
When semantically connected data matters most
Technological advances useful in solving some problems can often trigger side effects and other problems in the process. Consider the power of natural language processing, generative AI and related forms of AI in accelerating the knowledge graph development process, for example.
If you’re an equity analyst, experiencing that kind of power can trigger thoughts like, “Hey. I can process all the annual reports from this OEM (=original equipment manufacturer, such as a computer or car maker) and also from their partner and supply networks, mine those relationships and predict the impact of their second and third-order effects. Connecting that graph with a news graph could help refine, geolocate and time those predictions.’
