Archives week 4 of 2023

What is a knowledge graph?

A knowledge graph is a collection of interconnected data that is used to represent real-world entities and the relationships between them. The data is typically represented as a set of nodes (representing entities) and edges (representing relationships) in a graph structure hence the name "knowledge graph".

Knowledge graphs can be used to build semantic data models, and can also be utilized to enhance your website’s semantic model quality. One way to do it is by integrating it with external knowledge sources such as Wikipedia, Wikidata, and product ontology, This allows for the data to be linked with high authority resources and to be aligned with a common understanding of the concepts and relationships, which can improve the consistency and accuracy of your content.

In this article, we will explore the benefits of using ontologies and knowledge graphs, and how to implement them on a website in different industries ,while we are going to discuss the followings:

• What is ontology?
• How a knowledge graph works?
• What are popular knowledge graphs?
• Knowledge Graph Use-Cases: Is there examples of domain-specific industries?
  • Automotive Industry
  • Real Estates Industry
  • e-Commerce Industry
• Conclusion

SEO Guide to Building a Semantic Model Quality

In natural language processing, semantic model quality is used to evaluate and understand the performance of language models, machine translation systems, and other text-generating systems.

It is important because it helps to ensure that the model is able to convey text that aligns with the intended meaning.

Semantic quality is used in a wide range of applications such as text summarization and simplification, fact-checking, information retrieval, translation, chatbots, and question answering.

High-quality semantic models are coherent, consistent, and relevant to the current context. While poor-quality models are confusing, inconsistent, or irrelevant.

By evaluating the quality of a semantic model, SEO professionals can identify and address any issues that may be affecting the model's performance on search engines. This can help to improve the overall accuracy and effectiveness of the content for users and search engines.

So how you can measure this quality? The followings are the main quality dimensions that should be considered when evaluating a semantic data model:

• Accuracy
• Completeness
• Consistency
• Conciseness
• Timeliness
• Relevancy
• Understandability
• Trustworthiness
• How to increase semantic quality?