Enterprise knowledge graphs have become ubiquitous in contemporary IT. In particular, their impact on artificial intelligence (AI) applications has to be acknowledged notes Jelani Harper, an editorial consultant servicing the information technology market. The reason knowledge graphs have become almost indispensable is partly because of its multifaceted utility for AI and data management.
Knowledge graphs can link enterprise data irrespective of the type, structure or format. The data linked through knowledge graphs are semantically tagged with unique machine-readable identifiers. These semantically tagged data with unique machine-readable identifiers are ideal for intelligent systems, machine learning analytics and interoperability, and an array of other benefits influential for AI applications.
In fact, AI and enterprise knowledge graphs have a symbiotic relationship. For instance, the data from knowledge graphs, with machine-readable, unique identifiers, can facilitate data integration and intelligent system interoperability. In turn, machine-learning capabilities can increase and improve the knowledge contained within these graphs. Similarly, knowledge graphs can be instrumental in improving AI technologies as it has the potential to become a foundational platform for AI applications in the future. Knowledge graphs can help data scientists engaged in advanced predictive analytics reduce the time taken to prepare data for the analysis.
Determining the probability of equipment failure for airplane parts in the industrial internet is a good example of the mutually beneficial relationship between knowledge graphs and AI technologies. Enterprises can use the data in the knowledge graphs to run machine-learning algorithms to determine the probability of airplane parts failure. The data on the probability along with the data on parts failure can then be integrated into the knowledge graph. This enriched data can be used again as inputs for running the machine-learning algorithms and obtaining enhanced forecasts. Depending on whether it is maintenance or a repair activity, enterprises can run machine learning on the results and use the outcome as inputs for the knowledge graph. In turn, the enriched data can be used as inputs for forecasting the probability of equipment failure. This data loop, can only help AI technologies further evolve.
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