Ontology-based urban data exploration
Ontology-based urban data exploration
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DOI：
10.1145/3007540.3007550
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发表时间：
2016-10
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影响因子：
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通讯作者：
B. Balasubramani;Vivek R. Shivaprabhu;Smitha Krishnamurthy;I. Cruz;T. Malik
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文献类型：
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作者：
B. Balasubramani;Vivek R. Shivaprabhu;Smitha Krishnamurthy;I. Cruz;T. Malik
城市正在积极创建开放的数据门户，以实现对城市数据的预测分析。然而，大量的可观察的模式，可以提取的技术，如关联规则挖掘（ARM）的规则，使筛选模式的任务是一个繁琐和耗时的任务。在本文中，我们将探讨使用领域本体：（一）过滤和修剪规则，是本体中的一个更一般的概念的变化，以及（二）替换组的规则，一个单一的一般规则的意图，缩小初始规则的数量，同时保持语义。我们展示了几种方法的组合如何显着减少规则的数量，从而有效地允许城市管理者使用开放数据来生成模式，将其用于决策，并更好地指导有限的政府资源。
Cities are actively creating open data portals to enable predictive analytics of urban data. However, the large number of observable patterns that can be extracted as rules by techniques such as Association Rule Mining (ARM) makes the task of sifting through patterns a tedious and time-consuming task. In this paper, we explore the use of domain ontologies to: (i) filter and prune rules that are variations of a more general concept in the ontology, and (ii) replace groups of rules by a single general rule with the intent of downsizing the number of initial rules while preserving the semantics. We show how the combination of several methods reduces significantly the number of rules thus effectively allowing city administrators to use open data to generate patterns, use them for decision making, and better direct limited government resources.