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Ontology-driven relevance reasoning architecture for data integration techniques

机译：有关数据集成技术的本体驱动相关性推理架构

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摘要

In order to execute a user’s query in a data integration system, the query execution process needs to be optimized. Before executing a query at real time, relevant and effective data sources must be identified. In this paper we propose an ontology-driven relevance reasoning architecture for future data integration techniques that will improve the response time for queries during the relevance reasoning process. Ontology has played a vital role to develop various component of the architecture. Source descriptions are plotted over the bitmap index in an intelligent and improved manner. Despite taking a lot of time in traversing local ontologies of source descriptions, bitmap index is exploited in relevance reasoning to identify the relevant and most effective data sources for user’s query. These identified data sources are ranked based on their relevance to the user’s query and then queried accordingly. A distinguished feature of the system is that it facilitates the user to write the query in terms of their local ontology concepts as well as global ontology concepts. A brief discussion is done on the results of the experimental study of proposed methodology for relevance reasoning and improvements are shown as compared to the previous systems.
机译：为了在数据集成系统中执行用户的查询，需要优化查询执行过程。在实时执行查询之前，必须识别相关和有效的数据源。在本文中，我们提出了一个本体驱动的相关性推理架构，用于将来的数据集成技术提高了相关性推理过程中查询的响应时间。本体论发挥了重要作用，可以开发建筑的各种组成部分。以智能和改进的方式绘制源描述以位图索引上绘制。尽管在遍历源描述的本地本体中进行了大量时间，但位图索引中的相关推理被利用，以识别用户查询的相关和最有效的数据源。这些识别的数据源基于它们与用户查询的相关性，然后相应地查询。系统的一个杰出特征是，它促使用户根据其本地本体概念以及全局本体概念来编写查询。简要讨论是对所提出的相关性方法的实验研究的结果，与先前的系统相比，改进的改进。

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