Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and thus the generation of relevant results. We argue that the explicit management of ontological knowledge, and of the meaning of concepts (by integrating linguistic and encyclopedic knowledge in the system ontology), can improve the analysis of search queries, because it enables a flexible identification of the topics the user is searching for, regardless of the adopted vocabulary. This paper proposes an information retrieval support model based on semantic concept identification. Starting from the recognition of the ontology concepts that the search query refers to, this model exploits the qualifiers specified in the query to select information items on the basis of possibly fine-grained features. Moreover, it supports query expansion and reformulation by suggesting the exploration of semantically similar concepts, as well as of concepts related to those referred in the query through thematic relations. A test on a data-set collected using the OnToMap Participatory GIS has shown that this approach provides accurate results.

中文翻译：

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概念感知地理信息检索

文本查询主要用于信息检索，让用户以自然的方式指定搜索目标。然而，用户和系统术语的差异可能会对用户信息需求的识别以及相关结果的生成提出挑战。我们认为本体知识和概念含义的显式管理（通过在系统本体中集成语言和百科全书知识）可以改进搜索查询的分析，因为它能够灵活识别用户正在搜索的主题因为，无论采用的词汇如何。本文提出了一种基于语义概念识别的信息检索支持模型。从识别搜索查询所指的本体概念开始，该模型利用查询中指定的限定符根据可能的细粒度特征选择信息项。此外，它通过建议探索语义相似的概念以及与通过主题关系在查询中引用的概念相关的概念来支持查询扩展和重构。对使用 OnToMap 参与式 GIS 收集的数据集进行的测试表明，这种方法提供了准确的结果。