SEKE 2012 Proceedings - Knowledge Systems Institute

III.
VALIDATING THE DIMENSION HEADERS
In [14] the dimensions including their headers are extracted
according to visual clues. Nevertheless, our research found that
the dimension headers are not always in the same category and
hence should not be covered by the same label. Let us consider
the example in Fig. 3. The group of dimension headers {Males,
Females, Sex unknown, Aboriginals, Non-Aboriginals,
Aboriginal identity unknown} have the same visual clues but
are not from the same group, and no label can represent them
as one collection.
To confirm that the dimension headers are related, we need
to use the numeric values in the table data region. This is done
by summing the numbers associated with the dimension
headers and one of the columns. In our example, the sum of the
numeric values under column “2004” associated with the
previously mentioned dimension headers is 316. The sum
exceeds the numeric value associated with the dimension
summary header, which is 158. Thus, for our purpose, this
proves that it is not a collection and it is not necessary to find a
label for them. On the other hand, if the sum is less than the
total under every column, as in the case of the table in Fig. 2,
the group of the dimension headers is acceptable as they are
only a proper subset. We called this the summarization rule.
Figure 3. Statistics table shows the summation problem
IV. FINDING isA RELATIONSHIPS - GENERAL SCHEME
In this section and in Section V, the main problem of this
research is tackled: given a set of headers that belongs to a
dimension { , ..., }, a label needs to be found such that an
isA relationship exists between the label and , 1