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Matching Representations at different Levels of Granularity

匹配不同粒度级别的表示

基本信息

批准号：
257850598
负责人：
Professor Dr. Heiner Stuckenschmidt
金额：
--
依托单位：
Lehrstuhl für Künstliche Intelligenz
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2016-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/257850598?language=en
关键词：
Matching Representations different Levels Granularity

项目摘要

Conceptual models of information structures and information flows are a central concept in computer science. They play a crucial role in the design and maintenance of information systems and the task of identifying mappings between different models as a basis for integrating different systems has become more and more important.Automatically identifying semantically correct mappings between models that describe a domain at different degrees of granularity pose some problems that cannot be adequately handled by existing matching approaches: (1) The mapping can be partial, that means that only some elements from one model actually do have counterparts in the other model and (2) the mapping can be n to m, meaning that one element in the first model can correspond to a combination of elements in the second model and vice versa. Existing methods for complex matching either require a high number of models as basis for correlation statistics or provide heuristic solution that only apply in a very restricted setting. Optimization-based matching algorithms that try to maximize the similarity between mapped elements from two models have proven to be the method of choice for one-to-one matching, due to their benefits over purely heuristic methods. Despite this fact, there have been no attempts so far to extend these approaches to the problem of complex matching. The goal of the project is (1) to develop new optimization-based methods for solving complex matching problems, i.e., matching problems that require the detection of n-to-m correspondences between elements in the models to be matched and (2) to show the practical benefit of the methods by applying them to the problem of matching real-world conceptual models, in particular ontologies and process models.We will approach this problem by combining a deep linguistic analysis of complex labels found in conceptual models with the idea of computing one-to-one correspondences not at the level of model elements, but on the level of meaningful linguistic components found in the labels of elements. We then generate complex mappings between model elements from the resulting mappings between parts of element labels. In the course of the project, we will develop and implement linguistic methods for analyzing complex class labels in ontologies and extend exsting work for analyzing activity labels in process models. We will further investigate efficient and effective formulations of complex matching task as an optimization problem and efficient and scalable methods for computing solutions taking our previous work on computing most probably consistent models as a starting point. Finally, we will spend significant effort on creating benchmark data for complex ontology matching and making it available to the scientific community.

信息结构和信息流的概念模型是计算机科学中的一个核心概念。它们在信息系统的设计和维护中起着至关重要的作用，识别不同模型之间的映射作为集成不同系统的基础的任务变得越来越重要。自动识别以不同粒度描述域的模型之间的语义正确映射带来了一些现有匹配方法无法充分处理的问题：（1）映射可以是部分的，这意味着只有来自一个模型的一些元素实际上在另一个模型中具有对应物，以及（2）映射可以是n到m，这意味着第一个模型中的一个元素可以对应于第二个模型中的元素的组合，反之亦然。复杂匹配的现有方法要么需要大量的模型作为相关统计的基础，要么提供仅适用于非常有限的设置的启发式解决方案。基于优化的匹配算法试图最大化来自两个模型的映射元素之间的相似性，已被证明是一对一匹配的首选方法，因为它们比纯粹的启发式方法更有优势。尽管如此，迄今为止还没有尝试将这些方法扩展到复杂匹配的问题。该项目的目标是（1）开发新的基于优化的方法来解决复杂的匹配问题，即，需要检测待匹配的模型中的元素之间的n到m对应的匹配问题，以及（2）通过将所述方法应用于匹配真实世界概念模型的问题来显示所述方法的实际益处，特别是本体和过程模型。我们将通过将对概念模型中发现的复杂标签的深层语言分析与计算一对一的概念相结合来解决这个问题。一个对应不是在模型元素的级别上，而是在元素的标签中发现的有意义的语言成分的级别上。然后，我们从元素标签部分之间的映射生成模型元素之间的复杂映射。在这个项目的过程中，我们将开发和实现语言学方法来分析本体中的复杂类标签，并扩展现有的工作来分析过程模型中的活动标签。我们将进一步研究复杂匹配任务作为优化问题的高效和有效的公式，以及计算解决方案的高效和可扩展的方法，以我们以前在计算最可能一致模型方面的工作为起点。最后，我们将花费大量精力为复杂的本体匹配创建基准数据，并将其提供给科学界。