Ähnlichkeit im Kontext: Verbesserung semantischer Ähnlichkeitsranglisten durch Kontextinformationen

上下文相似度：通过上下文信息提高语义相似度排名

• 批准号: 22485116
• 负责人: Professor Dr. Krzysztof Janowicz
• 金额: --
• 依托单位: Department of Geography
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2006
• 资助国家: 德国
• 起止时间: 2005-12-31 至 2012-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/22485116?language=en
• 关键词: hnlichkeit; im; Kontext; Verbesserung; semantischer

Categorisation is a fundamental step in knowledge representation because it structures modelled knowledge into coherent graded groups of mutually similar members and relates these categories within a hierarchical and meteorological structure. This is a crucial task for information extraction and also the basic step that enables reasoning within an ontology or knowledge base. Semantic similarity measurements are used to calculate semantic distances between category members, but also across categories. Similarity measurement plays an important role in discovery and information extraction in heterogeneous sources where pure subsumption reasoning is too rigid and potential matches are not visible for the user. Current mathematical approaches to calculate semantic similarity between geospatial entity classes are only able to catch the similarity between feature-based categories, but cannot be used for role-governed and ad hoc categories. These types of categories group members that satisfy a certain relationship (e.g. transport(X,Y)) or play a spatiotemporal role in a given situation without having features in common which make them similar. For example, Human and Water are members of a flooding category because of their spatiotemporal role within an emergency scenario. Contrary to feature-based categories the relevant aspect for membership is not intrinsic but extrinsic. It is the named edge (transport) between nodes (X,Y). The goal of this project is to develop a formal framework for representing role-governed categories in onthlogies and to propose a mathematical semantic similarity measure (called role-based similarity measurement] to calculate distances between and within them. Its integration in geographic information services will lead to more efficient system support for people in decision situations. Information discovery fundamentally relies on similarity because it is often not precisely known what one is looking for. In contrast to subsumption reasoning based discovery it offers a ranking of the results. Similarity also supports identity assumptions such as needed in the area of cultural heritage. Two place names represent the same real world place if both share common events that are related (via our knowledge from historic documents) to them. Similarity measurements are further used in location based decision support services and allow for the integration of individual user profiles.

分类是知识表示的基本步骤，因为它将建模知识构建为由相互相似的成员组成的连贯分级组，并将这些类别在层次结构和气象结构中关联起来。这是信息提取的关键任务，也是在本体或知识库中进行推理的基本步骤。语义相似度测量用于计算类别成员之间以及跨类别的语义距离。相似性测量在异构源的发现和信息提取中发挥着重要作用，其中纯粹的包含推理过于僵化，并且潜在的匹配对用户来说是不可见的。当前计算地理空间实体类之间语义相似性的数学方法只能捕获基于特征的类别之间的相似性，而不能用于角色管理和临时类别。这些类型的类别将满足某种关系（例如运输（X，Y））或在给定情况下发挥时空作用的成员分组，但没有使它们相似的共同特征。例如，人类和水是洪水类别的成员，因为它们在紧急情况下的时空作用。与基于特征的类别相反，成员资格的相关方面不是内在的，而是外在的。它是节点 (X,Y) 之间的命名边（传输）。该项目的目标是开发一个正式的框架来表示本体论中的角色管理类别，并提出一种数学语义相似性度量（称为基于角色的相似性度量）来计算它们之间和内部的距离。它与地理信息服务的集成将为人们在决策情况下提供更有效的系统支持。信息发现从根本上依赖于相似性，因为它通常不准确地知道人们正在寻找什么。与包含相反 基于推理的发现它提供了结果的排名。相似性还支持身份假设，例如文化遗产领域所需的。如果两个地名共享与它们相关的共同事件（通过我们从历史文献中获得的知识），则它们代表相同的现实世界地点。相似性测量进一步用于基于位置的决策支持服务，并允许集成各个用户配置文件。