(6.07 MB) Abstract
Media producers publish large amounts of multimedia content online - both text, audio, image and video. As the online media market grows, the management and delivery of contents becomes a challenge. Semantic and linking technologies can be used to organize and exploit these contents through the use of knowledge graphs. This industrial doctorate dissertation addresses the problem of constructing knowledge resources and integrating them into a system used by media producers to manage and explore their contents. For that purpose, knowledge graphs and their maintenance through Information Extraction (IE) from news streams is studied. This thesis presents solutions for multimedia understanding and knowledge extraction from online news, and their exploitation in real product applications, and it is structured in three parts.
The first part consists on the construction of IE tools that will be used for knowledge graph population. For that, we built an holistic Entity Linking (EL) system capable of combining multimodal data inputs to extract a set of semantic entities that describe news content. The EL system is followed by a Relation Extraction (RE) model that predicts relations between pairs of entities with a novel method based on entity-type knowledge. The final system is capable of extracting triples describing the contents of a news article.
The second part focuses on the automatic construction of a news event knowledge graph. We present an online multilingual system for event detection and comprehension from media feeds, called VLX-Stories. The system retrieves information from news sites, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer four Ws from journalism: who, what, when and where. This part of the thesis deals with the problems of Topic Detection and Tracking (TDT), topic modeling and event representation.
The third part of the thesis builds on top of the models developed in the two previous parts to populate a knowledge graph from aggregated news.
The system is completed with an emerging entity detection module, which detects mentions of novel people appearing on the news and creates new knowledge graph entities from them. Finally, data validation and triple classification tools are added to increase the quality of the knowledge graph population.
This dissertation addresses many general knowledge graph and information extraction problems, like knowledge dynamicity, self-learning, and quality assessment. Moreover, as an industrial work, we provide solutions that were deployed in production and verify our methods with real customers.
