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.
Merging Open Knowledge Extracted from Text with MERGILO
M Mongiovi;D Reforgiato;A Gangemi;V Presutti;
2016
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
