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ViewpointS: Towards a Collective Brain Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Framework for Merging Probabilistic Knowledge Bases

Authors : Van Tham Nguyen, Ngoc Thanh Nguyen, Trong Hieu Tran

Published in: Computational Collective Intelligence

Publisher: Springer International Publishing

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Abstract

Knowledge merging is of major concern in developing probabilistic expert systems. Each system provides a consistent probabilistic knowledge while the merged knowledge base is often inconsistent. Because of this reason, a wide range of approaches has been put forward to merge probabilistic knowledge bases. However, the input of the models is the set of possible probabilistic functions representing the original probabilistic knowledge bases. In this paper, we investigate a framework for merging probabilistic knowledge bases represented by the new form. To this aim, a process to merge probabilistic knowledge bases is introduced, several transformation methods for the representation of the original probabilistic knowledge base is presented, a set of merging operators is proposed, and several desirable logical properties are investigated and discussed.

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Metadata
Title
Framework for Merging Probabilistic Knowledge Bases
Authors
Van Tham Nguyen
Ngoc Thanh Nguyen
Trong Hieu Tran
Copyright Year
2018
Book
Computational Collective Intelligence

Print ISBN: 978-3-319-98442-1

Electronic ISBN: 978-3-319-98443-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-98443-8_4

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