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Distributed Linked Data as a Framework for Human-Machine Collaboration

This paper presents a novel application of Linked Data as an indirect communication framework for human-machine collaboration. In a decentralised fashion, agents interact by publishing Linked Data resources without having access to a centralised knowledge base. This framework provides an initial set of solutions to the problems of dynamic Linked Data discovery, of querying frequently-updated distributed datasets and of guaranteeing consistency in the case of concurrent updates. As a motivation for this framework we take the use-case of human and machine agents collaborating to the execution of tasks. This use-case is based on existing real-world Linked Data representations of human instructions and research on their integration with machine functionalities.