Media and publishers’ content drives their business, and yet online content can be difficult to optimize and repurpose, especially across brands.
But by using semantics and knowledge graphs, media companies can create a rich web of interconnected knowledge enables better search and personalized recommendations based on users' interests and preferences.
By starting with semantic building blocks of controlled vocabularies, media companies identify and organize the essential concepts, entities, and relationships relevant to the media content, such as topics, genres, themes, locations, or characters. This facilitates the integration and interoperability of the vocabulary with other systems and applications, and standardizes use of these terms across domains or geographies.
Enhanced Search Capabilities: Knowledge graphs can improve search functionality by understanding the relationships between different entities and concepts, which helps provide more accurate and relevant search results. For instance, a search for specific content can be expanded to include related content, regardless of format.
Personalization and Recommendations: By creating a graph of user behavior and preferences, media companies can enable targeted content delivery, personalized recommendations, and more effective advertising strategies. Knowledge graphs can also provide insights into user demographics, interests, and consumption patterns, allowing for better segmentation and targeting.
Cross-platform integration: Connecting content across different platforms and media types enables media companies to create seamless experiences for their audience, where information from articles, videos, podcasts, and other formats can be interconnected and easily accessible across platforms.