Knowledge graphs are increasingly becoming important in the AI world as an enabling technology for data integration and analytics, semantic search and question answering, and other cognitive applications. However, developing and maintaining large knowledge graphs in a manual way is too expensive and time consuming. To accelerate things, methods and techniques from the areas of information extraction and natural language processing (NLP) can be very helpful.
In this talk we’ll see the main NLP tasks that knowledge graph mining involves, the factors that affect how easy or difficult the execution of these tasks can be, and some common pitfalls that we need to avoid in order to mine high quality knowledge graphs.
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Panos Alexopoulos, Head of Ontology at Textkernel, and the author of Semantic Modeling for Data – Avoiding Pitfalls and Breaking Dilemmas (O’Reilly).
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