Artificial Intelligence
GATE: an architecture for development of robust HLT applications
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Analyzing the combination of conflicting belief functions
Information Fusion
Failure mode and effects analysis using a group-based evidential reasoning approach
Computers and Operations Research
Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Analyzing the degree of conflict among belief functions
Artificial Intelligence
The third PASCAL recognizing textual entailment challenge
RTE '07 Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
A survey of paraphrasing and textual entailment methods
Journal of Artificial Intelligence Research
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
Measuring conflict between possibilistic uncertain information through belief function theory
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
A Geometric Approach to the Theory of Evidence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Decision making is an important element throughout the life-cycle of large-scale projects. Decisions are critical as they have a direct impact upon the success/outcome of a project and are affected by many factors including the certainty and precision of information. In this paper we present an evidential reasoning framework which applies Dempster-Shafer Theory and its variant Dezert-Smarandache Theory to aid decision makers in making decisions where the knowledge available may be imprecise, conflicting and uncertain. This conceptual framework is novel as natural language based information extraction techniques are utilized in the extraction and estimation of beliefs from diverse textual information sources, rather than assuming these estimations as already given. Furthermore we describe an algorithm to define a set of maximal consistent subsets before fusion occurs in the reasoning framework. This is important as inconsistencies between subsets may produce results which are incorrect/adverse in the decision making process. The proposed framework can be applied to problems involving material selection and a Use Case based in the Engineering domain is presented to illustrate the approach.