Importance of Knowledge Graphs and Their Issues

Use cases

The main importance of knowledge graphs comes when handling several real-world use cases. Here are some of the few different use cases:

• Semantic search: Google Search and Google's Knowledge Graph are great examples of this use case. The graphs are used to enhance the search capabilities of the website.

• Recommendation system: We model the relationships between products, users, and other entities to provide personalized recommendations. Netflix's recommendation system models the relationship between users, movies, and TV shows.

• Question answering: A chatbot or a language model is a good example for this use case. OpenAI's GPT-3, Google Assistant, and Apple's Siri are some examples that use a knowledge graph to generate human-like text responses.

• Data integration: Since knowledge graphs are multi-relational, we can integrate and link data from multiple sources to make them more useful.

• Natural language processing (NLP): Knowledge graphs can be used in NLP use cases such as text summarization, sentiment analysis, and translation using tools like Google's BERT.

• Business intelligence: We can analyze data relating to customers and products to make data-driven business decisions.

• Medical research: We can model relationships between genes, proteins, drugs, and diseases to boost medical research.

It's easy to see that we can construct many different use cases modeled on knowledge graphs. The possibilities are vast.

A simple use case

Let's look at a practical example of a use case with knowledge graphs. The code is for a simple recommendation system constructed using knowledge graphs.