Enterprise Use Cases Combining Knowledge Graphs and Natural Language Processing

As modern organizations continuously adapt to the evolving requirements of the digital age, the importance of successfully managing enterprise data has never been greater. In this article, we define the term'enterprise' as a large-scale business, which operates on a national or international level and typically involves significant risks and resources. The competitive advantage of data-driven decisions affects all industry sectors and nearly every part of the value chain (Schopf et al. 2022b). For example, market trends are analyzed for business development, production is optimized through process metrics, and customer reviews are monitored for predictive maintenance. However, raw data alone is insufficient for decision-making. In order to become actionable information, data has to be endowed with meaning and purpose (Rowley 2007). This can be achieved by data enrichment through a relevant context. A compelling approach to achieve this is by modeling knowledge in the form of graph connections between data items (Martin et al. 2021). In view of the above, knowledge graphs (KGs) have emerged as a powerful representation for integrating knowledge from multiple information sources.