Leveraging Knowledge Graphs of Movies and Their Content for Web-Scale Analysis

Abstract

The amount of video content available on the Web is constantly growing, especially due to the increasing popularity of Video on Demand (VoD) platforms such as Netflix, Hulu and Youtube. This has made it harder for viewers to discover the right visual content for them. Recommender systems are being offered by VoD services in order to automatically suggest potentially interesting videos to users. However, recommendations are typically based on: (i) limited video metadata fields, such as genre, title and actors; (ii) the content that other users liked; (iii) private or isolated data repositories. In this paper we describe our approach leveraging the richness of semantic technologies to enrich movie metadata, and create meaningful semantically-enriched descriptions of movie scenes using video and audio processing techniques. This approach allows for the creation of a structured and interoperable Knowledge Graph (KG) describing movies and their content. This KG can be easily interlinked with existing Linked Data datasets available on the Web, resulting in a more comprehensive representation of the movies. Our approach enables seamless integration with existing data sources and fine-grained data analysis of movie content at Web scale.