Generating a Question Answering System from Text Causal Relations

• E.C. Garrido Merchán ^[1] ; C. Puente ^[2] ; J.A. Olivas ^[3]
  1. [1] Universidad Autónoma de Madrid

     Universidad Autónoma de Madrid

     Madrid, España

     
  2. [2] Universidad Pontificia Comillas

     Universidad Pontificia Comillas

     Madrid, España

     
  3. [3] Universidad de Castilla-La Mancha

     Universidad de Castilla-La Mancha

     Ciudad Real, España

     

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• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García , Lidia Sánchez González , Manuel Castejón Limas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2019, ISBN 978-3-030-29858-6, págs. 14-25
• Idioma: inglés
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  • The aim of this paper is to present a methodology for creating expert systems by processing texts in order to respond to the queries of a question answering system. In previous work, we have shown several algorithms that were able to extract causal information from text documents and to summarize it. These approaches extracted knowledge from unstructured information, but the performed representation could not be processed automatically to infer new knowledge. Generated summaries only present the information in natural language, and hence cannot be processed in order to generate complex implications. In this paper, we introduce a procedure capable of using this knowledge in order to infer new causal relations between concepts automatically by creating expert systems from the processed texts. These expert systems will contain the causal relations presented in the processed texts. In this representation, by using logic programming, we can infer new concepts that are implied by causal relations. We describe the methodology, technical details of the implementation of our question answering system and a full example where its usefulness is described.