Reasoning with Uncertainty and Inconsistency in Structured Scientific Knowledge

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.