Recommended Replay: Knowledge Graphs: 5 Use Cases and 10 Steps to Get There
Watch our on-demand webinar now!
Only available until November 30, 2020
Join the founders and CEOs of two of the leading companies in the field of graph technologies and the implementation of knowledge graphs in this on-demand webinar.
Andreas Blumauer of Semantic Web Company and Atanas Kiryakov of Ontotext will explain why semantic knowledge graphs play a central role in improving data analytics, data governance, and content management. They will also demonstrate how knowledge graphs support business decision making, customer satisfaction, and knowledge discovery.
During the on-demand webinar, you will learn ten simple steps to create, use, and augment knowledge graphs to help organizations in different industries and throughout different processes accomplish the following:
- Cost savings through better orchestration of knowledge workflows and more efficient reuse of assets, e.g., in HR-related processes.
- Generation of 360° views of customers and products by unifying unstructured and structured data and connecting data silos through harmonized metadata, e.g., in the financial industry.
- Improved customer experience through precise text analysis and auto-tagging using knowledge graphs, e.g., to improve technical documentation or customer support.
- Enhanced engagement and better re-purposing and monetization of your published content through rich semantic metadata and personalized recommendations.
- Quicker and more efficient decision making and research through better search and analysis options, e.g., in drug development, market intelligence or supply chain management.
Along the knowledge graph life cycle, you will understand how to enable humans and computers to complement each other in knowledge management, information extraction, and data analytics.
The on-demand webinar will highlight technologies and methodologies that automate the routine work of data scientists, librarians, editors, and other knowledge workers while enabling them —based on explainable AI— to control these processes and contribute with explicit knowledge, sample data, or feedback.