Reasoning with Uncertainty and Inconsistency in Structured Scientific Knowledge

Lead Research Organisation: Queen's University of Belfast
Department Name: Computer Science


There is a huge and rapidly expanding amount of information available for scientists in various online resources. However, this wealth of information has created challenges for scientists who wish to locate and analyse knowledge from heterogeneous sources. Key problems that exist are that there is much uncertainty in individual sources of scientific knowledge, and many conflicts arising between different sources of scientific knowledge. Scientists therefore need tools that are tolerant of uncertainty and inconsistency in order to query and merge scientific knowledge.This project aims to facilitate the analysis of scientific knowledge by the development of technology for structured scientific knowledge (SSK). SSK is represented by a set of SSK reports each of which is a structured report that describes one or more scientific datasources (such as one or more journal articles, empirical datasets, etc). The format is an XML document with textentries restricted to individual words, values or simple phrases in scientific terminology. SSK is intended to help scientists understand the contents of a datasource. Each one contains summaritive information about the datasource (e.g. information from an abstract, summary of techniques used, etc) plus evaluative information about the datasource (eg. delineation of uncertainties and errors in the information source, qualifications of the key findings, etc). The summaritive information describes the information provided by the authors of the datasource, and the evaluative information describes the information provided by the users or authors of the datasource. SSK can be constructed by hand, by information extraction technology, and as a result of analysing datasources. In this project, we want to extend our existing work for merging and analysing heterogeneous structured information by harnessing formal theories for representing and reasoning with uncertain and inconsistent information. The result of the project will be a general theoretical framework for handling uncertainty and inconsistency in SSK, and a demonstration of the framework in a prototype implementation for querying and merging potentially conflicting SSK from heterogeneous sources.


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Hunter A (2010) A survey of formalisms for representing and reasoning with scientific knowledge in The Knowledge Engineering Review

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Hunter A (2008) A Context-Dependent Algorithm for Merging Uncertain Information in Possibility Theory in IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

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Ma J (2011) Modeling and reasoning with qualitative comparative clinical knowledge in International Journal of Intelligent Systems

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Ma J (2015) A belief revision framework for revising epistemic states with partial epistemic states in International Journal of Approximate Reasoning

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Ma J (2011) A framework for managing uncertain inputs: An axiomization of rewarding in International Journal of Approximate Reasoning

Description Two particular application focuses of the project were for handling results from clinical trials and on rapid screening for substrate prediction in bioscience. Often, when considering results from a number of trials, there is uncertain and conflicting information.

To address these issues, we developed techniques for performing meta-analysis with missing data, for querying conflicting trials results using ontological information to describe the patient and intervention classes, and for constructing arguments for determining relative superiority of particular interventions based on the available evidence. Parallel to this, a rapid screening method was developed to identify useful substrates based on previous experimental data.

The results of these studies have been published in computer science and biomedical informatics forums. We have also written a state of the art review of technology for representing and reasoning with scientific knowledge that is published in Knowledge Engineering Review in 2010.
Exploitation Route A Proof of Concept project would be a viable way.

A toolset on detecting inconsistency has been developed and released to the research community and has been used by international researchers.

Some of the information fusion algorithms have been used in subsequent research projects.
Sectors Digital/Communication/Information Technologies (including Software),Healthcare,Pharmaceuticals and Medical Biotechnology

Description Some of the data fusion techniques published in a scientific paper have been cited by medical researchers.
First Year Of Impact 2010
Sector Healthcare,Pharmaceuticals and Medical Biotechnology
Description Autonomous Systems Underpinning Research (ASUR) (project title: Personalising Autonomous Systems )
Amount £40,000 (GBP)
Organisation Defence Science & Technology Laboratory (DSTL) 
Sector Public
Country United Kingdom
Start 04/2014 
End 11/2014
Description EPSRC Autonomous and Intelligent Systems Program
Amount £623,000 (GBP)
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2012 
End 03/2016
Title A tool for detecting inconsistency in knowledge bases 
Description A tool has been developed to detect and identify inconsistencies in arbitrary knowledge bases, putting theory into practice, 
Type Of Material Improvements to research infrastructure 
Year Produced 2013 
Provided To Others? Yes  
Impact IBM (Northern Ireland) is looking into the possibility to commercialize it. 
Title An agent based event correlation framework 
Description A multi-agent based event reasoning framework was developed to correlate events detected from multiple heterogeneous sources under uncertainty. The tool has been applied to cyber-physical security scenarios for generating demonstrations. 
Type Of Material Improvements to research infrastructure 
Provided To Others? No  
Impact The framework has been successfully applied to event detection and correlation in security. 
Description Fraud detection with Allstate 
Organisation Allstate
Country United States 
Sector Private 
PI Contribution Contribute an Artificial Intelligence based approach to anomaly detection and correlation for discovering fraud.
Collaborator Contribution Provide real-world examples, data and expertise on fraud detection.
Impact N/A
Start Year 2014