Open Geospatial Consortium (OGC) Web Services (OWS) are highly significant for geospatial data sharing and widely used in many scientific fields. However, those services are hard to find and utilize effectively. Focusing on addressing the big challenge of OWS resource discovery, we propose a measurement model that integrates spatiotemporal similarity and thematic similarity based on ontology semantics to generate a more efficient search method: OWS Geospatial Data Semantic Similarity Model (OGDSSM)-based search engine for semantically enabled geospatial data service discovery that takes into account the hierarchy difference of geospatial service documents and the number of map layers. We implemented the proposed OGDSSM-based semantic search algorithm on United States Geological Survey mineral resources geospatial service discovery. The results show that the proposed search method has better performance than the existing search engines that are based on keyword-based matching, such as Lucene, when recall, precision, and F-measure are taken into consideration. Furthermore, the returned results are ranked based on semantic similarity, which makes it easier for users to find the most similar geospatial data services. Our proposed method can thus enhance the performance of geospatial data service discovery for a wide range of geoscience applications.

中文翻译：

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OGC Web服务地理空间数据语义相似性模型，用于改善地理空间服务发现

开放式地理空间联盟（OGC）Web服务（OWS）对于地理空间数据共享非常重要，并在许多科学领域中得到广泛使用。但是，很难有效地找到和利用这些服务。着眼于应对OWS资源发现的巨大挑战，我们提出了一种基于本体语义将时空相似度和主题相似度相结合的度量模型，以生成一种更有效的搜索方法：基于OWS地理空间数据语义相似度模型（OGDSSM）的语义搜索引擎启用了地理空间数据服务发现，该发现考虑了地理空间服务文档的层次结构差异和地图层数。我们在美国地质调查局矿产资源空间服务发现中实现了基于OGDSSM的语义搜索算法。结果表明，在考虑召回率，精度和F度量的情况下，所提出的搜索方法比基于关键字匹配的现有搜索引擎（如Lucene）具有更好的性能。此外，返回的结果基于语义相似性进行排序，这使用户更容易找到最相似的地理空间数据服务。因此，我们提出的方法可以为广泛的地球科学应用程序增强地理空间数据服务发现的性能。这使用户可以更轻松地找到最相似的地理空间数据服务。因此，我们提出的方法可以为广泛的地球科学应用程序增强地理空间数据服务发现的性能。这使用户可以更轻松地找到最相似的地理空间数据服务。因此，我们提出的方法可以为广泛的地球科学应用程序增强地理空间数据服务发现的性能。