A Qualitative Representation and Similarity Measurement Method in Geographic Information Retrieval

Abstract: The modern geographic information retrieval technology is based on quantitative models and methods. The semantic information in web documents and queries cannot be effectively represented, leading to information lost or misunderstanding so that the results are either unreliable or inconsistent. A new qualitative approach is thus proposed for supporting geographic information retrieval based on qualitative representation, semantic matching, and qualitative reasoning. A qualitative representation model and the corresponding similarity measurement method are defined. Information in documents and user queries are represented using propositional logic, which considers the thematic and geographic semantics synthetically. Thematic information is represented as thematic propositions on the base of domain ontology. Similarly, spatial information is represented as geo-spatial propositions with the support of geographic knowledge base. Then the similarity is divided into thematic similarity and spatial similarity. The former is calculated by the weighted distance of proposition keywords in the domain ontology, and the latter similarity is further divided into conceptual similarity and spatial similarity. Represented by propositions and information units, the similarity measurement can take evidence theory and fuzzy logic to combine all sub similarities to get the final similarity between documents and queries. This novel retrieval method is mainly used to retrieve the qualitative geographic information to support the semantic matching and results ranking. It does not deal with geometric computation and is consistent with human commonsense cognition, and thus can improve the efficiency of geographic information retrieval technology.