J. Software Engineering & Applications, 2010, 3: 65-72
doi:10.4236/jsea.2010.31008 Published Online January 2010 (http://www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes JSEA
Element Retrieval Using Namespace Based on Keyword
Search over XML Documents
Yang WANG1, Zhikui CHEN1, Xiaodi HUANG2
1School of Software, Dalian University of Technology, Dalian, China; 2School of Computing and Mathematics, Charles Sturt Uni-
versity, Australia.
Email: Wayag2000@yahoo.com.cn, zkchen@dlut.edu.cn, xdhuang@csu.edu.au
Received July 31st, 2009; revised September 12th, 2009; accepted September 22nd, 2009.
Querying over XML elements using keyword search is steadily gaining popularity. The traditional similarity measure is
widely employed in order to effectively retrieve various XML documents. A number of authors have already proposed
different similarity-measure methods that take advantage of the structure and content of XML documents. However,
they do not consider the similarity between latent semantic information of element texts and that of keywords in a query.
Although many algorithms on XML element search are available, some of them have the high computational complexity
due to searching for a huge number of elements. In this paper, we propose a new algorithm that makes use of the se-
mantic similarity between elements instead of between entire XML documents, considering not only the structure and
content of an XML document, but also semantic information of namespaces in elements. We compare our algorithm
with the three other algorithms by testing on real datasets. The experiments have demonstrated that our proposed
method is able to improve the query accuracy, as well as to reduce the running time.
Keywords: Semantics, Namespace, SVD, Text Matching
1. Introduction
Keyword search querying over XML elements has emer-
ged as one of the most effective paradigms in information
retrieval. To identify relevant results for an XML key-
word query, different approaches lead to various search
results in general. Some authors calculated the similarity
between the content of XML documents and query, only
analyzing the content and structure of XML (e.g., [1–3]).
Many algorithms calculate the degree of text of elements
matching with the keywords to produce the ranked re-
sult-list (e.g., DIL Query processing algorithm [4] and
Top-k algorithm [5]). The classical methods focus on
TF-IEF formula to calculate the cosine similarity between
elements and query (e.g., Tae-Soon Kim et al. [6]; Maria
Izabel M et al. [7]; Yun-tao Zhang et al. [8]).
In particular, overlaps of elements in XML documents
must be considered. For several overlapping relevant ele-
ments, we have to choose which one should be avoided
to ensure that users do not see the same information for
several times. Su Cheng Haw et al. [9] presented the
TwigINLAB algorithm to improve XML Query process-
ing. In this paper, we modify it to deal with the elements
overlap occurring in keyword search results.
On the basis of previous work, we make the following
contributions in this paper. Firstly, we utilize the seman-
tic information of namespaces in elements to filter the
relevant components since the text of elements are com-
monly related with semantic information of namespace.
Secondly, the precision and recall of our algorithm show
that the non-text matching but semantic relevant ele-
ments with respect to the keyword can be effectively
retrieved. Compared with traditional work, our algorithm
also shows the better performance on time execution
over a large collection of elements.
The rest of this paper is organized as follows: Section
2 introduces the element-rank schema by keyword search.
Section 3 presents the Namespace Filter Algorithm
(NFA). The experiments on the comparison of NFA and
related methods are reported in Section 4. Related work
is presented in Section 5, followed by the conclusion.
2. Element-Rank Schema
In this section, we utilize the namespace of elements to
describe our element rank schema. Another goal of util-
izing namespace is to filter relevant elements with the
keyword in a query to reduce time execution compared
with traditional algorithms.
Interestingly, namespaces can distinguish different ele-
Element Retrieval Using Namespace Based on Keyword Search over XML Documents
ments containing the same markup that refers to different
semantic meanings. As an illustration, we consider two
elements with the same markup of <table>:
<name>coffee table</name>
This will lead to the confliction when they are in the
same XML document. Thus, we utilize different name-
spaces of 'h' and 'f' to distinguish them as below.
<h:table xmlns:h = "http://.../fruit">
<f:table xmlns:f = "http://.../furniture">
<f:name>coffee table</f:name>
As discussed above, the text of elements is commonly
related with the semantic information of their namespaces.
Given the semantic information of namespace that is ir-
relevant to the keyword, it is not desirable to access all the
elements containing this namespace. In order to calculate
the semantic similarity between namespaces and key-
words, we map semantic information of namespaces and
keywords into different vectors in a concept vector space
created by Singular Value Decomposition (SVD) [10]
over a collection of elements. In order to do this, Defini-
tions 1 and 2 are provided as follows.
Definition 1: : a function that maps the
namespace of element v into a vector and represents a
special meaning in the concept vector space created by
Definition 2:: the
degree of relevance calculated by the cosine similarity
between the namespace vector of element v and the key-
word vector in concept vector space created by SVD.
)),(( keywordvprefixncorrelatio
The value of the correlation is commonly normalized
between the range [-1,1]. If the semantic meaning of
namespaces is very close to that of the keywords, the
value of ‘correlation’ will be around 1. Nobert Govert et
al. [2] proposed the concept of degree of relevance be-
tween elements. We extend it to include several intervals
in [-1,1] to describe the degree of the semantic similarity
of namespaces and keywords. Without loss of generality,
some definitions are provided to describe the degree of
relevance between namespaces and keywords.
Definition 3: High relevance: the high correlation
between namespaces and keywords which satisfies
1 keywordvprefixncorrelatio
Definition 4: Common relevance: the median corre-
lation between namespaces and keywords which satisfies
12 )),((
keywordvprefixncorrelatio (2)
Definition 5: Irrelevance: the lower correlation be-
tween namespaces and keywords which satisfies
keywordvprefixncorrelatio (3)
In the above equations, we have 10 12
, our
ranking algorithm accesses the elements containing the
namespaces that satisfy either Equation (1) or Equation
(2) rather than Equation (3).
3. Espace Filter Algorithm
In this section, we introduce some preliminary knowl-
edge, followed by presenting our algorithm called the
Namespace Filter Algorithm (NFA).
3.1 Preliminaries
The idftf
weight is commonly used to calculate the
term weight in documents in the field of traditional in-
formation retrieval. The purpose of our work is to re-
trieve the appropriate nested elements that contain the
relevant text to keywords instead of entire XML docu-
ments. So we extend idftf
to for ele-
ments in XML documents.
ieftf et
tf , the number of times that keyword t occurs in
the text of element e.
tf , the number of times that keyword t occurs
in the query q.
ief 10
log whereis the total number of elements
over a collection of XML documents, and is the
number of elements that contain the keyword. We
then give Definition 6 as below.
Definition 6: keyword weights in elements and query
0)log1( ,,10
, (4)
ieftfW qtqt )log1( ,10, (5)
where is keyword weight in the text of element ,
and keyword weight in a query . We calculate
the cosine similarity between query vector
and ele-
ment vector e
in Equation (6) on text matching factor.
),( (6)
Copyright © 2010 SciRes JSEA
Element Retrieval Using Namespace Based on Keyword Search over XML Documents67
Figure 1. Example of elements with the label ID in the XML
document tree
where is the ith keyword weight in
, and is the
ith keyword weight in
. Their weight values are calcu-
lated using Equation (4) and Equation (5), respectively.
In XML documents, elements are of varying size and
nested. Since relevant elements can be at any level of
granularity, either an element or its children can be rele-
vant to a given query. These facts commonly lead to a
problem that the same resulting elements of a query based
on keyword search will be presented to users for several
times. As an illustration, Consider the structure of an XML
document that is shown as the labeled tree in Figure 1.
Besides, let us suppose the relevant element list after key-
word search are listed in Table 1.
Elements with ID 0.2.1 and 0.2 are overlapping, so are
with 0.1, 0.1.1, and 0.12. If one element's parent is the
component of another element, the two relevant compo-
nents can be merged into one. An element will be merged
into its parent only if the number of the keyword occur-
ring in this particular element is less than that of its par-
ent element. In this way, there will be no overlap in the
resulting list shown in Table 2.
Furthermore, we denote value[v] calculated by NFA in
Section 3.2 as element v. Combining with Definition 2.
The final comprehensive evaluation formula about rele-
Table 1. Example of ranked list
Rank Self Parent
1 0.2.1 0.2
2 0.2 Root
3 0.1 Root
4 0.1.1 0.1
5 0.1.2 0.1
Table 2. Result list without overlap
Rank Self Parent
1 0.2.1 0.2
2 0.1 Root
vant elements ranking is given as Equation (7).
][)),(()( 21 vvalueakeywordvprefixncorrelatioavrank 
where 1
aa . In order to highlight the factor of
namespace's semantic, we have .
10 12  aa
3.2 NFA Description
In the following discussion, we will focus on presenting
the Namespace Filter Algorithm (NFA) and how it per-
forms based on the keyword search over a collection of
Let A be a set consisting of different elements to be
accessed by NFA, and the namespaces of elements in set
A satisfy either Equation (1) or Equation (2). Other ele-
ments not included in A will be neglected by NFA. The
length[e] in Equation (6) is defined as
Value[e] in Figure 2 gives the degree of text matching
between the text of element and keywords.
3.3 An Example
To evaluate the effectiveness of NFA, using an example,
we perform it with different pair values of 1
and 2
Equations (1) and (2). We empirically provide an XML
document named as record.xml in Figure 3 which con-
sists of many elements with namespace 'c' describing
semantic "computer" and 'n' describing "joy". Let the
query be "data and space in algorithm". Meanwhile, we
set 1
in Equation (1) and 2
in Equation (2) to 0.8
and 0.6, respectively. SVD is commonly applied to docu-
ments in traditional information retrieval. We extend it to
NFA : retrieve the ranked element based on the keyword
Input:query, a collection of relevant elements denoted as A
Output: top k elements of ranked result list
01 float value[N] = 0//N is the number of elements
02 float Length[N]
03 for each keyword t in the query
04 do for each pair(element A,tf(t,e))
05 do value[e] += //Equations.(4) and (5)
qtet WW ,,
06 end-for
07 end-for
08 for each element e
09 do value[e] = value[e] / length[e]
10 end-for
11 merge the overlap
12 calculate the rank[] with Equation (7)
13 return top K elements of rank[] over all documents
Figure 2. Namespace filter algorithm
Copyright © 2010 SciRes JSEA
Element Retrieval Using Namespace Based on Keyword Search over XML Documents
Copyright © 2010 SciRes JSEA
Table 3. Term-element matrix M
<c:cs xmlns:c = "http://....../computer">
<c:complexity>data and space</c:complexity>
<c:time>data in computer's Algorithm</c:time>
<c:java>data of Algorithm in computer science</c:java>
<n:joy xmlns:n = "http://....../happiness">
<n:in>no space with audience's joy</n:in>
<n:out>jackson dance in large space</n:out>
</root1> 0.1.2 0.1.4
Computer 0 0 1 0 1 1 0 0
Data 0 0 1 1 1 1 0 0
Space 0 0 1 1 1 0 1 1
Algorithm 0 0 0 0 1 1 0 0
Joy 0 0 0 0 0 0 1 0
elements in this example.
Each element in record.xml corresponds to a node in
the tree with labeled IDs in Figure 4.
Given the correlation value between the semantic
meaning of namespaces: 'c', and 'n', and that of the key-
words :"data","space", and "Algorithm", we construct a
term-element matrix denoted as M, the elements of
which are term frequencies occurring over all of ele-
ments in record.xml in Table 3.
Then we normalize matrix M denoted by M1 as fol-
M1 is decomposed into the following three matrixes
by SVD
Figure 3. Example of record.xml
Figure 4. Tree structure of record.xml
Element Retrieval Using Namespace Based on Keyword Search over XML Documents69
In the following, we consider the reduced semantic
space with two most informative dimensions. Let U1 be
first two columns of U, S1 be the diagonal square matrix
that contains the first two biggest eigenvalues 1.8397, and
13770 of S as diagonal elements, and other elements in
S1 are 0.V1 be the transpose of first two columns of V.
We then build up a new term-element matrix M2 by us-
ing U1* S1*V1 as below.
The correlation values between terms are shown in Ta-
ble 4a by using T
22. We then normalize these
values in table 4a to the range of [-1,1] as given in Table 4b.
According to Table 4b, the correlation values between
the semantic meanings of namespaces 'c','n' and those of
the keywords in the query are given in Table 5.
Consider the keyword search for "data" or "Algorith-
m" in a query. As shown in Table 5, both the values of
correlation of Namespace 'c' vector with "data" and "Al-
gorithm" vector satisfies Equation (1) and Equation (2).
In contrast, the correlation value of Namespace 'n' vector
and keyword vectors does not satisfy Equations (1) and
(2). So we have {0.2,0.2.1,,}A. The pa-
rameter values (in Section 3.1) of elements in set A are
listed in Table 6.
From line 05 to 11 of NFA in Figure 2, combining
with Table 6, the value[e] s of elements in set A are
shown in Table 7.
As shown in Table 7, there exists the overlap between
element 0.1 and other elements. After merging the over-
lap, the result is 0.1 including its descendent elements
0.1.2,0.1.3,0.1.4 as a whole components. The other re-
sulting element is 0.1.1 including all its descendent ele-
ments. Let , and in Equation (7) be 0.9 and 0.1,
respectively, and the correlation value between name-
space and keywords be 0.8085, which is the average cor-
relation value between “data" and "Algorithm". Then we
can get the final ranked result by using Equation (7) in
Table 8.
In order to exploit the relation between in Equation
(1), and in Equation (2) and search result, we assign
different pair values to and such as 0.6 and 0.3.
We still give the same query to perform NFA over re-
cord.xml. This time we focus on "space" in the query
rather than "Algorithm" and "data". Table 5 shows that
the correlation value between namespace 'c' vector and
"space" vector is 0.3038, which satisfies Equation (2) and
namespace 'n' vector and "space" vector is 0.5233, which
satisfies Equation (2). Consequently, the search result
performed by NFA is given in Figure 6.
As shown in Figures 5 and 6, the different degrees of
semantic information relevance between the name-
spaces and keywords will lead to various search results
by using NFA.
In summary, the degree of semantic relevance between
the namespace and keywords depends not only on their
semantic information similarity, but also on user-speci-
fied weights on other factors.
4. Experiments
In our experiments, we compare NFA with other related
Table 4. Correlation value between different pair of terms
in record.xml
Table 5. Correlation value between semantic of namespace
'c', 'n' vectors and other three keyword vectors over ele-
ments in record.xml
Correlation data space Algorithm
computer 0.9967 0.3038 0.6203
oy 0.0611 0.5233 -0.0935
Table 6. Times of "data","space","algorithm" occurring in
query and revelant elements of record.xml
Dewey ID )(
tf )(
tf )lg(
0.1 3 3 2
0.1.2 1 1 0
0.1.3 1 1 1 1 1 0 0 0 1
0.1.4 1 1 1
Table 7. The ranked result-list with element overlap
Rank Self Parent Value[e]
1 0.1 Root 1.6160
2 0.1.4 0.1 1.5779
3 0.1.3 0.1 1.5779
4 0.1.2 0.1 1.4145
5 0.1.3 1.4145
6 0.1.3 1
Table 8. Comprehensive ranking using Equation (7)
Rank Dewey ID Score
1 0.1 0.8893
2 0.1.1 0.7277
Copyright © 2010 SciRes JSEA
Element Retrieval Using Namespace Based on Keyword Search over XML Documents
algorithms and methods on two metrics: precision and
recall. The result of comparing NFA with the methods
that have the similar Precision and Recall on aspect of
time execution of algorithm is also presented. We set 0.9
to 1
, 0.6 to 2
, 0.9 to , and 0.1 to in Equation
(7) to perform NFA.
4.1 Experimental Setup and Results
Equipment: Our experiments are performed on a PC
with a 2.33GHz Intel(R) Core(TM) 2 Duo CPU, 3.25 GB
memory, and Microsoft Windows XP. The TermJoin
algorithm [11], semantic tree creation algorithm [12], and
NFA are all implemented in C++.
Data set: We have tested NFA on two data sets called
Dataset1 [13] and Dataset2 [14], respectively. In order to
show its performance, we add some namespaces to ele-
ments [13]. Each namespace represents the general idea
of text embedded in elements [13].
Query set: the query set consists of two parts with 13
queries that represent all kinds of queries over Dataset1
and Dataset2 in Table 9.
4.1.1 Precision and Recall
Precision is defined as the number of relevant elements
retrieved by keyword search divided by the total number
of elements, while recall refers to as the number of rele-
vant elements retrieved by keyword search divided by the
total number of existing relevant elements. We compare
the precision and recall of NFA with the Termjoin algo-
rithm [11], semantic tree creation algorithm [12] on
Dataset1 and CAS Query [7] on Dataset2. We then cal-
culate the precision and recall of top 20 components re-
trieved by each algorithm as reported in Figure 7.
As shown in Figure 7, the Term-join algorithm re-
trieves the relevant elements. However, it also retrieves
some non-relevant elements. The basic idea of the Term-
join algorithm is to calculate the degree of text matching
of elements with keywords rather than the latent semantic
information of text of elements. Furthermore, both NFA
and semantic tree creation algorithm efficiently solve the
semantic information similarity between text of elements
and keyword. However, they do not have the equal run-
ning time as given in Section 4.1.2. In [6, 7], authors pro-
vide the methods that utilize the semantic
Figure 5. Experimental result elements in record.xml re-
trieved by NFA within Equation (1) be 0.8 andin Equa-
tion (2) be 0.6
Figure 6. Experimental result elements in record.xml retrieved by
NFA with in Equation (1) be 0.6 andin Equation (2) be 0.3
Table 9. Query set on Dataset 1 and Dataset 2
Q11: pitch step B and octave 2
Q12: natural type
Q13: voice 1 and type eighth
Q14: music with voice 1 staff 1
Q15: music with beam begin and down
Q16: 16th type in music
Q17: 16th type and type of beam
Q18: 16th type and duration 2
Q21: best table in furniture
Q22: best fruit table in furniture
Q23: eat apple at the table
Q24: have coffee at the table
Q25: the list of table
Figure 7. Precision and recall on Dataset1
Figure 8. Precision and recall on Dataset2
Copyright © 2010 SciRes JSEA
Element Retrieval Using Namespace Based on Keyword Search over XML Documents71
Figure 9. The average running time of NFA and semantic
formation of markups in elements to calculate the se-
ussed in Section 2.1, namespace can distinguish
In terme in practice, we compare NFA to
ts with
knowledge, no existing work has
earches have been done on the area of
er related area in elements retrieval is ranking
tree creation algorithm over 100 thousand elements from
Dataset1 based on the queries from Q11 to Q18 in Table 9
mantic information similarity between the elements and
query. However, sometimes it can only get the relevant
components with various markups. In order to present the
difference of search results of CAS Query [7] and NFA,
we test both of them on Dataset2 consisting of elements
with namespace 'h' and 'f' nested in the same markup <ta-
ble>. Both of them are tested by queries from Q21 to Q25
in Table 9 and the precision and recall is shown in Figure
As disc
fferent elements even with the same markup which
leads to different precision of NFA and CAS in Figure 8.
4.1.2. Running Time of NFA and Semantic Tree Crea-
tion Algorithm
s of running tim
the semantic tree creation algorithm. We test both of
them on Dataset1, and plot the average running time
based on the queries from Q11 to Q18 in Table 9 over
100 thousand elements from Dataset1 in Figure 9.
The idea of NFA is to filter the relevant elemen
spect to keywords in order to reduce the running time
of the semantic tree creation algorithm [12], which ac-
cesses all elements in a collection to get the semantic
information similarity between the text of elements and
keywords. Figure 9 shows that the semantic information
of namespace in elements significantly reduces running
time compared with the semantic tree creation algorithm
over a large collection of elements.
5. Related Work
To be the best of our
formally studied on the namespaces [14] for elements
retrieval. There has been a large body of work on con-
tent-oriented of XML documents and corresponding
ranking schema.
Substantial res
king relevant matches between the content and the
query as the criteria. e.g., DIL Query processing algo-
rithm [4], Termjoin Algorithm [11] and Top-k algorithm
[5]. Jovan Pehcevski et al. [15] content that the purpose
of XML retrieval task is to find elements that contain as
much relevant information. However, some elements that
are not keyword matches may be also relevant to the
query but not return in those algorithms. The classical
method is to calculate the value of consine similarity be-
tween the content and keyword utilizing the formula of
TF-IEF, the related work have been reported in
[6–8,11,16,18,19]. Unfortunately, most of them still can-
not accurately calculate the similarity on semantic prob-
lem only by this formula. Li Deng et al. [12] present the
semantic tree creation algorithm. Other proposals are
given on semantic problem from the inner structure of
XML document (e.g., Hongzhi Wang et al. [1]; Norbert
Govert et al. [20]; Felix Weigel et al. [3]; Sihem
Amer-Yahia et al. [5]; M.S.Ali et al. [21]). However,
they have to do a large time execution. Benny Kimelfeld
et al. [5] have observed this shortcoming. They presented
the method which filters the relevant documents before
processing the algorithm. Due to the notion of methods
[22], we interestingly find that the namespace in elements
not only solve the latent semantic problems between
elements and keyword, but also filter the relevant ele-
ments based on the keyword to reduce time execution in
the traditional algorithm. The most related work to this
paper is [6,11], both of which have proposed the content
of markup or frequency of markup as a factor contributed
to semantic similarity between the content and query.
However, It cannot effectively distinguish the elements
with same markup representing different semantic infor-
hema based on keyword search. The classical scoring
function is tf-ief (e.g., [5,23]) in information retrieval.
However, many approaches simply calculate et
tf , with
respect to all elements of the collection [9] or p con-
sider it by estimating et
tf , across elements of the same
type [25]. et
tf , is also calculated based on the concen-
tration of thxt of the element and that of its descen-
dants [25,26]. A different approach is to compute et
tf ,
for leaf-elements only, which are then used to score
leaf-elements themselves. All non-leaf elements are
scored based on combination of the score of their de-
scendants elements. The propagation of score starts from
the leaf elements and can consider the distance between
the element being considered and its descendent
leaf-elements [27]. Similar notion is adopted by the DIL
algorithm [4]. V. Mihajlovi et al. [28] rank elements us-
ing a utility function that is based not only on the rele-
vance score of an element, but also on its size.
e te
Copyright © 2010 SciRes JSEA
Element Retrieval Using Namespace Based on Keyword Search over XML Documents
Copyright © 2010 SciRes JSEA
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