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GraphDB in Action: Navigating Knowledge About Living Spaces, Cyber-physical Environments and SkiesĀ 

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This has enabled them to meet the requirements coming from heterogeneous data in building automation systems, the interoperability issues critical for design engineering and, last but not least, the challenges in air-traffic control. The framework addresses current data integration needs and prepares for future capability.

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Are You Content with Your Organizationā€™s Content Strategy?

Rocket-Powered Data Science

Techniques that both enable (contribute to) and benefit from smart content are content discovery, machine learning, knowledge graphs, semantic linked data, semantic data integration, knowledge discovery, and knowledge management.

Strategy 267
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Why Establishing Data Context is the Key to Creating Competitive Advantage

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Worse, and according to Gartner, upward of 80% of enterprise data today is unstructured which further exacerbates the loss of knowledge, insights, and the wisdom needed to make effective business choices. As a result, organizations are looking for fresh data integration approaches to challenge the mindset with which we created them.

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KGF 2023: Bikes To The Moon, Datastrophies, Abstract Art And A Knowledge Graph Forum To Embrace Them All

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So, KGF 2023 proved to be a breath of fresh air for anyone interested in topics like data mesh and data fabric , knowledge graphs, text analysis , large language model (LLM) integrations, retrieval augmented generation (RAG), chatbots, semantic data integration , and ontology building.

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Bridging the Gap Between Industries: The Power of Knowledge Graphs ā€“ Part I

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Knowledge graphs are changing the game A knowledge graph is a data model that uses semantics to represent real-world entities and the relationships between them. It can apply automated reasoning to extract further knowledge and make new connections between different pieces of data.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

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Graphs boost knowledge discovery and efficient data-driven analytics to understand a companyā€™s relationship with customers and personalize marketing, products, and services. Graph-based solutions further leverage the relationships among the entities involved to create a semantically enhanced machine learning model.

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From Data Silos to Data Fabric with Knowledge Graphs

Ontotext

Added to this is the increasing demands being made on our data from event-driven and real-time requirements, the rise of business-led use and understanding of data, and the move toward automation of data integration, data and service-level management. This provides a solid foundation for efficient data integration.