Presented Sören Auer, Professor of Data Science and Digital Libraries at Leibniz Universität Hannover and Director of the TIB.
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Mastering the digitalization challenge requires enterprises to radically innovate. Data, information, and knowledge become increasingly important assets for realizing innovative digital business models and services. In order to create an enterprise data innovation architecture, which facilitates the rapid and agile creation of novel and unforeseen use cases we need to establish a common understanding as well as interoperability and accessibility of data and knowledge. To establish such a common understanding of the structure and meaning of data among individuals, departments, value chains or industries we must leverage vocabularies, Linked Data, knowledge graphs or Semantic Data Lakes to facilitate innovate data-driven use cases and enterprise agility. As a result, data can be seamlessly exchanged in a secure way between trustworthy parties in the enterprise and value chain to increase agility, efficiency, effectiveness and ultimately realize the potential of digitization.
This talk reflects on this development and give an overview on recent approaches in the areas of Big Data software architectures, data spaces and knowledge graphs, which all help to realize the emerging concept of hybrid AI, where large-scale, rich semantic data and knowledge tightly interacts with machine learning and analytics. You will see examples of semantic information models used in practice for governing the information exchange as well as analytical applications. Examples of these include ScorVoc, which provides a semantic model of the APICS Supply Chain Council's SCOR vocabulary or FIBO, the Financial Business Ontology. The presentation discusses practical approaches for implementing Knowledge Graphs in the Enterprise such as the Industrial Data Space, Linked Enterprise Data or the Administrative Shell for Industry 4.0. It discusses some successful examples along with lessons learned in the Supply Chain Management, Industry 4.0 and Cultural Heritage domains.
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Offered on Columbia University’s Morningside campus in New York City, the Knowledge Graph Conference (KGC) is a world-class curated program that brings experienced practitioners, technology leaders, cutting-edge researchers, academics and vendors together for two days of presentations, discussions and networking on the topic of knowledge graphs.
While the underlying technologies to store, retrieve, publish and model knowledge graphs have been around for a while, it is only in recent years that widespread adoption has started to take hold.
As knowledge is an essential component of intelligence, knowledge graphs are an essential component of AI. They form an organized and curated set of facts that provide support for models to understand the world. Today, they power tasks like natural language understanding, search and recommendation, and logical reasoning. Tomorrow they will ubiquitously be used to store and retrieve facts learned by intelligent agents.
In the enterprise, knowledge graphs are the ultimate dataset. Integrating and organizing together internal and external data sources. Knowledge graphs integrate with the larger information system: master data management, data governance, data quality. Their flexibility and powerful representation capabilities allow data scientists to tap them to build powerful models.
The Knowledge Graph Conference is coordinated by Columbia University School of Professional Studies' Executive Education program. Visit: [ Ссылка ] for more information.
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