Semantic reconciliation of knowledge extracted from text

This paper proposes a novel method for reconciling knowledge extracted from multiple natural language sources, and delivering it as a knowledge graph. The problem is relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, e.g. automatic news summarisation, human-robot dialoguing, etc. Solving this problem requires solving sub-tasks that have only been studied individually, so far. After providing a formal definition of the problem, we propose a holistic approach to handle natural language input { typically independent texts as in news from different sources { and we output a knowledge graph representing their reconciled knowledge. The method is evaluated on its ability to identify corresponding entities and events across documents against a manually annotated corpus of news, showing promising results.