The world is complex and interconnected, and nowhere is that clear than publishing and media. Most media tells a story, and given that stories often tie together existing elements, knowledge graphs are a natural storage arena for them. Significantly the first large-scale knowledge graphs were in the media space.
Keeping the Story Straight
As streaming has become more popular, single, disconnected episodic stories are being increasingly replaced by epic series that span media and can prove highly interconnected. A production studio’s bible has traditionally been how such information is kept. Still, in a linear form, such a bible can get out of date quickly because it is difficult to search over a story’s evolution.
Knowledge graphs are a natural next step in keeping this information straight, as it becomes possible to see (and maintain) the links between characters, stories, events, locations, and related resources. While this holds for many organizations, it’s especially critical for entertainment and investigative journalism.
Tracking Production Over Time
Large-scale media production typically involves a complicated trail of contracts and product generation, which is important in planning and controlling costs. It can reduce the danger of expensive revisions because the wrong media file went to production due to a mis-key or transcription error. Knowledge graphs can also be used to ensure that intermediate products can be reused more efficiently, cutting costs and keeping within schedule.
Investigative and Predictive Journalism
Events do not occur in a vacuum. Good investigative journalism (and industry analysis) requires understanding what is happening in the world before the story breaks. To do this, most news organizations are becoming more predictive and analytical. Knowledge graphs can help to gather and synthesize this information, can make it available to multiple analysts simultaneously who can develop ongoing leads, and can also help to rate story leads based on provenance and reliability so that a story doesn’t turn into an embarrassment.
Seeding Story Lines and Gaining Inspiration
As streaming gains steam (and as the metaverse looms where interactive worlds become ever more reliant upon original and engaging stories), knowledge graphs can move beyond recommendation engines and into generative engines – mining historical data, feeding generative AIs, managing existing resources that can be used as fodder for innovative content. It also makes it possible to bring in your audience as contributors while taking advantage of identity management (another strength of knowledge graphs) to keep IP contributions clean.
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