使用特定领域依赖知识为物联网模式构建概念层次结构

V. Sithole, L. Marshal
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引用次数: 3

摘要

文献中物联网(IoT)模式的数量正在迅速增长。因此,这使得很难从大量相关模式中识别和区分一个模式。因此,有必要以一种有意义的方式组织这些模式,以促进快速检索,并指导物联网架构师构建分类方案,将一系列相关模式组合在一起。由于这些模式的名称通常没有揭示模式的核心本质,因此这些模式的分类通常变得困难。为了理解模式的本质,用户通常需要浏览几个页面,这些页面可能由于语义障碍和语言的丰富性而仍然模糊不清,难以理解。直观地说,这个问题可以通过分配一些最能描述每个模式核心本质的口头谓词来解决。在本文中,我们证明了形式概念分析(FCA)和概念格是支持这项任务的合适工具。因此,我们使用FCA来构建一个概念格,它作为一个语义索引来建模定义每个模式的核心属性的术语。我们引入了属性层次结构的概念,以科学地识别一个主要概念,这个概念似乎是每个物联网模式含义的基础。模式中更重要的属性由从根节点概念分支出来的概念表示,形成层次结构中的叶节点。这个概念格从从文档中提取的几个预先识别的句子中获取信息。这些句子描述了模式的核心属性。通过量化这些预先识别的句子与文档中其他句子之间的句子相似度,我们可以识别出可以从中提取概念以构建概念格的句子。实验结果表明,使用该方法组织物联网模式具有良好的性能。
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Building Concept Hierarchies for the Internet of Things Patterns Using Domain-specific Dependency Knowledge
The number of the Internet of Things (IoT) patterns in the literature is growing rapidly. As a result, this makes it difficult to identify and differentiate a pattern from a large number of related patterns. Thus, there is a need for organizing these patterns in a meaningful way to facilitate speedy retrieval and guide the IoT architects in building a classification scheme that groups a family o f r elated patterns together. The classification of these patterns is often made difficult by the fact that the names of these patterns generally do not reveal the core essence of the pattern. In order to understand the essence of a pattern, users are generally expected to go through several pages which may still be obscure and difficult to understand due to semantic barriers and richness of language. Intuitively, this problem can be addressed by assigning a few verbal predicates that best describe the core essence of each pattern. In this paper, we show that Formal Concept Analysis (FCA) and Concept Lattices are suitable tools to support this task. Accordingly, we make use of FCA to build a concept lattice, which serves as a semantic index to model terms that define the core attributes of each pattern. We introduce the notion of attributes hierarchies to scientifically identify the one main concept that seems to underlie the meaning of each IoT pattern. The more significant attributes for the pattern are represented by concepts that branch out of the root node concept, forming leaf nodes down the hierarchy. This concept lattice feeds from information taken from a few pre-identified sentences taken from a document. These are sentences that describe the core attributes of the pattern. By quantifying sentence similarity between these preidentified sentences and other sentences in the document, we can identify sentences from which we can extract concepts for building the concept lattice. Experimental results show a promising performance in using this approach for organizing the IoT patterns.
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