PDF (1MB) - QUT ePrints

14 · Alsayed Algergawy et al.
data type compatibility table is built, as the one used in [Madhavan et al. 2001], as
shown in Fig. 7(a). The figure illustrates that elements having the same data types
or belonging to the same data type category have the possibility to be similar and
their type similarities (Typesim) are high. The type similarity between two objects
using the type compatibility table is:
Typesim(type1, type2) = TypeTable(type1, type2)
(3) Constraint Similarity. Another feature of an object that makes a small contribution
in determining the simple similarity is its cardinality constraint. The authors
of XClust [Lee et al. 2002] have defined a cardinality table for DTD constraints,
as shown in Fig. 7(b). Also, the authors of PCXSS [Nayak and Tran 2007] have
adapted the cardinality constraint table for constraint matching of XSDs. The cardinality
similarity between two objects using the constraint cardinality table is:
Cardsim(card1, card2) = CardinalityTable(card1, card2)
(4) Content Similarity. To measure the similarity between text nodes of XML documents,
the content information of these nodes should be captured. To this end, a
token-based similarity measure can be used. According to the comparison made
in [Cohen et al. 2003], the TFIDF ranking performed best among several tokenbased
similarity measures. TFIDF (Term Frequency Inverse Document Frequency)
is a statistical measure used to evaluate how important a word is to a document or
corpus. The importance increases proportionally to the number of times a word
appears in the document but is offset by the frequency of the word in the corpus. It
is used here to assess the similarity between text nodes. In this case, the content of
these nodes can be considered as multisets (or bags) of words. Given the content of
two text nodes represented as texts cont 1 and cont 2 , The TFIDF measure between
them is given by [Cohen et al. 2003]:
where,
and,
Contsim(cont 1 , cont 2 ) =
V (w, cont 1 ) =
∑
w∈cont 1∩cont 2
V (w, cont 1 ).V (w, cont 2 )
V ′ (w, cont 1 )
√∑
w ′ V ′ (w, cont 1 ) 2
V ′ (w, cont 1 ) = log (TF w,cont1 + 1). log (IDF w )
where, TF w,cont1 is the frequency of the word w in cont 1 , and IDF w is the inverse
of the fraction of names in the corpus that contain w.
These simple similarity measures are then aggregated using an aggregation function,
such as the weighted sum, to produce the simple similarity between two objects.
ACM Computing Surveys, Vol. , No. , 2009.