What is Ontology?

An ontology can be defined as a model which represents knowledge as a set of concepts within a domain. An ontology also captures the relationships between these concepts.

Ontologies are used to standardize and organize knowledge, making it easier to share and reuse information.

Ontology is used to create a formal representation of the entities and their relationships in a knowledge graph, Note that ontology is a general data model, meaning that you don’t have to include information about specific entities in your ontology. Instead, its a reusable framework you could use to describe additional entities in the future.

Using ontology as a framework, you can then add in specific real data about your individual entities to create a knowledge graph.

Ontologies are similar to knowledge graphs in that they use nodes and edges and are based on the Resource Description Framework (RDF) triples. This similarity can also be seen in their visual representation.

Ontology can be used to describe the content of a website or blog by providing a clear and defined structure for the information that is provided. It can be used to help a website rank better in search engine results by providing a clear structure and meaning for the website's content.

schema.org is an example of a widely adopted ontology, that is supported by Google Bing Yahoo, Yandex, and a fantastic community that supports semantic representation for the longevity of the internet.

Ultimately, this representation of knowledge is supported by technological infrastructure such as databases, APIs, and machine learning algorithms, which exist to help people and services locate, access and process information more efficiently.

How a knowledge graph works?

A knowledge graph is composed of data from various sources that often have different structures. To organize this data, elements such as schemas, identities, and context are used to provide structure and meaning.

The schemas create the structure for the graph, identities classify the nodes, and the context sets the stage for the knowledge. These elements enable products like Google's search engine algorithm to distinguish words with multiple meanings, such as "Orange" the brand, and "Orange" the fruit. Search engines like Google, Bing and Yahoo are using knowledge graph to provide more accurate search results by understanding the context and intent behind the query.

Machine-learning-powered knowledge graphs use natural language processing (NLP) to build a comprehensive representation of nodes, edges and labels through a technique called semantic enrichment.

Semantic enrichment is the process of adding a layer of topical metadata to content so that machines can make sense of it and build connections to it. This process allows machines such as search engines to understand and identify the meaning of objects and the connections between them.

After a knowledge graph is built, it is compared and combined with other datasets that are related and similar in nature. Once completed, the graph enables question answering and search engines to quickly retrieve and reuse detailed responses to queries.

These systems not only save time for consumers but can also be used in a business setting to automate data collection and integration, thereby supporting decision-making.

Creating knowledge graphs can also support the discovery of new knowledge by connecting previously unconnected data points.

What are popular knowledge graphs?

Popular knowledge graphs, which are used by consumers, have established high expectations for search systems within businesses. Examples of these knowledge graphs include:

• DBpedia: a graph of information extracted from Wikipedia.
• YAGO: a large knowledge base with general knowledge about people, cities, countries, movies, and organizations.
• Freebase): a graph of general knowledge (discontinued) but worth learning its history.
• Wikidata: a graph of structured data from Wikipedia and other sources.
• Google Knowledge Graph: a graph of information used by Google to enhance search results.
• Microsoft Satori: a proprietary graph of knowledge used by Bing and other Microsoft services.
• Amazon Neptune: a fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets.

Knowledge Graph Use-Cases: Is there examples of Domain-Specific Industries?

To automate the process of creating knowledge graphs and describing your entities and their relationships without knowing how to code, you can use a tool such as Schemantra which allows you to create the relevant entities and build ontologies to link them using schema.org.

However, knowledge graphs also have applications in every industry, such as:

Automotive Industry

A knowledge graph can be used in the automotive industry to describe and organize information about vehicles, parts, suppliers, offers and prices.

This information can be used to improve the customer experience by providing personalized vehicle recommendations, identifying potential issues with vehicles, and facilitating faster and more accurate search results for parts and service information.

I created a video about the essentials of structured data and an example on knowledge graph modeling for the automotive industry. And how to connect entities for a knowledge graph using schema markups

Real Estates Industry

A knowledge graph can be used in the real estate industry to describe and organize information about properties, locations, prices, and amentitiess.

This information can be used to improve the customer experience by providing personalized property recommendations, identifying potential issues with properties and facilitating faster and more accurate, and relevant search results for listings and real estate market information.

You can watch my video to learn how to build a knowledge graph for real estate, and for more information, you can read my real estate SEO case study.

e-Commerce Industry

A knowledge graph can be used in the e-commerce industry to store and organize and represent information about products, manufacturers, and prices.

This information can be used to improve the customer experience by providing personalized product recommendations, identifying products that are frequently purchased together, and facilitating faster and more accurate search results.

Additionally, a knowledge graph can be used to improve search engine efficiency by identifying patterns in customer behavior and product performance, which can help to find the target product.

I created a video about the very basic principles with an example of how to create a knowledge graph for a product.

Conclusion

In conclusion, knowledge graphs play a significant role in SEO by providing a more comprehensive understanding of a website's content to search engines.

By creating ontologies and using tools such as Schema.org to mark up web pages with structured data, websites can improve their visibility in search results and make it easier for search engines to understand the content on the website.

Knowledge graphs also help search engines to understand the relationships between different entities and concepts on a website, which allows them to provide more relevant and accurate search results to users. Overall, knowledge graphs are a powerful tool for SEO and can greatly enhance a website's search engine performance.