In the context of disaster management, geospatial information plays a crucial role in the decision-making process to protect and save the population. Gathering a maximum of information from different sources to oversee the current situation is a complex task due to the diversity of data formats and structures. Although several approaches have been designed to integrate data from different sources into an ontology, they mainly require background knowledge of the data. However, non-standard data set schema (NSDS) of relational geospatial data retrieved from e.g. web feature services are not always documented. This lack of background knowledge is a major challenge for automatic semantic data integration. Focusing on this problem, this article presents an automatic approach for geospatial data integration in NSDS. This approach does a schema mapping according to the result of an ontology matching corresponding to a semantic interpretation process. This process is based on geocoding and natural language processing. This article extends work done in a previous publication by an improved unit detection algorithm, data quality and provenance enrichments, the detection of feature clusters. It also presents an improved evaluation process to better assess the performance of this approach compared to a manually created ontology. These experiments have shown the automatic approach obtains an error of semantic interpretation around 10% according to a manual approach.

中文翻译：

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地理空间数据到语义网的解释和自动集成

在灾害管理的背景下，地理空间信息在保护和拯救人口的决策过程中起着至关重要的作用。由于数据格式和结构的多样性，从不同来源收集最多的信息来监督当前情况是一项复杂的任务。尽管已经设计了几种方法来将来自不同来源的数据集成到本体中，但它们主要需要数据的背景知识。然而，从 Web 要素服务等检索的关系地理空间数据的非标准数据集模式 (NSDS) 并不总是被记录在案。这种背景知识的缺乏是自动语义数据集成的主要挑战。针对这个问题，本文提出了一种在 NSDS 中自动集成地理空间数据的方法。该方法根据语义解释过程对应的本体匹配结果进行模式映射。此过程基于地理编码和自然语言处理。本文通过改进的单元检测算法、数据质量和来源丰富以及特征集群的检测，扩展了之前出版物中所做的工作。与手动创建的本体相比，它还提供了改进的评估过程，以更好地评估这种方法的性能。这些实验表明，自动方法根据手动方法获得约 10% 的语义解释错误。本文通过改进的单元检测算法、数据质量和来源丰富以及特征集群的检测，扩展了之前出版物中所做的工作。与手动创建的本体相比，它还提供了改进的评估过程，以更好地评估这种方法的性能。这些实验表明，自动方法根据手动方法获得约 10% 的语义解释错误。本文通过改进的单元检测算法、数据质量和来源丰富以及特征集群的检测，扩展了之前出版物中所做的工作。与手动创建的本体相比，它还提供了改进的评估过程，以更好地评估这种方法的性能。这些实验表明，自动方法根据手动方法获得约 10% 的语义解释错误。