一种地理信息检索的定性模型

提出一种定性地理信息检索方法, 用于地理信息的定性表达、语义匹配、推理和结果排序, 可以避免目前定量地理信息检索中语义信息丢失问题。采用命题逻辑方法综合表达查询和文档中的主题信息和地理语义信息, 将文档与查询的相关性度量分为主题相似度和地理相似度。前者通过命题关键词间加权本体距离获得。后者可进一步分为概念相似度和位置相似度, 分别基于地理本体和空间语义度量。由于信息的表达形式为命题和信息单元, 采用证据理论和模糊逻辑对上述子相关性度量进行统一建模。所提方法可以基于语义检索网页中的定性地理信息, 并对相关文档进行排序。这种检索和排序方法符合人类空间认知, 因此可以有效提高地理信息检索的效率。

A Qualitative Method for Geographic Information Retrieval

A qualitative method is presented for geographic information retrieval (GIR) to support qualitative representation, semantic matching, reasoning and ranking. The novel approach can avoid semantic information lost in current quantitative GIR methods. Information in documents and user queries are represented by propositional logic, which considers the thematic and geographic semantics synthetically. The similarity between documents and queries can be divided into thematic similarity and geographic similarity. The former is calculated by the weighted distance of proposition keywords in domain ontology, and the latter is further divided into conceptual similarity and location similarity which are measured by geo-ontology and spatial semantic respectively. Represented by propositions and information units, the similarity measurement takes evidence theory and fuzzy logic to obtain a general similarity from all sub similarities. This novel method retrieves qualitative geographic information from web and ranks documents semantically, which is consistent with commonsense, and thus can improve the efficiency of geographic information retrieval technology.