A Hybrid Semantic Similarity Measure for Spatial Information Retrieval
A Hybrid Semantic Similarity Measure for Spatial Information Retrieval
复制标题
空间信息检索的混合语义相似性度量
DOI：
10.1080/13875860802645087
复制
发表时间：
2009
影响因子：
1.9
通讯作者：
W. Kuhn
中科院分区：
文献类型：
--
作者：
A. Schwering;W. Kuhn
摘要 语义相似性是许多认知过程的核心，在人类处理和推理信息的方式中发挥着重要作用。特别是，从记忆中检索知识关键取决于相似性。同样，信息检索系统使用相似性来检测给定查询的相关信息。当前的信息检索系统主要应用句法技术来确定相似性。尽管这种句法相似性度量在包含大量文本的资源中表现良好，但如果术语的语义不明确可用，则它们无法适当地处理句法和语义异质性和歧义性。因此，它们相当僵化和不灵活，因为它们无法适应用户的需求和领域的概念化。此外，地理特征通过其几何和主题数据来区分。通常不可能通过单个名称或文本描述来捕获地理特征的复杂语义。因此，空间数据不同于企业数据库或 Web 上常见的文本文档。空间信息的检索需要新的、智能的检索机制来满足其特定的要求。基于语义的解决方案可以更轻松地适应用户需求，从而提高空间数据和检索方法的灵活性和可用性。本文研究了各种方法（最初是为了解释人类相似性判断而开发的）在空间信息检索背景下的适用性。我们提出了一种新的、混合的语义相似性测量方法，它可以表示空间数据的复杂语义。它允许通过确定查询与数据库内地理特征类型的语义描述之间的相似性来检索相关数据。混合相似性度量将概念空间的几何结构与语义网络的关系结构结合成一种具有内在相似性度量的、认知上合理的知识表示。
Abstract Semantic similarity is central to many cognitive processes and plays an important role in the way humans process and reason about information. In particular, the retrieval of knowledge from memory hinges crucially on similarity. Likewise, information retrieval systems use similarity to detect relevant information for a given query. Current information retrieval systems apply mainly syntactic techniques to determine similarity. Although such syntactic similarity measures have performed strongly with resources containing large amounts of text, they cannot appropriately cope with syntactic and semantic heterogeneity and ambiguity, if the semantics of the terms is not explicitly available. Therefore, they are rather rigid and inflexible as they cannot adapt to the user's requirements and conceptualization of the domain. Furthermore, geographic features are distinguished via their geometric and thematic data. It is often not possible to capture the complex semantics of geographic features by a single name or a textual description. Therefore spatial data is different from text documents typically found in enterprise databases or on the Web. Retrieval of spatial information requires new, intelligent retrieval mechanisms that satisfy its specific requirements. A semantics-based solution can more easily adapt to user needs and therefore increases the flexibility and usability of spatial data and retrieval methods. This paper investigates the suitability of various approaches—originally developed to explain human similarity judgement—in the context of spatial information retrieval. We propose a new, hybrid approach for semantic similarity measurement, which can represent the complex semantics of spatial data. It allows for retrieving relevant data by determining the similarity between the query and the semantic descriptions of geographic feature types within the database. The hybrid similarity measure combines the geometric structure of conceptual spaces with the relational structure of semantic nets to one, cognitively plausible knowledge representation with an inherent similarity measure.