Importance of Knowledge Graphs and Their Issues
Explore the significance of knowledge graphs in various applications such as semantic search, recommendation systems, and NLP. Understand how they enhance data integration and business intelligence, while also learning about challenges like data quality, scalability, and privacy. This lesson provides practical examples to help you grasp the core issues and advantages of using knowledge graphs in machine learning.
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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.
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