Detection, Representation and Management of Concept Drift in Linked Open Data: Report of the Drift-a-LOD2016 Workshop

摘要

The Web of Data has expanded to - and is being deployed in - a wide range of domains. In many of these domains, facts change continuously: political landscapes evolve, medical discoveries lead to new cures, and artists form new collaborations. In terms of knowledge representation, we observe that instances change their roles, new relations appear, old ones become invalid, and classes change both their definition and member-instances. These changes pose new challenges to creators and users of Linked Open Data (LOD) to avoid that semantic interoperability and access to digital content are compromised. For instance, LOD publishers need to detect changes in the real world and capture them in their datasets; users and applications need automated tools to adapt querying over such diachronic datasets.