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The modern geographic information retrieval technology is based onquantitative models and methods. The semantic information in web documents andqueries cannot be effectively represented, leading to information lost ormisunderstanding so that the results are either unreliable or inconsistent. Anew qualitative approach is thus proposed for supporting geographic informationretrieval based on qualitative representation, semantic matching, andqualitative reasoning. A qualitative representation model and the correspondingsimilarity measurement method are defined. Information in documents and userqueries are represented using propositional logic, which considers the thematicand geographic semantics synthetically. Thematic information is represented asthematic propositions on the base of domain ontology. Similarly, spatialinformation is represented as geo-spatial propositions with the support ofgeographic knowledge base. Then the similarity is divided into thematicsimilarity and spatial similarity. The former is calculated by the weighteddistance of proposition keywords in the domain ontology, and the lattersimilarity is further divided into conceptual similarity and spatialsimilarity. Represented by propositions and information units, the similaritymeasurement can take evidence theory and fuzzy logic to combine all subsimilarities to get the final similarity between documents and queries. Thisnovel retrieval method is mainly used to retrieve the qualitative geographicinformation to support the semantic matching and results ranking. It does notdeal with geometric computation and is consistent with human commonsensecognition, and thus can improve the efficiency of geographic informationretrieval technology.

A Qualitative Representation and Similarity Measurement Method in Geographic Information Retrieval