Merging open knowledge extracted from text with MERGILO

This paper presents MERGILO, a method for reconciling knowledge extracted from multiple natural language sources, and for delivering it as a knowledge graph. The underlying problem is relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, e.g. automatic news summarization, human–robot dialoguing, etc. After providing a formal definition of the problem, we propose our 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. MERGILO is evaluated on its ability to identify corresponding entities and events across documents against a manually annotated corpus of news, showing promising results.