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Knowledge graphs are essential for large businesses, particularly in web search and e-commerce. This course, taught by Andreas Kiger, explores knowledge graphs' applications with large language models. Learners will understand how to organize and store data effectively to highlight relationships between entities, such as actors and movies. The course includes practical exercises on querying databases and using LangChain for specific applications like analyzing SEC filings. By the end, participants will be equipped to use knowledge graphs to enhance their generative AI applications and better understand industry contexts.
Knowledge graphs and their undervalued role in academic AI are discussed.
Knowledge graphs store relational data, like actors related to movies.
Knowledge graphs integral to web search engines for pulling relevant information.
Data querying techniques with Cipher will be applied in this course.
Knowledge graphs provide a critical foundation for enhancing AI-driven insights. By harnessing relational data, generative AI applications can achieve greater accuracy and relevance, especially in domains like finance, where understanding complex relationships is crucial. For instance, companies like NE 4J play pivotal roles in developing tools that facilitate the integration of such data structures into existing AI frameworks, thus expanding the capabilities of both companies and researchers alike.
The integration of knowledge graphs into AI solutions raises important ethical considerations, particularly regarding data privacy and the accuracy of derived insights. As organizations leverage these data structures for analysis, such as SEC filings, establishing transparent practices in how data is stored and used becomes imperative. Effective data governance frameworks must ensure that the insights produced do not perpetuate biases, especially in generative AI applications that can influence critical financial decisions.
The course focuses on utilizing knowledge graphs alongside language models to enhance data context in AI applications.
It will be employed in the course to query a movie database.
It will be used to analyze SEC data in the course.
NE 4J has developed tools that integrate AI for enhanced data management and retrieval in knowledge graphs.
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S. government agency overseeing securities markets and protecting investors. Its data serves as a practical application for knowledge graphs, illustrating how they contextualize financial information.
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