SEKE 2012 Proceedings - Knowledge Systems Institute

The table title and the dimension summary header are most
unlikely full sentences. Many resemble noun phrases.
Therefore, they need to be partitioned to appropriate sizes in
order to find a label to be matched with the ancestor set. Those
labels could be phrases or words. This study has developed
three different ways of partitioning in order to find the best
method to choose the candidate set. The candidates can be
partitioned into NLP chunks, or they can be partitioned to the
WordNet synset and the full chunk that would serve as a label
can then be retrieved. The third method is a combination of the
previous two.
1) Case #1: NLP chunks
Chunking any sentence/phrase, in this case the table title
and the dimension summary header, depends on the part of
speech tagging of words in the sentence. These chunks are
considered as candidates for the label that covers the dimension
headers. We refer to “Statistical parsing of English sentences”
[17] to divide the candidates into appropriate noun phrases,
verb phrases and words. For example, for the table title in Fig.
2, the chunks are {Cancer, new cases, selected primary site of
cancer, sex}, which become candidates in the candidate set.
2) Case #2: single words
Each candidate in the candidate set will be a single
WordNet synset that is found in the table title and the
dimension summary header.
3) Case #3: sliding window chunks
A sliding window technique is formed on the table title and
the dimension summary header. The candidates in the
candidate set are of size 1, 2 or 3 WordNet synsets picked from
this technique.
C. Assigning Label from the Candidate Set
This step computes the semantic similarity between the list
of ancestors, derived in step A, and the candidates in the
candidate set, derived in step B. This is done using the
semantic score presented in “WordNet-based semantic
similarity measurement” [18]. The semantic similarities are
computed between the ancestors in the ancestor set and the
candidate set, beginning with the highest scoring ancestor and
the candidates that appear after the prepositions in the table
title. The candidate that matches one of the ancestors is
selected as a label. This procedure is done in two steps,
depending on the type of candidates in the candidate set. The
steps are presented in the following algorithm.
1: for each in { , ..., }
2: for each candidate in {
3: divide each to each WordNet synset
{
4: for each
5: identify LCA between and , the
depth d for and ,
6: Sim= LCA / )
7: end
8: end
9: if Case #1 or Case#3 then{
compute the similarity for the ancestor
and the full chunk
10: =
11: =
12: T= ( + )/ ( + )
13: if T certain threshold then{
14: L =
15: quit loop}
16: else if Case#2 then{
17: if (Max Sim certain threshold) then{
18: L =
19: quit loop}
When the candidates are single WordNet synsets, the
semantic similarity between each ancestor and each candidate
is measured according to Wu and Palmer [19], as shown in (2),
where LCA is the least common ancestor depth in WordNet
taxonomy between two WordNet synsets; i.e., the ancestor and
the candidate, is the depth for the ancestor, and is the
depth for the candidate word.
Sim= LCA / ) (2)
When the candidates are chunks, the chunks must be
divided into single WordNet synset and their semantic
similarity computed with the ancestors. This is followed with a
computing of the full similarity of the full chunk, since any
semantic similarity function cannot compute the similarity
directly between the phrase and words, because the chunk
contains more than one WordNet synset and the ancestor is one
WordNet synset. Therefore, it is necessary to ensure that the
semantic similarity measure considers comparisons of a group
of WordNet synsets taken together.
Since (2) computes the similarity between two WordNet
synsets, the similarity scores for the case when the candidate is
a phrase must be collected. According to [18] the total
similarity, T, is equal to the summation of the similarities
between the phrase and the ancestor, p1, and p2 is the highest
similarity score between the ancestor and a word in the phrase.
This is divided by the summation of their length l1 and l2.
T= (p1+p2)/ (l1+l2) (3)
VI. EXPERIMENTAL RESULTS
Our system was tested with multidimensional tables from
Statistics Canada’s website of summary tables [20] and from
Statistics Austria website [21]. The tables are freely accessible
and belong to a few national statistical agencies that continue to
publish in HTML. They contain invaluable quantities of isA
relationships. More importantly, the tables are valuable because
they cover a wide range of topics. Our test dataset contained
some 305 randomly selected tables, containing 781 dimensions,
which were domain-independent and covering such topics as
education, construction, household, travel, and languages. By
implementing the algorithms, we found that we are able to
extract 92% of isA relationships successfully.
Different techniques were implemented to reach the goal of
this research. This section of the paper looks deeper into the
results to determine their effectiveness. First, the preprocessing
step described in Section V was found crucial to
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