Operationalizing the Logical Uncertainty Principle in a Language Modelling Framework for Context-based Information Retrieval

在基于上下文的信息检索的语言建模框架中实施逻辑不确定性原理

• 批准号: EP/E002145/1
• 负责人: Dawei Song
• 金额: $ 20.82万
• 依托单位: The Open University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE002145%2F1
• 关键词: Operationalizing; Logical; Uncertainty; Principle; Language

The pressing need to deal with information overload has brought about the recent revolutionary emergence of context-based information retrieval (IR). IR research is experiencing a shift to context-centric approaches enabling one to infer from his/her retrieval context. I strongly believe that context-driven reasoning is the key in building more user and context-sensitive IR systems. In applications related to IR (for example, retrieving relevant documents to a given query, expanding the original query with related terms, determining the correct answer in Question-Answering, or determining an appropriate translation in cross-language retrieval), some forms of reasoning are often embedded as a result of information transformation and context dependency. As an illustration, given a query Java , an IR system may return documents about programming and documents about Merapi (a volcano in central Java island), as they all contain the term java . If the retrieval context is computer , documents about programming are relevant. However, for a volcanologist, documents about Merapi are more likely to be relevant. The above uncertainty arises from the flow of information from Java and computer to programming , or from Java and volcano to Merapi , depending on the retrieval context. It is essential to understand that current IR technology, including current search engines such as Google, does not cater for scenarios like that just given.This research is motivated by the following fundamental question: can we make the reasoning process explicit in an IR system which can in turn gain a capability of reasoning to select truly relevant information items depending on his/her retrieval context? The Logical Uncertainty Principle views IR as a plausible logical inference process and thus provides a potentially significant theoretical foundation for context-based IR. Nevertheless, its operationalization has long been a problem, due to the difficulty with obtaining the contextual and domain knowledge as well as implementing the symbolic logical models on a large scale. Recent advances in language technologies open the door to realizing the Logical Uncertainty Principle in a practical setting. Recently, language modelling frameworks have been developed for IR to integrate different types of term relationships via a smoothing mechanism. The language modelling approach provides a solid theoretical setting, produces promising experimental results (comparable to the best IR systems), and is also computationally efficient. Based on my existing work in this direction, I will investigate the operationalization of the Logical Uncertainty Principle in a language modelling framework to facilitate effective context-dependent reasoning. I will conduct theoretical research, prototyping system development, and experimental evaluation with large-scale datasets. It is my belief that the combination of the strengths of logical inference and language modelling as a new generation IR infrastructure can lead to more intelligent and context-sensitive but at the same time computationally tractable IR systems.

处理信息过载的迫切需要导致了最近革命性的基于上下文的信息检索(IR)的出现。信息检索研究正在向以上下文为中心的方法转变，使人们能够从他/她的检索上下文中推断。我坚信，上下文驱动推理是构建更多用户和上下文敏感的信息检索系统的关键。在与信息检索相关的应用程序中(例如，检索与给定查询相关的文档、使用相关术语扩展原始查询、在问答中确定正确答案、或在跨语言检索中确定适当的翻译)，某些形式的推理通常作为信息转换和上下文依赖的结果而嵌入。例如，给定查询Java，IR系统可以返回关于编程的文档和关于Merapi(爪哇岛中部的一座火山)的文档，因为它们都包含术语Java。如果检索上下文是计算机，则与编程有关的文档是相关的。然而，对于火山学家来说，关于默拉皮的文件更有可能是相关的。上述不确定性源于从Java和计算机到编程的信息流，或者从Java和Volcano到Merapi的信息流，具体取决于检索上下文。需要理解的是，当前的信息检索技术，包括目前的搜索引擎，如Google，不能满足这样的场景。本研究的动机是以下基本问题：我们能否在信息检索系统中显式地进行推理过程，从而获得根据他/她的检索上下文选择真正相关的信息项的推理能力？逻辑不确定性原则认为信息检索是一种似是而非的逻辑推理过程，从而为基于语境的信息检索提供了潜在的重要理论基础。然而，由于难以获得上下文和领域知识以及大规模实施符号逻辑模型，其可操作性一直是一个问题。语言技术的最新进展为在实际环境中实现逻辑不确定原则打开了大门。最近，已经开发了用于信息检索的语言建模框架，以通过平滑机制整合不同类型的术语关系。语言建模方法提供了坚实的理论基础，产生了有希望的实验结果(可与最好的IR系统相媲美)，而且计算效率也很高。基于我在这方面的现有工作，我将研究逻辑不确定性原则在语言建模框架中的可操作性，以促进有效的上下文相关推理。我将对大规模数据集进行理论研究、原型系统开发和实验评估。我相信，将逻辑推理和语言建模的优点结合起来，作为新一代信息检索基础设施，可以产生更智能和上下文敏感的信息检索系统，同时还可以在计算上易于处理。

项目成果

Knowledge discovery for adaptive negotiation agents in e-marketplaces

• DOI: 10.1016/j.dss.2007.12.018
• 发表时间: 2008-05
• 期刊: Decis. Support Syst.
• 作者: Raymond Y. K. Lau;Yuefeng Li;D. Song;R. Kwok

Towards a belief-revision-based adaptive and context-sensitive information retrieval system

• DOI: 10.1145/1344411.1344414
• 发表时间: 2008-03
• 期刊: ACM Trans. Inf. Syst.
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On Tsallis Entropy Bias and Generalized Maximum Entropy Models

关于 Tsallis 熵偏差和广义最大熵模型

• DOI: 10.48550/arxiv.1004.1061
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Database Systems for Advanced Applications - 14th International Conference, DASFAA 2009, Brisbane, Australia, April 21-23, 2009. Proceedings

高级应用数据库系统 - 第 14 届国际会议，DASFAA 2009，澳大利亚布里斯班，2009 年 4 月 21-23 日。会议记录

• DOI: 10.1007/978-3-642-00887-0_60
• 发表时间: 2009
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Toward a Fuzzy Domain Ontology Extraction Method for Adaptive e-Learning

• DOI: 10.1109/tkde.2008.137
• 发表时间: 2009-06
• 期刊: IEEE Transactions on Knowledge and Data Engineering
• 影响因子: 8.9
• 作者: Raymond Y. K. Lau;D. Song;Yuefeng Li;C. Cheung;Jin-Xing Hao