A Distributional Structured Semantic Space for Querying RDF Graph Data
A Distributional Structured Semantic Space for Querying RDF Graph Data
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DOI：
10.1142/s1793351x1100133x
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
A. Freitas;E. Curry;J. G. Oliveira;Seán O'Riain
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文献类型：
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作者：
A. Freitas;E. Curry;J. G. Oliveira;Seán O'Riain
创建关联数据Web的愿景带来了允许跨高度异构和分布式数据集进行查询的挑战。为了在今天的Web上查询关联数据，最终用户需要知道哪些数据集可能包含数据，以及哪些数据模型描述了这些数据集。允许用户在RDF中表达查询关系，同时从底层数据模型中抽象它们的过程代表了Web规模的链接数据消费的一个基本问题。本文介绍了一种分布式结构化语义空间，它支持对RDF数据进行与数据模型无关的自然语言查询。该方法的中心依赖于使用分布式语义模型来解决构建数据模型独立方法所需的语义解释级别。文章分析了所提出的空间的几何方面，提供其描述为分布式结构的向量空间，这是建立在广义向量空间模型（GVSM）。最终的语义空间被证明是灵活和精确的真实世界的查询条件下实现平均倒数排名= 0.516，平均。精密度= 0.482，平均召回率= 0.491。
The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data on the Web today, end users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a distributional structured semantic space which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation demanded to build the data model independent approach. The article analyzes the geometric aspects of the proposed space, providing its description as a distributional structured vector space, which is built upon the Generalized Vector Space Model (GVSM). The final semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall = 0.491.