Reasoning with Uncertainty and Inconsistency in Structured Scientific Knowledge

结构化科学知识中不确定性和不一致的推理

• 批准号: EP/D074282/1
• 负责人: Anthony Hunter
• 金额: $ 40.48万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD074282%2F1
• 关键词: Reasoning; Uncertainty; Inconsistency; Structured; Scientific

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.

在各种在线资源中，科学家可以获得数量巨大且迅速扩大的信息。然而，这些丰富的信息给希望定位和分析来自不同来源的知识的科学家带来了挑战。存在的关键问题是，个别科学知识来源存在很大不确定性，不同科学知识来源之间出现许多冲突。因此，科学家需要能够容忍不确定性和不一致性的工具来查询和合并科学知识。本项目旨在通过开发结构化科学知识(SSK)技术来促进科学知识的分析。SSK由一组SSK报告表示，每个SSK报告都是描述一个或多个科学数据源(如一篇或多篇期刊文章、经验数据集等)的结构化报告。该格式是一种XML文档，其文本条目被限制为科学术语中的单个单词、值或简单短语。SSK旨在帮助科学家理解数据源的内容。每一条都包含关于数据源的概要信息(例如，摘要中的信息、所用技术的概要等)以及关于数据源的评估信息(例如，描述信息来源中的不确定性和错误、关键调查结果的资格等)。摘要信息描述由数据源的作者提供的信息，而评价信息描述由数据源的用户或作者提供的信息。SSK可以手工构建，可以通过信息提取技术构建，也可以作为分析数据源的结果。在这个项目中，我们希望通过利用形式化理论来表示和推理不确定和不一致的信息，来扩展我们现有的合并和分析异类结构化信息的工作。该项目的结果将是一个处理SSK中不确定性和不一致性的一般理论框架，并在一个原型实现中演示该框架，用于查询和合并来自不同来源的潜在冲突的SSK。

项目成果

Modeling and reasoning with qualitative comparative clinical knowledge

利用定性比较临床知识进行建模和推理

• DOI: 10.1002/int.20445
• 发表时间: 2011
• 期刊: International Journal of Intelligent Systems
• 影响因子: 7
• 作者: Ma J

Performing meta-analysis with incomplete statistical information in clinical trials.

在临床试验中使用不完整的统计信息进行荟萃分析。

• DOI: 10.1186/1471-2288-8-56
• 发表时间: 2008-08-18
• 期刊: BMC MEDICAL RESEARCH METHODOLOGY
• 影响因子: 4
• 作者: Ma, Jianbing;Liu, Weiru;Hunter, Anthony;Zhang, Weiya
• 通讯作者: Zhang, Weiya

Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction

• DOI: 10.1007/s10472-012-9286-x
• 发表时间: 2012-03
• 期刊: Annals of Mathematics and Artificial Intelligence
• 影响因子: 1.2
• 作者: Anbu Yue;Weiru Liu;A. Hunter