Ontology Property-based Adaptive Crawler for Linked Data(OPAC)

Jihoon An, Younggi Kim, Minseok Lee, Younghee Lee
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引用次数: 1

Abstract

Linked Data is expected to play an important role for interoperability at the data level for the future internet. One of the main applications of Linked Data might be semantic query processing. The current method of warehousing based query processing requires the crawling of all data periodically and also frequent crawling of data from distributed datasets of Linked Data to make the data as up-to-date as possible. Frequent crawling is required for dynamic data to meet the high freshness requirement of real time applications. Crawling large datasets may cause serious scalability problems. In this paper, we propose an Ontology Property-based Adaptive Crawler to alleviate this problem. Linked data are crawled adaptively based on the Change Frequency of the Documents and the Properties. Performance evaluation shows that this system can reduce overhead costs by more than 70% while maintaining a high freshness of data.
基于本体属性的自适应关联数据爬虫(OPAC)
在未来的互联网中,关联数据有望在数据层面的互操作性方面发挥重要作用。关联数据的主要应用之一可能是语义查询处理。当前基于仓库的查询处理方法需要定期抓取所有数据,也需要频繁地从关联数据的分布式数据集中抓取数据,以使数据尽可能地更新。动态数据需要频繁的抓取来满足实时应用的高新鲜度要求。爬行大型数据集可能会导致严重的可伸缩性问题。本文提出了一种基于本体属性的自适应爬虫来解决这一问题。根据文档和属性的变化频率自适应地抓取链接数据。性能评估表明,该系统在保持高数据新鲜度的同时,可将间接成本降低70%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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