A framework for semantic link discovery over relational data

Oktie Hassanzadeh, Anastasios Kementsietsidis, Lipyeow Lim, Renée J. Miller, Min Wang
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引用次数: 66

Abstract

Discovering links between different data items in a single data source or across different data sources is a challenging problem faced by many information systems today. In particular, the recent Linking Open Data (LOD) community project has highlighted the paramount importance of establishing semantic links among web data sources. Currently, LOD sources provide billions of RDF triples, but only millions of links between data sources. Many of these data sources are published using tools that operate over relational data stored in a standard RDBMS. In this paper, we present a framework for discovery of semantic links from relational data. Our framework is based on declarative specification of linkage requirements by a user. We illustrate the use of our framework using several link discovery algorithms on a real world scenario. Our framework allows data publishers to easily find and publish high-quality links to other data sources, and therefore could significantly enhance the value of the data in the next generation of web.
用于发现关系数据上的语义链接的框架
发现单个数据源中不同数据项之间或不同数据源之间的链接是当今许多信息系统面临的一个具有挑战性的问题。特别是,最近的链接开放数据(LOD)社区项目强调了在web数据源之间建立语义链接的重要性。目前,LOD源提供了数十亿个RDF三元组,但数据源之间只有数百万个链接。这些数据源中的许多都是使用对存储在标准RDBMS中的关系数据进行操作的工具发布的。在本文中,我们提出了一个从关系数据中发现语义链接的框架。我们的框架基于用户对链接需求的声明性规范。我们通过在真实场景中使用几个链接发现算法来说明我们的框架的使用。我们的框架允许数据发布者轻松地找到并发布到其他数据源的高质量链接,因此可以显著提高下一代网络中数据的价值。
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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