III-SGER: Spatio-Temporal-Thematic Queries of Semantic Web Data: a Study of Expressivity and Efficiency

III-SGER：语义网络数据的时空主题查询：表达性和效率的研究

• 批准号: 0842129
• 负责人: Amit Sheth
• 金额: --
• 依托单位: Wright State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0842129&HistoricalAwards=false
• 关键词: III; SGER; Spatio; Temporal; Thematic

Spatial and temporal data are critical components in many applications. This is especially true in analytical applications ranging from scientific discovery to national security and criminal investigation. This exploratory research develops of new methods for modeling and querying spatial, temporal and thematic (STT) data. The methods differ significantly from traditionalapproaches for STT data management; they follow a paradigm that goes beyond querying for resources to querying about the relationships between resources.Three STT data management advances this will lead to are: (1) new query operators that exploit the graph-centric nature of Semantic Web data models, (2) new indexing and query processing techniques for STT data that are specialized for Semantic Web data models and (3) an extension of the SPARQL RDF query language to support STT queries.A second aspect of this project is to compare the STT-RDF approach described above with an alternative approach based on OWL-DL and qualitative spatial and temporal reasoning. This exploratory study evaluates whether the STT-RDF analytics approach provides a more efficient and expressive query language than the OWL-DL approach with space-time ontology. Specifically, (1) for the queries that can be encoded in both the formalisms, is the former implementation more efficient? and (2) are there queries that can be formulated in the STT-RDF analytics formalismthat cannot be expressed as OWL-DL queries? Throughout this project, special attention is given torepeatability of experimental results. All code will be open source, and all benchmarks, datasets and ontologies will be available through the project web site athttp://knoesis.wright.edu/research/semweb/projects/stt/

空间和时间数据是许多应用程序中的关键组成部分。在从科学发现到国家安全和刑事调查的分析应用中尤其如此。这种探索性的研究开发了新的方法建模和查询空间，时间和专题（STT）数据。这些方法与传统的STT数据管理方法有很大的不同;它们遵循的范式超越了查询资源，而是查询资源之间的关系。这将带来三个STT数据管理方面的进步：（1）新的查询操作符，利用语义Web数据模型的以图为中心的性质，（2）专门用于语义Web数据模型的STT数据的新索引和查询处理技术;（3）SPARQL RDF查询语言的扩展，以支持STT查询。本项目的第二个方面是将上述STT-RDF方法与基于OWL的替代方法进行比较。DL和定性时空推理。这项探索性的研究评估是否STT-RDF分析方法提供了一个更有效的和更有表现力的查询语言比OWL-DL方法与时空本体。具体来说，（1）对于可以用两种形式主义编码的查询，前一种实现是否更有效？（2）是否存在可以用STT-RDF分析形式化表示但不能表示为OWL-DL查询的查询？在整个项目中，特别注意实验结果的可重复性。所有代码都将是开源的，所有基准、数据集和本体都将通过项目网站athttp：//www.example.com提供。knoesis.wright.edu/research/semweb/projects/stt/