Enterprise Use Cases Combining Knowledge Graphs and Natural Language
  Processing
Phillip Schneider Tim Schopf Juraj Vladika Florian Matthes
Knowledge management is a critical challenge for enterprises in today's
digital world, as the volume and complexity of data being generated and
collected continue to grow incessantly. Knowledge graphs (KG) emerged as a
promising solution to this problem by providing a flexible, scalable, and
semantically rich way to organize and make sense of data. This paper builds
upon a recent survey of the research literature on combining KGs and Natural
Language Processing (NLP). Based on selected application scenarios from
enterprise context, we discuss synergies that result from such a combination.
We cover various approaches from the three core areas of KG construction,
reasoning as well as KG-based NLP tasks. In addition to explaining innovative
enterprise use cases, we assess their maturity in terms of practical
applicability and conclude with an outlook on emergent application areas for
the future.