Barton Libraries Matadata

http://simile.mit.edu/rdf-test-data/barton/ 

Queries suggested in: 
http://web.mit.edu/dna/www/abadirdf.pdf
based on Longwell browsing.

Query 1 (Q1). Calculate the opening panel displaying the counts of
the different types of data in the RDF store. This requires a search
for the objects and counts of those objects with property Type.
There are 30 such objects. For example: Type: Text has a count of
1, 542, 280, and Type: NotatedMusic has a count of 36, 441.

Query 2 (Q2). The user selects Type: Text from the previous panel.
Longwell must then display a list of other defined properties for resources
of Type: Text. It must also calculate the frequency of these
properties. For example, the Language property is defined 1, 028, 826
times for resources that are of Type: Text.

Query 3 (Q3). For each property defined on items of Type: Text,
populate the property panel with the counts of popular object values
for that property (where popular means that an object value appears
more than once). For example, the property Edition has 8 items with
value "[1st ed. reprinted]".

Query 4 (Q4). This query recalculates all of the property-object
counts from Q3 if the user clicks on the "French" value in the "Language"
property panel. Essentially this is narrowing the working set
of subjects to those whose Type is Text and Language is French.
This query is thus similar to Q3, but has a much higher-selectivity.

Query 5 (Q5). Here we perform a type of inference. If there are
triples of the form (X Records Y) and (Y Type Z) then we can infer
that X is of type Z. Here X Records Y means that X records
information about Y (for example, X might be a web page with information
on Y). For this query, we want to find the inferred type of
all subjects that have this Records property defined that also originated
in the US Library of Congress (i.e. contain triples of the form
(X origin "DLC")). The subject and inferred type is returned for all
non-Text entities.

Query 6 (Q6). For this query, we combine the inference first step
of Q5 with the property frequency calculation of Q2 to extract information
in aggregate about items that are either directly known to
be of Type: Text (as in Q2) or inferred to be of Type: Text through
the Q5 Records inference.

Query 7 (Q7). Finally, we include a simple triple selection query
with no aggregation or inference. The user tries to learn what a
particular property (in this case Point) actually means by selecting
other properties that are defined along with a particular value of this
property. The user wishes to retrieve subject, Encoding, and Type
of all resources with a Point value of "end." The result set indicates
that all such resources are of the type Date. This explains why these
resources can have "start" and "end" values: each of these resources
represents a start or end date, depending on the value of Point.