Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-29859-3_2 | HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2019 |
Keywords | Field | DocType |
Causality, Question answering system, Causal texts, Causal detection, Causal summary | Causality,Question answering,Causal relations,Computer science,Expert system,Natural language,Natural language processing,Artificial intelligence,Logic programming,Machine learning | Conference |
Volume | ISSN | Citations |
11734 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eduardo C. Garrido-Merchán | 1 | 1 | 0.37 |
| Cristina Puente | 2 | 19 | 5.60 |
| José A. Olivas | 3 | 106 | 20.85 |