Authors:
Susana Martin-Toral
1
;
Gregorio I. Sainz-Palmero
2
and
Yannis Dimitriadis
3
Affiliations:
1
CARTIF Centro Tecnológico, Spain
;
2
CARTIF Centro Tecnológico, School of Industrial Engineering University of Valladolid, Spain
;
3
School of Telecommunications Engineering, University of Valladolid, Spain
Keyword(s):
Incoherence, Document corpus, N-tuple, Information retrieval, Neuro-fuzzy system, Expert knowledge, Decision making system.
Related
Ontology
Subjects/Areas/Topics:
Advanced Applications of Fuzzy Logic
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of Artificial Intelligence
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Knowledge-Based Systems Applications
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Software Engineering
;
Strategic Decision Support Systems
;
Symbolic Systems
;
Theory and Methods
Abstract:
The way in which document collections are generated, modified or updated generates problems and mistakes in the information coherency, leading to legal, economic and social problems. To tackle this situation, this paper proposes the development of an intelligent virtual domain expert, based on summarization, matching and neuro-fuzzy systems, able to detect incoherences about concepts, values, or references, in technical documentation. In this scope, an incoherence is seen as the lack of consistency between related documents. Each
document is summarized in the form of 4-tuples terms, describing relevant ideas or concepts that must be free of incoherences. These representations are then matched using several well-known algorithms. The final decision about the real existence of an incoherence, and its relevancy, is obtained by training a neuro-fuzzy system with expert knowledge, based on the previous knowledge of the activity area and domain experts. The final system offers a semi-auto
matic solution for incoherence detection and decision support.
(More)