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首页> 外文期刊>International journal of metadata, semantics and ontologies >Cross-querying LOD data sets using complex alignments: an experiment using AgronomicTaxon, Agrovoc, DBpedia and TAXREF-LD
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Cross-querying LOD data sets using complex alignments: an experiment using AgronomicTaxon, Agrovoc, DBpedia and TAXREF-LD

机译:使用复杂的比对交叉查询LOD数据集:使用AgronomicTaxon,Agrovoc,DBpedia和TAXREF-LD进行的实验

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摘要

An increasing amount of data sets have being published on the Linked Open Data (LOD), covering different aspects of overlapping domains. This is typically the case of agronomy and related fields, where several LOD data sets describing different points of view on scientific classifications have been published. This opens emerging opportunities in the field, providing to practitioners new knowledge sources. However, without help, querying the different datasets is a time-consuming task for LOD users as they need to know the ontologies describing the data of each of them. Rewriting queries can be automated with the help of ontology alignments. This paper presents a query rewriting approach that relies on complex alignments. This kind of alignment, opposite to simple ones, better deals with ontology modelling heterogeneities. We evaluate our approach on a scenario of query rewriting on agronomic information needs across four different datasets: AgronomicTaxon, AGROVOC, DBpedia, and TAXREF-LD.
机译:在链接开放数据(LOD)上发布了越来越多的数据集,涵盖了重叠域的不同方面。农艺学和相关领域通常是这种情况,其中已经发布了描述科学分类的不同观点的多个LOD数据集。这为该领域带来了新的机遇,为从业人员提供了新的知识来源。但是,对于LOD用户而言,在没有帮助的情况下,查询不同的数据集是一项耗时的任务,因为他们需要了解描述每个数据集的本体。重写查询可以借助本体对齐自动进行。本文提出了一种依赖复杂对齐方式的查询重写方法。这种对齐方式与简单对齐方式相反,可以更好地处理本体建模异质性。我们在跨四个不同数据集的农学信息需求的查询重写场景中评估了我们的方法:AgronomicTaxon,AGROVOC,DBpedia和TAXREF-LD。

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