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Generating partial civil information model views using a semantic information retrieval approach

机译:使用语义信息检索方法生成部分民用信息模型视图

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Open data standards (e.g. LandXML, TransXML, CityGML) are a key to addressing the interoperability issue in exchanging civil information modeling (CIM) data throughout the project life-cycle. Since these schemas include rich sets of data types covering a wide range of assets and disciplines, model view definitions (MVDs) which define subsets of a schema are required to specify what types of data to be shared in accordance with a specific exchange scenario. The traditional procedure for generating and implementing MVDs is time-consuming and laborious as entities and attributes relevant to a particular data exchange context are manually identified by domain experts. This paper presents a method that can locate relevant information from a source XML data schema for a specific domain based on the user's keyword. The study employs a semantic resource of civil engineering terms to understand the semantics of a keyword-based query. The study also introduces a novel context-based search technique for retrieving related entities and their referenced objects. The developed method was tested on a gold standard of several LandXML subschemas. The experiment results show that the semantic MVD retrieval algorithm achieves a mean average precision of nearly 90%. The research is original, being a novel method for extracting partial civil information models given a keyword from the end user. The method is expected to become a fundamental tool assisting professionals in extracting data from complex digital datasets.
机译:开放数据标准(例如LandXML,TransXML,CityGML)是解决在整个项目生命周期内交换民事信息建模(CIM)数据时的互操作性问题的关键。由于这些模式包括覆盖各种资产和学科的丰富数据类型,所需的模型视图定义(MVDS)是根据特定的Exchange方案指定要共享的数据类型类型所需的类型。生成和实现MVDS的传统程序是耗时和费力的,因为与特定数据交换上下文相关的实体和属性由域专家指定。本文介绍了一种方法,可以根据用户的关键字从源XML数据模式找到相关信息。该研究采用土木工程术语的语义资源来理解基于关键字的查询的语义。该研究还介绍了一种用于检索相关实体及其引用对象的基于新的基于语境的搜索技术。开发方法对几个LandXML子化学型的金标准进行了测试。实验结果表明,语义MVD检索算法达到近90%的平均平均精度。该研究是原创的,是提取来自最终用户的关键字的部分民事信息模型的新方法。该方法预计将成为一个基本工具,协助专业人员从复杂数字数据集中提取数据。

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