Thomas will introduce fundamental concepts of linking data to knowledge in order to empower – not only – subject matter experts to benefit from data and provide knowledge. By leveraging a W3C standards-based Semantic Web approach to the Linked Data life cycle we will understand how to bridge data silos, and manage and extract metadata in order to create entity centric and knowledge-based applications. We will look into search, analytics, and recommendation.
Semantic Web languages such as RDF, RDFS and OWL are suitable for representing static knowledge, which enables the integration of data and information from multiple systems. With knowledge graphs, these technologies are becoming widely deployed in enterprise settings. But many applications require more than just information integration. Scenarios around the Internet of Things, such as Smart Homes and Smart Factories, where the physical and virtual worlds are becoming connected via sensors and actuators, require languages that allow for the representation of behaviour.
The volume of published, scientific data is growing at an exponential rate. When carefully curated and paired with emerging technologies such as knowledge graphs, the data is a powerful catalyst to accelerate innovation across a wide range of disciplines. This talk explores the abiding value of intellectual curation to maximize the quality and utility of scientific data collections and highlights how CAS is leveraging knowledge graphs to enhance innovation across multiple use cases.