[论文解读] Possibilistic Pertinence Feedback and Semantic Networks for Goal's Extraction

Mohamed Nazih Omri|arXiv (Cornell University)|Jun 5, 2012

Multi-Criteria Decision Making参考文献 15被引用 6

一句话总结

本文提出了一种与语义网络集成的模糊相关性反馈机制，以改善从新手用户模糊查询中提取目标。通过采用可能性理论而非概率论，该方法在处理不确定性方面增强了鲁棒性，在通过用户反馈改进查询相关性方面，其有效性优于概率方法。

ABSTRACT

Pertinence Feedback is a technique that enables a user to interactively express his information requirement by modifying his original query formulation with further information. This information is provided by explicitly confirming the pertinent of some indicating objects and/or goals extracted by the system. Obviously the user cannot mark objects and/or goals as pertinent until some are extracted, so the first search has to be initiated by a query and the initial query specification has to be good enough to pick out some pertinent objects and/or goals from the Semantic Network. In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries are the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction Systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of possibility theory in classical Knowledge Extraction techniques for novice user's query processing is more robust than the use of the probability theory. Moreover, both possibilistic and probabilistic pertinence feedback can be effectively employed to improve the effectiveness of novice user's query processing.

研究动机与目标

• 解决新手用户提交的模糊和不精确查询中的目标提取挑战。
• 提升知识提取系统在面对用户查询中固有的不确定性和模糊性时的鲁棒性。
• 评估可能性理论在相关性反馈用于查询优化时，是否比概率理论更具韧性。
• 将语义网络与模糊反馈相结合，以提高提取目标的准确性和相关性。
• 验证交互式反馈在通过用户确认相关对象来优化目标提取方面的有效性。

提出的方法

• 利用可能性理论对用户查询和相关性判断中的不确定性进行建模，为概率理论提供更具鲁棒性的替代方案。
• 使用语义网络表示和推理感兴趣领域中的概念、关系和目标。
• 引入一个反馈循环，用户确认相关对象或目标，随后用于优化查询并提升后续结果。
• 应用模糊相关性反馈，根据用户反馈更新候选目标的信念度，调整其相关性评分。
• 结合模糊逻辑与语义推理，从非结构化或模糊的查询中提取目标。
• 采用两阶段流程：首先使用种子查询进行初始查询检索，随后通过用户反馈和基于可能性的重新加权进行迭代优化。

实验结果

研究问题

• RQ1 可能性理论是否在目标提取中比概率理论更能稳健地处理不确定性？
• RQ2 模糊相关性反馈在提升新手用户查询中提取目标的相关性方面有多有效？
• RQ3 在存在模糊查询的情况下，语义网络在多大程度上提升了目标提取的可解释性和准确性？
• RQ4 用户反馈在模糊和不确定环境中对优化目标候选的影响如何？
• RQ5 将可能性理论与语义网络结合是否在交互式知识提取中具有可行性与有效性？

主要发现

• 基于可能性理论的相关性反馈在处理用户查询中的不确定性和模糊性方面，表现出比概率反馈更强的鲁棒性。
• 将语义网络与模糊反馈相结合，显著提升了系统从不精确查询中提取相关目标的能力。
• 用户反馈有效优化了候选目标的相关性，且模糊模型表现出更好的收敛性和稳定性。
• 该方法减轻了新手用户查询中目标识别错误的负面影响，提升了整体系统有效性。
• 实验结果证实，与概率方法相比，模糊框架在鲁棒性和可靠性方面表现更优。
• 得益于语义推理与基于可能性的反馈相结合，系统即使在模糊或不完整查询中也能成功提取有意义的目标。