Intelligent Information Retrieval for Maps
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Analysis of spoken and search engine queries showthat people often look for maps of acertain location that are at a certain time or show asubject theme. Their search may behindered by the fact that maps in both physical anddigital collections are often organizedby region, and maps in non-geographical publicationsare essentially invisible to searchbecause they are rarely indexed. This research offersa way to resolve the problem bywriting algorithms that automatically classify mapsby time and theme as well as region,demonstrating these techniques in a web-basedprototype system. The classificatio...
Analysis of spoken and search engine queries show
that people often look for maps of a
certain location that are at a certain time or show a
subject theme. Their search may be
hindered by the fact that maps in both physical and
digital collections are often organized
by region, and maps in non-geographical publications
are essentially invisible to search
because they are rarely indexed. This research offers
a way to resolve the problem by
writing algorithms that automatically classify maps
by time and theme as well as region,
demonstrating these techniques in a web-based
prototype system. The classification is intelligent because it is aided by knowledge
resources. These automatic classification
were evaluated by comparing system-classified maps to
manual classification of the same
maps, with results showing automatic correspondence
with the manual at 75% for region,
69% for time, and up to 93% for theme (when plausible
classification categories are
counted). The discussion will appeal to those who
study information retrieval as well as
to those who just like maps.
that people often look for maps of a
certain location that are at a certain time or show a
subject theme. Their search may be
hindered by the fact that maps in both physical and
digital collections are often organized
by region, and maps in non-geographical publications
are essentially invisible to search
because they are rarely indexed. This research offers
a way to resolve the problem by
writing algorithms that automatically classify maps
by time and theme as well as region,
demonstrating these techniques in a web-based
prototype system. The classification is intelligent because it is aided by knowledge
resources. These automatic classification
were evaluated by comparing system-classified maps to
manual classification of the same
maps, with results showing automatic correspondence
with the manual at 75% for region,
69% for time, and up to 93% for theme (when plausible
classification categories are
counted). The discussion will appeal to those who
study information retrieval as well as
to those who just like maps.
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