Knowledge graphs and blockchain (a distributed ledger system) are highly referential. For instance, knowledge graphs need distributed ledgers to secure keys, and distributed ledgers need knowledge graphs to provide the necessary context and provenance for those keys. For this to happen, knowledge graphs need to evolve to become at least immutable—a critical requirement for data provenance.
Fittingly, knowledge graph vendors are beginning to address the challenge of evolving knowledge graphs. Most vendors have indicated that they are in the process of incorporating distributed ledger technology into their knowledge graph store or ensuring that their products support the technology.
Given this momentum, it is likely that integration of the two technologies will become routine by 2021. This will happen in parallel with the attempts to integrate machine-learning algorithms and an industry-wide effort to incorporate semantic and property graphs, through a W3C action such as a new version of SPARQL and standardization of Graph Query Language (GQL) and GraphQL.
Ultimately, knowledge graphs will become the integration point for a number of artificial intelligence technologies. Primarily because most of the artificial intelligence technologies touch on the nature of global data in distributed environments, distributed ledgers, immutable computing, and the Internet of Things. This will become increasingly the case, as a federation — the ability to address, query and update data across multiple data stores of different types — becomes an intrinsic capability of such systems.
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