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System support for exploration and expert feedback in resolving conflicts during integration of metadata

机译:系统支持探索和专家反馈,以解决元数据集成期间的冲突

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A critical reality in integration is that knowledge obtained from different sources may often be conflicting. Conflict-resolution, whether performed during the design phase or during run-time, can be costly and, if done without a proper understanding of the usage context, can be ineffective. In this paper, we propose a novel exploration and feedback-based approach [FICSR (Pronounced as "fixer")] to conflict-resolution when integrating metadata from different sources. Rather than relying on purely automated conflict-resolution mechanisms, FICSR brings the domain expert in the conflict-resolution process and informs the integration based on the expert's feedback. In particular, instead of relying on traditional model based definition of consistency (which, whenever there are conflicts, picks a possible world among many), we introduce a ranked interpretation of the metadata and statements about the metadata. This not only enables FICSR to avoid committing to an interpretation too early, but also helps in achieving a more direct correspondence between the experts' (subjective) interpretation of the data and the system's (objective) treatment of the available alternatives. Consequently, the ranked interpretation leads to new opportunities for exploratory feedback for conflict-resolution: within the context of a given statement of interest, (a) a preliminary ranking of candidate matches, representing different resolutions of the conflicts, informs the user about the alternative interpretations of the metadata, while (b) user feedback regarding the preferences among alternatives is exploited to inform the system about the expert's relevant domain knowledge. The expert's feedback, then, is used for resolving not only the conflicts among different sources, but also possible mis-alignments due to the initial matching phase. To enable this (system ←→~(informs) user) feedback process, we develop data structures and algorithms for efficient off-line conflict/agreement analysis of the integrated metadata. We also develop algorithms for efficient on-line query processing, candidate result enumeration, validity analysis, and system feedback. The results are brought together and evaluated in the Feedback-based InConSistency Resolution (FICSR) system.
机译:集成中的一个关键现实是,从不同来源获得的知识可能经常会发生冲突。无论是在设计阶段还是在运行时执行的冲突解决方案,其代价都很高,而且如果在未正确理解使用上下文的情况下进行,则可能会无效。在本文中,我们提出了一种新的基于探索和反馈的方法[FICSR(发音为“ fixer”),用于在集成来自不同来源的元数据时解决冲突。 FICSR不再依赖纯粹的自动化冲突解决机制,而是将域专家带入了冲突解决过程,并根据专家的反馈为集成提供了信息。特别是,我们不依赖于基于传统模型的一致性定义(一致性定义(每当存在冲突时,都会在很多情况中选择一个可能的世界)),而是引入了对元数据的分级解释和有关元数据的声明。这不仅使FICSR可以避免过早作出解释,而且还有助于在专家(对数据的主观)解释与系统对可用替代方案的(客观)处理之间取得更直接的对应。因此,排名的解释为解决冲突提供了探索性反馈的新机会:在给定的利益声明的背景下,(a)代表冲突解决方案不同的候选匹配项的初步排名会通知用户有关替代方案的信息(b)利用用户对备选方案之间的偏好的反馈来告知系统有关专家的相关领域知识。然后,专家的反馈不仅用于解决不同来源之间的冲突,而且还用于解决由于初始匹配阶段而导致的可能的失准。为了启用此(系统←→〜(通知用户)用户)反馈过程,我们开发了数据结构和算法,以对集成元数据进行有效的脱机冲突/协议分析。我们还开发了用于高效在线查询处理,候选结果枚举,有效性分析和系统反馈的算法。结果汇总在一起,并在基于反馈的InConSistency Resolution(FICSR)系统中进行评估。

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