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Title: Finding and measuring inconsistency in arbitrary knowledge bases
Author: McAreavey, Kevin
ISNI:       0000 0004 5372 052X
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2014
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Inconsistency is prevalent in real-world knowledge base applications. In this thesis, we consider how inconsistency measures can support formal inconsistency handling techniques. We begin with a review of existing formula-level inconsistency measures proposed in the literature. We then carry out a case study on inconsistency in the QRadar security information and event management (SIEM) platform. From this work, we argue that formula-level inconsistency measures based on the notion of minimal inconsistent subsets (MISes) are an intuitive means of supporting inconsistency handling. However, few of these measures have been implemented or experimentally evaluated to support their viability for arbitrary knowledge bases, since computing all MISes is intractable in the worst case. Fortunately, recent work on a related problem, known as minimal unsatisfiable subformulae (MUSes), offers a viable solution in many cases. As such, we draw connections between MISes and MUSes and propose two new algorithms for computing MISes, termed MUS generalization and optimized MUS transformation. We implement these algorithms in a tool called MIMUS, along with a selection of existing measures for flat and stratified knowledge bases. We also propose and implement a novel measure for stratified knowledge bases which offers a more fine-grained inspection of the formulae involved in inconsistency. After this, we carry out an experimental evaluation of MIMUS using random arbitrary knowledge bases. Finally, we demonstrate how inconsistency measures can be exploited in other domains where we propose the use of inconsistency measures for evaluating belief merging operators. We conclude that computing MISes is viable for many large and complex random instances. We also conclude that these measures are relatively trivial to compute once MISes have been found. As such, these measures represent a viable and intuitive tool for inconsistency handling in real-world applications such as QRadar. Moreover, inconsistency measures represent an appropriate method for evaluating merging operators.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available