Power your AI marketing initiatives with your Knowledge Graph
Implement semantic Schema Markup to build a reusable marketing knowledge graph to inform search engines or train your LLMs.
Help search engines understand and contextualize your content
Search engines like Google have shifted from lexical to semantic search to improve the accuracy of search results. Search engines now look at the concepts and entities on a page and how they relate to other things on the website and on the web, to provide searchers with the most relevant results to their query.
You can help search engines further understand, contextualize and distinguish the entities on your site using Schema Markup.
1.
Schema.org is the vocabulary developed by major search engines. Implement Schema Markup on your site to describe the entities in your content using the standard Schema.org vocabulary.
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Connect entities on your site to explain the relationship between one another to provide search engines with contextual knowledge about your content.
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Link the entities on your site to external authoritative knowledge bases if applicable in your markup to help search engines further disambiguate the entities on your page.
Enable your marketing team with a knowledge graph that drives AI initiatives and improves search
By using the Schema.org vocabulary to define and connect the entities on your site, you are building a reusable knowledge graph for your organization.
Drive more qualified traffic to your site
By developing your knowledge graph, we help your team provide generative AI search engines with more contextual knowledge about your content. As a result, search engines can better match the concepts on your page to the user’s query, increase the click-through rate and drive more quality traffic to your site.
Train your LLM with domain-specific and timely knowledge
AI Chatbots are a great way to connect with your customers but the LLMs are prone to hallucination. By creating a knowledge graph using your website content, we provide your team with a reliable data layer that can be used to train and ground your LLM with domain-specific knowledge to generate more accurate results at a lower cost.
Enhance your reporting with a rich reusable data layer
Your Schema.org knowledge graph is an extensible and rich data set that can be reused for more granular reporting. With Schema App, you can easily integrate your knowledge graph with popular BI tools like Looker studio to combine data from various source and make informed decisions regarding strategy and marketing spend.
Develop your very own knowledge graph using Schema App
At Schema App, we help businesses implement semantic Schema Markup on their site to develop a reusable knowledge graph using our semantic technology platform.
Build your knowledge graph with ease
Whether you use our authoring toolkits (Highlighter, Editor, etc) or author your markup in-house, Schema App consolidates all the Schema Markup into your very own RDF Graph. Your graph will contain a collection of data items specifically for your website, and all of the schema Markup properties are combined into one easily accessible dataset.
Automate your entity linking
We also enrich your Schema Markup using the Schema App Linked Entity Recognition feature – where we apply natural language processing to your content to identify the relevant entities on your website and map them into your Schema Markup and graph. This provides search engines with richer context and meaning for how your content relates to known concepts (people, places, things, etc).
Ready to drive AI search performance using your knowledge graph?
Additional Resources
Frequently Asked Questions
Schema Markup, also known as structured data, is a language used by search engines to understand the content and entities on your site so that it can better match your content to the searcher’s query.
An entity is a thing with specific attributes (like length, duration, colour, etc.).
For example: Schema App is an entity that can be described by its logo, services, website url, address, employees that work for the organization, and more.
Learn more about Entities and their role in search here.
A knowledge graph is a collection of relationships between things defined using a standardized vocabulary, from which new knowledge can be gained through inferencing.
Semantics is a term for the study of language, how it expresses meaning, and the rules it must abide by in order to convey this meaning.
Semantic technology are the tools and technologies we use to articulate the meaning and relationship between things.
Learn more about Schema App’s semantic technologies here.