Impact of Semantic Granularity on Geographic Information Search Support
This work addresses search support for users exploring complex geographic information, though it is incremental as it builds on existing query expansion methods.
The study investigated how semantic granularity affects query expansion in Geographic Information Retrieval, finding that fine-grained representations improve recall but precision depends on ontology alignment with human conceptualization.
The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration. We consider session-based query expansion in Geographical Information Retrieval, and investigate the impact of semantic granularity (i.e., specificity of concepts representation) on the suggestion of relevant types of information to search for. We study how different levels of detail in knowledge representation influence the capability of guiding the user in the exploration of a complex information space. A comparative analysis of the performance of a query expansion model, using three spatial ontologies defined at different semantic granularity levels, reveals that a fine-grained representation enhances recall. However, precision depends on how closely the ontologies match the way people conceptualize and verbally describe the geographic space.