Towards geospatial semantic search: exploiting latent semantic relations in geospatial data
Towards geospatial semantic search: exploiting latent semantic relations in geospatial data
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DOI：
10.1080/17538947.2012.674561
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发表时间：
2014-01
影响因子：
5.1
通讯作者：
Wenwen Li;M. Goodchild;R. Raskin
中科院分区：
文献类型：
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作者：
Wenwen Li;M. Goodchild;R. Raskin
本文报告了我们的努力，以解决数字地球愿景的巨大挑战，从大量的地理参考数据的智能数据发现。我们提出了一种算法相结合的LSA和两层排名（LSATTR）算法的基础上修改余弦相似性，以建立一个更有效的搜索引擎-语义索引和排名（SIR）-语义启用，更有效的数据发现。除了能够处理基于主题的搜索，我们提出了一种机制，联合收割机地理空间分类和雅虎！GeoPlanet用于从空间查询中自动识别位置信息，并自动过滤空间上不相关的数据集。SIR的语料库采用NASA社会经济数据应用中心（SEDAC）的ISO 19115格式的元数据集。结果表明，我们的语义搜索引擎SIR建立在LSATTR方法优于现有的关键字匹配技术，如Lucene，在召回率和精度。此外，语料库中所有现有的词之间的语义关联被发现。这些协会提供了大量的支持，自动化人口的空间本体。我们希望这项工作能够通过推进基于语义的地理空间数据发现来支持数字地球愿景的运作。
This paper reports our efforts to address the grand challenge of the Digital Earth vision in terms of intelligent data discovery from vast quantities of geo-referenced data. We propose an algorithm combining LSA and a Two-Tier Ranking (LSATTR) algorithm based on revised cosine similarity to build a more efficient search engine – Semantic Indexing and Ranking (SIR) – for a semantic-enabled, more effective data discovery. In addition to its ability to handle subject-based search, we propose a mechanism to combine geospatial taxonomy and Yahoo! GeoPlanet for automatic identification of location information from a spatial query and automatic filtering of datasets that are not spatially related. The metadata set, in the format of ISO19115, from NASA's SEDAC (Socio-Economic Data Application Center) is used as the corpus of SIR. Results show that our semantic search engine SIR built on LSATTR methods outperforms existing keyword-matching techniques, such as Lucene, in terms of both recall and precision. Moreover, the semantic associations among all existing words in the corpus are discovered. These associations provide substantial support for automating the population of spatial ontologies. We expect this work to support the operationalization of the Digital Earth vision by advancing the semantic-based geospatial data discovery.