Matching Representations at different Levels of Granularity

信息结构和信息流的概念模型是计算机科学的核心概念。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。在描述不同粒度程度的领域的模型之间自动识别语义正确的映射会带来一些现有匹配方法无法充分处理的问题：(1)映射可以是局部的，这意味着一个模型中的一些元素实际上在另一个模型中有对应的元素；(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第二个模型中的元素组合，反之亦然。现有的复杂匹配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，这已被证明是一对一匹配的首选方法，因为它们优于纯粹的启发式方法。尽管如此，到目前为止还没有人尝试将这些方法扩展到复杂匹配问题。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，即需要检测待匹配模型中元素之间的n- m对应关系的匹配问题；(2)通过将这些方法应用于匹配现实世界概念模型（特别是本体和过程模型）的问题来展示这些方法的实际效益。我们将通过结合对概念模型中发现的复杂标签的深度语言分析和计算一对一对应的想法来解决这个问题，而不是在模型元素的级别上，而是在元素标签中发现的有意义的语言组件的级别上。然后，我们从元素标签各部分之间的结果映射中生成模型元素之间的复杂映射。在项目过程中，我们将开发和实现用于分析本体中复杂类标签的语言方法，并扩展用于分析流程模型中的活动标签的现有工作。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，以及以我们之前计算最可能一致模型的工作为起点的高效和可扩展的计算解决方案的方法。最后，我们将花费大量精力创建复杂本体匹配的基准数据，并将其提供给科学界。

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.