Large-scale knowledge repositories are becoming increasingly important as a foundation for enabling a wide variety of complex applications. In turn, building high-quality knowledge repositories critically depends on the technologies of knowledge curation and knowledge fusion, which share many similar goals with data integration, while facing even more challenges in extracting knowledge from both structured and unstructured data, across a large variety of domains, and in multiple languages. Our

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... utorial highlights the similarities and differences between knowledge management and data integration, and has two goals. First, we plan to introduce the Database community to the techniques proposed for the problems of entity linkage and relation extraction by the Knowledge management, Natural language processing, and Machine learning communities. Second, we plan to give a detailed survey of the work done by these communities in knowledge fusion, which is critical to discover and clean errors present in sources and the many mistakes made in the process of knowledge extraction from sources. Our tutorial is example driven and hopes to build bridges between the database community and other disciplines to advance research in this important area.