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

REFORGIATO RECUPERO, DIEGO ANGELO GAETANO;
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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/182203
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