5 research outputs found
Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
This paper proposes a faceted information exploration model that supports
coarse-grained and fine-grained focusing of geographic maps by offering a
graphical representation of data attributes within interactive widgets. The
proposed approach enables (i) a multi-category projection of long-lasting
geographic maps, based on the proposal of efficient facets for data exploration
in sparse and noisy datasets, and (ii) an interactive representation of the
search context based on widgets that support data visualization, faceted
exploration, category-based information hiding and transparency of results at
the same time. The integration of our model with a semantic representation of
geographical knowledge supports the exploration of information retrieved from
heterogeneous data sources, such as Public Open Data and OpenStreetMap. We
evaluated our model with users in the OnToMap collaborative Web GIS. The
experimental results show that, when working on geographic maps populated with
multiple data categories, it outperforms simple category-based map projection
and traditional faceted search tools, such as checkboxes, in both user
performance and experience
Impact of Semantic Granularity on Geographic Information Search Support
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