Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data
Integrating ontological modelling and Bayesian inference for pattern classification in topographic vector data
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DOI：
10.1016/j.compenvurbsys.2009.07.005
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
Patrick Lüscher;R. Weibel;D. Burghardt
中科院分区：
文献类型：
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作者：
Patrick Lüscher;R. Weibel;D. Burghardt
提出了一种本体驱动的空间数据库丰富方法，以支持地图综合。与纯粹的算法方法相比，本体驱动的空间数据库丰富是一种很有前途的方法，可以提供更好的透明度、灵活性和可重用性。表现在空间模式中的地理概念通过用于触发适当的低级模式识别技术的本体来形式化。本文着重研究空间现象定义中常见的模糊推理，以及空间度量的复杂性对分类精度的影响。英国梯形房屋的概念作为一个例子，展示了如何在本体中对地理概念进行建模，以丰富空间数据库。由于它们很好地集成到本体中，并且能够处理模糊定义，有监督的贝叶斯推理被用于推理复杂的概念。该方法在代表四个不同城市建筑物的大型矢量数据集的实验中得到了验证。我们将所提出的方法得到的分类结果与更传统的本体方法产生的结果进行了比较。与传统的本体方法相比，该方法的性能要好得多。除了阐明使用本体丰富空间数据库的好处外，我们的研究还表明贝叶斯网络是一种合适的方法来整合地图学和地理信息科学中关于概念化的模糊知识。
This paper presents an ontology-driven approach for spatial database enrichment in support of map generalisation. Ontology-driven spatial database enrichment is a promising means to provide better transparency, flexibility and reusability in comparison to purely algorithmic approaches. Geographic concepts manifested in spatial patterns are formalised by means of ontologies that are used to trigger appropriate low level pattern recognition techniques. The paper focuses on inference in the presence of vagueness, which is common in definitions of spatial phenomena, and on the influence of the complexity of spatial measures on classification accuracy. The concept of the English terraced house serves as an example to demonstrate how geographic concepts can be modelled in an ontology for spatial database enrichment. Owing to their good integration into ontologies, and their ability to deal with vague definitions, supervised Bayesian inference is used for inferring complex concepts. The approach is validated in experiments using large vector datasets representing buildings of four different cities. We compare classification results obtained with the proposed approach to results produced by a more traditional ontology approach. The proposed approach performed considerably better in comparison to the traditional ontology approach. Besides clarifying the benefits of using ontologies in spatial database enrichment, our research demonstrates that Bayesian networks are a suitable method to integrate vague knowledge about conceptualisations in cartography and GIScience.