database\nGraph Database\n\n\n\n\nData Structure\nRigid tables, rows, columns\nDynamic nodes and edges\n\n\nFlexibility\nFixed schemas, hard to modify\nDynamic and flexible, schema-less or schema-light\n\n\nRelationship Modeling\nThe irony of relational databases is that they don\u2019t do the best job of modeling relationships\nUse of primary and foreign keys to connect table rows together\nUse of defined ontological properties to connect nodes together.\nUses triples to connect two entities together through a predicate (ex. knowsAbout)\n\n\nScalability with Complexity\nStruggle to scale highly interconnected data. Manages more simplistic connections between data points\nHandle complex relationships within large datasets seamlessly\n\n\n\n\nUltimately, relational databases focus on structured, predefined relationships, while Graph databases prioritize flexibility and semantic meaning.\nWhen to Use a Relational Database vs. Graph Database\nChoosing between relational databases and graph databases depends on the nature of your data and the problem you aim to solve.\nWhen to Use Relational Databases\n\nYour data fits neatly into rows and columns.\nRelationships are straightforward and not deeply interconnected.\nYou need high performance for predefined queries (e.g., generating invoices or tracking orders).\n\nWhen to Use Graph Databases\n\nYour data is complex, with evolving or non-linear relationships.\nContext and meaning are critical, such as in recommendation engines or optimizing for semantic search.\nYou\u2019re building AI applications requiring enriched, interconnected datasets like internal chatbots.\n\nComplementary Use Cases\nIn some scenarios, both systems can work together to maximize efficiency. A relational database might store transactional data, while graph databases containing a Knowledge Graph enriches that same data with contextual insights.\nFor instance, an eCommerce platform could use a relational database to track inventory and sales but leverage a Knowledge Graph to power a recommendation engine that connects customer preferences with product features.\nExploring this example further:\n\nA movie streaming site may store movie ratings in relational database, but shows content recommendations in a Knowledge Graph.\nSocial media may store information about people in a relational database, but uses Knowledge Graphs for friend recommendations.\nWikipedia stores articles in a relational document store, but connects the articles together using the Wikidata Knowledge Graph.\nData that needs to be accessed quick should use a relational database, but can be complemented with a Knowledge Graph to drive more rich analytics and insights.\n\nEmpowering Data Strategies for the Semantic Era\nRelational databases and graph databases each excel in distinct areas. Relational databases are unmatched for structured, transactional data management, while graph databases are ideal for uncovering meaning and relationships in complex, interconnected datasets.\nBy understanding the unique strengths of these systems, organizations can make informed decisions that align with their specific needs. Leveraging both approaches in tandem unlocks new possibilities, empowering businesses to enhance efficiency, derive actionable insights, and innovate in ways that were previously unattainable.\nAs the digital landscape evolves, Knowledge Graphs and their semantic capabilities offer a path forward. They enable smarter AI, improved search visibility, and a competitive edge in an increasingly interconnected world.\nSchema App is here to help you harness the power of Knowledge Graphs by developing a robust, reusable Content Knowledge Graph that future-proofs your data strategies.\nReady to elevate your data management and AI capabilities? Get in touch with us to get started!\nDawson MacPhee Software Developer II Dawson MacPhee is a Software Developer II at Schema App, where he builds software that powers advanced knowledge graph solutions. With a strong foundation in semantic technologies, Dawson helps shape systems that improve visibility in search and AI and support more accurate, connected data. At Schema App, he contributes to the technical design and development of tools for Entity SEO.\n","author":[{"name":"Dawson MacPhee","@id":"https:\/\/www.schemaapp.com\/author\/dawsonschemaapp-om\/#ProfilePage_mainEntity_Person","@type":"Person","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"https:\/\/www.linkedin.com\/in\/dawson-macphee-5a1134252\/","identifier":{"@type":"PropertyValue","name":"Source","value":"Internal"}}],"about":[{"name":"Entity Manager","@id":"https:\/\/www.schemaapp.com\/glossary\/#Highlight-20240109181252822_24_DefinedTermSet_hasDefinedTerm_DefinedTerm","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"identifier":{"@type":"PropertyValue","name":"Source","value":"Internal"}},{"@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_graphdatabase_6c633897a9e44bdc11d1bc1cde40b7d4f97dfba39845afbfb929d309b03e0f96","@type":"Thing","name":"graph database","sameAs":["https:\/\/en.wikipedia.org\/wiki\/Graph_database","http:\/\/g.co\/kg\/m\/080jnxw","http:\/\/www.wikidata.org\/entity\/Q595971"],"@context":{"@vocab":"http:\/\/schema.org\/","kg":"http:\/\/g.co\/kg"}}],"mentions":[{"name":"Entity Manager","@id":"https:\/\/www.schemaapp.com\/glossary\/#Highlight-20240109181252822_24_DefinedTermSet_hasDefinedTerm_DefinedTerm","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"identifier":{"@type":"PropertyValue","name":"Source","value":"Internal"}},{"name":"Structured Data","@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-schema-markup-a-guide-to-structured-data\/#Thing","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"alternateName":"Schema Markup","sameAs":["https:\/\/en.wikipedia.org\/wiki\/Structured_data","kg:\/m\/05p2j70","http:\/\/www.wikidata.org\/entity\/Q26813700","http:\/\/www.wikidata.org\/entity\/Q26813700","kg:\/m\/05p2j70","https:\/\/en.wikipedia.org\/wiki\/Structured_data"],"identifier":{"@type":"PropertyValue","name":"Source","value":"Internal"}},{"name":"Mark van Berkel","@id":"https:\/\/www.schemaapp.com\/author\/vberkel\/#ProfilePage_mainEntity_Person","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":["https:\/\/www.linkedin.com\/in\/markvanberkel\/","https:\/\/g.co\/kgs\/z4aK6i","https:\/\/x.com\/vberkel","https:\/\/www.linkedin.com\/in\/markvanberkel\/","https:\/\/g.co\/kgs\/z4aK6i","https:\/\/x.com\/vberkel"],"identifier":{"@type":"PropertyValue","name":"Source","value":"Internal"}},{"name":"Content Knowledge Graph","@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-a-content-knowledge-graph\/#Thing","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"identifier":{"@type":"PropertyValue","name":"Source","value":"Internal"}},{"name":"unique key","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_uniquekey_3de1301deb59d7f5384abdcde4ce13ddbe5fc8cd968dbf63db8887a09eeb7c9e","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q3667566"},{"name":"database","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_database_d4536b4537dd6ae09228a746e5e96495da1c34e85d59f3fe559af351e4477ae5","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":["http:\/\/g.co\/kg\/m\/02bc6","http:\/\/www.wikidata.org\/entity\/Q8513","https:\/\/en.wikipedia.org\/wiki\/Database"]},{"name":"SQL","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_sql_1651bc6ee55dece4249e8e450da18ca501b8d32053fc191b941681d5f82f9578","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":["http:\/\/g.co\/kg\/m\/075st","http:\/\/www.wikidata.org\/entity\/Q47607","https:\/\/en.wikipedia.org\/wiki\/SQL"]},{"name":"relational database","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_relationaldatabase_ec1d23aae2f6d5a94ee7b56cddbc712c3bbbbccf9aaed46ea4f9acd988dc4651","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":["http:\/\/g.co\/kg\/m\/0119v3m2","http:\/\/g.co\/kg\/m\/0tv2h61","http:\/\/www.wikidata.org\/entity\/Q192588","https:\/\/en.wikipedia.org\/wiki\/Relational_database"]},{"name":"person","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_person_8af043b8f71f7f156ce9100ae313b4f493f7e6ee9c9940af9a02d140c95d45e8","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q690940"},{"name":"John Doe","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_johndoe_a3f559576e89e997d2fe47bb37021ff44047791000aedf153191d89c8f0dd3c4","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q302057"},{"name":"person","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_person_1ee4a6fc8535c2f1cb151b21153660879afc8b45e82a2ddce15efaa12382dfef","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q215627"},{"name":"data","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_data_6240737ae948f66b9dcd7d4adeee5f1438daa8a6fd6230e0fdb8d8f50aaf0602","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q42848"},{"name":"key","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_key_0c331e676fff7612951de2506af67e839ac42b68b5d9d8d3395ae34b235ba05d","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q471771"},{"name":"identifier","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_identifier_32c56e145eba27e0d5477c15b9090c1e58294ba799fb9446c1c968c3188cda05","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q853614"},{"name":"interpersonal relationship","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_interpersonalrelationship_657f5fa5af349f24406e9ff0374dde436bec08c2cba05c1b5e861944d8ccf7ba","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":["http:\/\/g.co\/kg\/g\/11bc6c02zz","http:\/\/g.co\/kg\/m\/015fwp","http:\/\/www.wikidata.org\/entity\/Q223642","https:\/\/en.wikipedia.org\/wiki\/Interpersonal_relationship"]},{"name":"author","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_author_86fc9e1961000a3d40e69c719772c73e5475965770c83aa960ae9140d35b07ad","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":["http:\/\/g.co\/kg\/m\/0kyk","http:\/\/www.wikidata.org\/entity\/Q482980","https:\/\/en.wikipedia.org\/wiki\/Author"]},{"name":"category","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_category_c003dec530a45a74df25cce4b819124246ed454724e078e9c1c5daf099453885","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q719395"},{"name":"publication","@id":"https:\/\/entity.schemaapp.com\/Ugozi_Edge_Inc\/Thing_publication_586dfdbc5a00b8066395782d5ae0d350d28906ee0a5c1f0882bf99376e155856","@type":"Thing","@context":{"@vocab":"http:\/\/schema.org\/"},"sameAs":"http:\/\/www.wikidata.org\/entity\/Q732577"}]},{"@context":"http:\/\/schema.org","@type":"Organization","areaServed":["https:\/\/www.wikidata.org\/wiki\/Q16","https:\/\/www.wikidata.org\/wiki\/Q46","https:\/\/www.wikidata.org\/wiki\/Q49","https:\/\/www.wikidata.org\/wiki\/Q664","https:\/\/www.wikidata.org\/wiki\/Q30","https:\/\/www.wikidata.org\/wiki\/Q408","http:\/\/www.wikidata.org\/entity\/Q13780930"],"keywords":["Structured Data","Knowledge Graph","Rich Results","Semantic Search","Search Engine Optimization","Schema Markup","Semantic Technology"],"slogan":"Our purpose is to build meaningful connections, with each other, our customers and the data so that we are all understood.","foundingDate":"2016","location":"http:\/\/www.wikidata.org\/entity\/Q504114","description":"Schema App helps you build and govern your semantic data layer using schema markup so AI and search engines understand your brand accurately, improving visibility, trust, and performance.","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\/","knowsLanguage":"https:\/\/www.schemaapp.com\/about\/#Language","email":"support@schemaapp.com","contactPoint":[{"@type":"ContactPoint","email":"Support@schemaapp.com","name":"Customer Support","@id":"https:\/\/www.schemaapp.com\/contact\/#ContactPoint"},{"@type":"ContactPoint","areaServed":["https:\/\/www.schemaapp.com\/contact\/#Place","Worldwide"],"telephone":"+18554448624","email":"Hello@schemaapp.com","name":"General Enquiries","@id":"https:\/\/www.schemaapp.com\/contact\/#ContactPoint1"}],"founder":[{"@type":"Person","sameAs":["https:\/\/www.google.com\/search?kgmid=\/g\/11gb4pbrk8","https:\/\/x.com\/marthavanberkel","https:\/\/www.linkedin.com\/in\/martha-van-berkel\/"],"name":"Martha van Berkel","@id":"https:\/\/www.schemaapp.com\/author\/martha\/#ProfilePage_mainEntity_Person"},{"@type":"Person","sameAs":["https:\/\/x.com\/vberkel","https:\/\/g.co\/kgs\/z4aK6i","https:\/\/www.linkedin.com\/in\/markvanberkel\/"],"name":"Mark van Berkel","url":"https:\/\/www.schemaapp.com\/author\/vberkel\/","@id":"https:\/\/www.schemaapp.com\/author\/vberkel\/#ProfilePage_mainEntity_Person"}],"knowsAbout":[{"@type":"Thing","sameAs":["kg:\/g\/11fjx_wmtf","https:\/\/www.wikidata.org\/wiki\/Q33002955","https:\/\/en.wikipedia.org\/wiki\/Knowledge_graph"],"name":"Knowledge Graph","@id":"https:\/\/www.schemaapp.com\/#Thing"},{"@type":"DefinedTerm","sameAs":["https:\/\/en.wikipedia.org\/wiki\/JSON-LD","kg:\/m\/0hzq_55","https:\/\/www.wikidata.org\/wiki\/Q6108942"],"name":"JSON-LD","@id":"https:\/\/www.schemaapp.com\/glossary\/#javascript-object-notation-for-linked-data-jsonld"},{"@type":"DefinedTerm","sameAs":["http:\/\/www.wikidata.org\/entity\/Q26813700","kg:\/m\/05p2j70","https:\/\/en.wikipedia.org\/wiki\/Structured_data"],"description":"information with a formal data model","name":"Structured Data","alternateName":"Schema Markup","@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-schema-markup-a-guide-to-structured-data\/#Thing"},{"@type":"Intangible","name":"Semantic Search Engine Optimization","alternateName":"Semantic SEO","@id":"https:\/\/www.schemaapp.com\/schema-markup\/what-is-semantic-seo\/#Intangible"}],"address":{"@type":"PostalAddress","streetAddress":"301 - 100 Stone Road","postalCode":"N1G 5L3","addressRegion":"Ontario","addressLocality":"Guelph","addressCountry":"CA","name":"Schema App Address","@id":"https:\/\/www.schemaapp.com\/#PostalAddress"},"logo":{"@type":"ImageObject","width":"290","height":"93","name":"Schema App Logo","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"},"identifier":{"@type":"PropertyValue","propertyID":"http:\/\/schemaapp.com\/ontology\/schemamodel#EntityHome","value":"True","name":"Entity Home","@id":"https:\/\/www.schemaapp.com\/#PropertyValue"},"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"},"@id":"https:\/\/www.schemaapp.com\/#Organization"}],{"@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":"Relational Databases vs. Graph Databases","item":"https:\/\/www.schemaapp.com\/schema-markup\/relational-databases-vs-graph-databases\/#breadcrumbitem"}]}]