J. Software Engineering & Applications, 2010, 3: 125-133
doi:10.4236/jsea.2010.32016 Published Online February 2010 (http://www.SciRP.org/journal/jsea)
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked
Software Interoperability
Bin Wen, Keqing He, Jian Wang
State Key Lab of Software Engineering, Wuhan University, Wuhan, China.
Email: binwenwebb@gmail.com
Received November 9th, 2009; revised December 1st, 2009; accepted December 20th, 2009.
Naturally, like the web, integrated software systems in Internet will have to be distributed and heterogeneous. To im-
prove the interoperability of services for SAAS, it is crucial to build requirements semantics that will cross the entire
lifecycle of services especially on requirements stage. In this paper, a requirements semantics interoperability extend-
ing approach called Connecting Ontologies (CO) that will act as semantics information carrier designing to facilitate
the requirements identification and services composition is proposed. Semantic measurement of Chinese scenario is
explored. By adopting the approach, a series of tools support for transport domain are developed and applied based on
CO and DPO (Domain Proble m Ontology) to enforce requirements engineering of networked software efficiently.
Keywords: Networked Software, Requirements Semantics, Requirements Engineering, Connecting Ontologies
1. Introduction
Ideally, users can access services based on their require-
ments without regard to where the services are hosted or
how they are delivered. Various computing paradigms
have promised to deliver IT as services including grid
computing, P2P computing, and more recently Cloud
computing. The latter term denotes the infrastructure as
“Cloud” from which businesses and users are able to
access application from anywhere in the world on de-
mand. Thus, the computing world is rapidly transforming
towards developing software for millions of consume as
a service, rather than to run on their individual computers
The development of networked software has emerged
varied forms and definitions. One is pervasive computing,
such as grid computing, e-science, and transparent com-
puting, which focus on resource sharing. Another cate-
gory is cloud computing based on SAAS (software as a
service) and related studies include SOA, Web Service,
Semantic Web Service etc. SAAS and virtualization of
hardware and software are two main features for Cloud
computing. Networked software that this paper refers to
[2] belongs to the second sort that is complex informa-
tion system based on Internet towards service computing.
Distribution, autonomy, opening and heterogeneity are
its basic features and stakeholders to be faced having
various sorts and interests. Typically, supporting diversi-
fied, personalized and dependable services to improve
user QoE (Quality of Experience) is the highest goal.
Requirements engineering (RE) is crucial to the suc-
cess of software engineering, especially for networked
software, and considering issues mainly include dynamic
elicitation and analysis, evolution modeling, require-
ments management and model verification of user re-
quirements and so on. Requirements modeling methods
mostly are classified as structural requirements modeling
and object-oriented requirements modeling according to
paradigm, and both of them can deal with functional and
nonfunctional requirements analysis. Now the typical
software RE approaches are goal-oriented, ontology-
oriented, scenario-based, problem framework, pre-
re qui re ment s analysis based on domain modeling, docu-
ment driving and aspect-oriented method [3].
The most widely significant approaches for networked
software RE are goal-oriented and pre-requirements analysis
based on domain ontology approach. Goal-oriented ap-
proach concentrates on analysis and modeling of early
requirements so as to help developer understand the mo-
tivation and expectation for various roles, and involves
the identification and analysis of functional an d nonfunc-
tional requirements goal. At present software RE is
switching from object-oriented to goal-oriented [4,5],
whereas goal-oriented approach has produced commer-
cial products for tool su pporting, for instance Cediti goal
analyzer: Objectiver. Accordingly goal-oriented re-
quirements analysis has become the hot spot of the
Building Requirements Semantics for Networked Software Inter o perability
studying of RE .
Virtually, pre-requirements analysis based on domain
modeling [6,7] is the process of requirements analysis
based on domain-level ontology knowledge. The issue of
ODE method based ontology [8] only acquires domain
conceptual knowledge especially, but it ignores the mod-
eling for task and functio nal knowledge.
All the above-mentioned requirements modeling
methods consider only for object-orient development.
The applicability and feasibility of those approaches for
service-oriented computing must be reconsidered. Re-
garding the features for service computing, role, goal,
process and service, the four fundamental elements can
be used to modeling for the users’ truly intentions of
networked software. A meta-modeling framework con-
taining the four fundamental elements, namely RGPS [9],
is presented for condu cting syn ergy and order ed structur e
requirements specification from disordered requirements
information. Furthermore, choosing ontology meta-
modeling [10] and encapsulating domain reusable core
services asset, O-RGPS (Ontology-RGPS) meta-model
proposal [2] is al so put fo rward (see Figure 1).
Based on O-RGPS requirements meta-model frame-
work, user requirements can be described from different
angle, level and granularity in order to form domain re-
quirements asset and store as OWL for reuse.
Interaction and collaboration of networked software is
a restricted semantic interoperable issue on essence.
Then, how to constrain and extend the semantic interop-
erability in the process of self-organization and action
emergence for the distributing services resource? How
to categorize the structure of interoperability? How to
satisfy stakeholders’ requirements?
Regarding the above issues, this paper proposes an
Onto logy
Domain Ontology
Ro le
Go al
t akeCharge
Atom ic
Co m posit e
Se rvice
Co mposit
Figure 1. Domain asset customizing based-on O-RGPS
requirement semantic interoperable extending approach
for networked software based on connecting ontologies
(CO) and furnishes the unified and dynamic semantic
information carrier for service aggregating and evolution
The rest of the paper is organized as follows: Section 2
explores software RE method based on domain ontology
and requirements asset; furthermore, provides formal
definition and aggregating method of connecting ontolo-
gies, and presents the related algorithm and integrating
environment design for interoperable extending of net-
worked software requirements semantics; Section 3
summarizes the related cutting-edge work in the research
community; at the last, we conclude the paper an d survey
the future work.
2. Connecting Ontologies for Networked
Networked software system includes the overall archi-
tecture and goal software system that can embody dy-
namic property of the architecture. Goal software system
is composed of services, whereas service resources dis-
tribute in network and are loosely coupled, dynamic
binding and permit various levels of semantic interop-
Since service resources are dynamically distributed,
for the sake of acquiring requirements knowledge from
multi-domain service resources, disseminated ontology
registry repositories in network require ontology en-
capsulation which is unified annotation of service with
respect to requirements semantic. Ontology registry re-
positories will accord with ISO meta-model framework
MFI (ISO/IEC SC32 19763) [11] that we participate.
Requirements are gained by requirements acquiring &
analysis (RAA) approach, and Requirements Sign On-
tology (RSO, Definition 11, similar to process specifica-
tion or workflow of application) is generated. Based on
RSO, published ontologies of requirements semantic for
available services are dynamic found and matched in
network. Matched ontologies and RSO form ontologies
group that is loosely coupled connected and dynamic
generated, named Connecting Ontologies (CO). Stated in
Figure 2, is requirements modeling approach for net-
worked software based on CO. In ontology level, re-
quirements semantic are dynamic acquired with semantic
extending and matching. Furthermore, initial require-
ments model is generated by reusing multi-domain re-
quirements asset. CO is the process of dynamic generat-
ing and continuous evolving, as stakeholders’ require-
ments are uninterruptedly changed and loosely coupled
for multi-domain requirements asset.
2.1 Domain Ontology Based on Description Logic
In the line of computer, ontology is explicit representa-
tion and description of conceptualization objects.
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability127
Figure 2. Requirements modeling for networked software
based-on connecting ontologies
Ontology can also be used for software RE as require-
ments representation and carrier. At the same time, since
reusability of broad-spectru m ontology is relatively hard ,
the principal application direction of ontology for soft-
ware requirements should be domain-oriented and prob-
Firstly, this section gives the definition of domain on-
tology based on description logic and other related defi-
nitions. Next section will apply these definitions. Then
requirements elicitation based on domain ontology and
requirements asset is designed and implemented.
Definition 1 (Domain Ontology based on description
logic). Domain Ontology is expressed as DO=<D, C, T,
A, LH>, where D represents domain; C represents a set
of concepts; T represents TBox; A represents ABox; and
LH represents lattice hierarchy of concepts.
Definition 2 (Relation Triple among concepts). For
domain ontology DO=<D, C, T, A, LH> with ,
cc C
and relation, if
rc cT
cand satisfy (1)
(2) .
rcc(3) .
, and is concepts inclu-
sion relation, then represents relation
triple between
cand .
Definition 3 (Semantic Association). For two relations
 2222
 12
semantic association between 1
and 2
Definition 4 (semantic association path). For a set of
relations {|,,, 1,2,,}
ii piiqi
 
 dp
and rela-
tion triples , ,
, if ,for ,then
DO have a semantic association path in X from
ij Xj
 ,m
where semantic associations 11
ii i
im d
exist, namely semantic association path be-
tween concept and .
Definition 5 (concept semantic depth, Depth). Apart
form the class for itself, the meaning of ontology con cept
is also described by the associated classes, namely con-
cept semantic depth. To calculate semantic depth, let the
Depth of ontology root concept is zero, if the Depth of
concept c, Depth(c), is I, then the Depth of its father
concept (if existed) is I-1 and the Depth of its child con-
cept (if existed) is I+1.
2.2 Connecting Ontologies
Connecting ontologies based on semantic matching of
multi-domain requirements asset only utilize local or part
of ontologies registry repositories for services. Modulari-
zation is an important technique of ontology reuse for ser-
vices. Different researchers have different definitions or
designations including segment, module, view or sub-
ontology etc. The paper adopts sub-ontology [12] notion.
Some definitions and algorithms are presented as follows.
Definition 6 (sub-ontology). For domain ontology
DO=<D,C,T,A,LH>, a sub-ontology sub-Onto consists
of 5 elements <Csub, Tsub, Asub, LHsub, I>where Csub
represents the set of sub-Onto concepts which denotes
the context of sub-ontology; ; there exist
semantic association or semantic association path in Csub;
Tsub T, Asub A represent sub-Onto’s local knowl-
edge base for Tsub, Asub; LHsub represents lattice hierarchy
of concepts; and I represents index pointer towards DO.
If sub-ontology=DO or sub-ontology have nondetermin-
istic domain, then I is nil.
Definition 7 (sub-ontology space in same source). For
DO=<D,C,T,A,LH>, sub-ontology space in same source
Space represents {<sub-Ontob, B, DO| sub-On-
tob.I=DO, B
2.2.1 Algorithm: Sub-Ontology Extracting Algorithm
For DO=<D,C,T,A,LH>, <CON,n,DO> is the input of
sub-ontology extracting, where CON={con1, con2, …,
conk} represents a set of concepts which will be matched;
DO represents father ontology; n represents the depth of
travel. Based on [12], we can get sub-ontology extracting
algorithm. The outcome of the algorithm is a sub-ontology
Sub-ontology extracting algorithm can be seen from
Algorithm 1.
Attentively, semantic similarity matching can be de-
scribed in details: for any two concepts C1 and C2, as-
suming string S1 and S2 is the name of C1 and C2 re-
spectively. Firstly, lexical analysis that preposition, con-
junction, pronoun and interjection are cancelled is carried
out for two strings, whereas continuous and meaning
words are reserved. Strings S1 and S2 will be transferred
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability
to <S1w1,…,S1wn> and <S2w1 ,…,S2wm>. For any words
S1wi<S1w1,…,S1wn> and S2wj
<S2w1,…,S2wm>, we can
calculate two words’ similarity similarityScore(S1wi,
S2wj)=wst.lookup(S1wi,S2wj). This similarity is acquired by
looking up similarity table which is generated by experts
in matching computing by using words association tool
(such as WordNet) in advance. If n<=m, then for S1wi, we
can find S2wj in accordance with maximum similarity,
namely matchscore(S1wi, S2wj)=similarityScore(S1wi, S2wj).
Finally similarity between two concepts is match-
score(C1,C2)=Sum(matchscore(S1wi, S2wj))/n.
2.2.2 Algorithm: Sub-Ontology Merging Algorithm
For a set of sub-ontology, onto-set consists of {Sub-
Onto1, Sub-Onto2, …, Sub-Onton}, n2, and the out-
come of the algorithm generates a sub-ontology Onto=
Sub-ontology merging algorithm can be seen in Algo-
rithm 2 in details.
Definition 8 (maximum self-contained sub-ontology
on concepts). For a set of concepts C which will be
matched and a sub-on tology ex tracting algorithm, the last
sub-ontology represents maximum self-contained sub-
ontology on concepts C, where the set of concepts in the
extracted sub-ontology unable to increase along with
addition of travel depths to cease th e extracting process.
Definition 9 (domain requirements ontology). For
Algorithm 2 sub-on tolo gy merging algorithm.
INPUT:onto-set={Sub-Onto1, Sub-Onto2,….., Sub-Onton},n >=2
OUTPUT: Merge(onto-set)
1: add all concepts in Sub-Onto1.Csub from onto_set
to a new set C
2: add all items in Sub-Onto1.Tsub from onto_set to a
new set T
3: add all items in Sub-Onto1.Asub from onto_set to a
new set A
4: i<- 2;
5: repeat
6: get i-th Sub_Onto Sub-Ontoi from onto_set
7: for each concept CK in Sub-Ontoi.Csub
8: if Ck not in C then
9: add Ck to C
10: end if
11: end for
12: for each item Mj in Sub-Ontoi.Tsub
13: if Mj not in T then
14: add Mj to T
15: end if
16: end for
17: for each item Aq in Sub-Ontoi.Asub
18: if Aq not in A then
19: add Aq to A
20: end if
21: end for
22: i<- i+1
23: until i>=n
24: get the corresponding Onto = <C,T,A,LH,nil>
Algorithm 1 sub-ontology extracting algor ithm.
OUTPUT: sub-Onto
1: i<-1
2: Repeat
3: get i-th concept coni from CON
4: remove coni from CON
5: if exist a concept c satisfy concept coni semantic
similarity match constraint in DO
6: then
7: do breadth-first traversal from c through rela-
tion in DO
8: if another concept c’ in DO is reached in
9: then
10: ad d the tripl es alon g the tr av ersed path from c
to c’ into Tsub
11: add c to a set Csub
12: add c’ to a set Csub
13: remove c’ from CON
14: end if
15: stop when traversal up to a depth of n
16: end if
17: i<- i+1
18: Until CON is empty or i>=k
19: m<- |Csub|
20: If m<k then return nil
21: end if
22: get the set of propertys Tsub from DO for all the
concepts in CON
23: get the set of individuals Asub from DO for all the
concepts in CON
et the extractin
convenient requirements acquisition and matching, do-
main requirements ontology is a special DO which only
have two concepts with semantic depth Depth=1 in the
sets of concepts: Operation denotes requirements verb
concept and Entity denotes requirements noun concepts.
Maximum self-contained sub-ontology of the set of op-
eration is called operation ontology and maximum self-
contained sub-o ntolog y of the set o f entity is called en tity
ontology for domain requirements ontology.
Definition 10 (Domain Problem Ontology, DPO).
Domain Problem Ontology (DPO) represents as Merge
RGPS(Extracting (P, Dep,asseti),indexi)), where P
represents a set of problem’s concepts; Dep refers to
travel depth; RGPS represents domain-customized asset
based RGPS; indexi represents source ontology index
with respect to matched problem concepts of RGPS as-
Note that the Problem is a specific application context,
for example travel is a Problem for traffic domain.
Definition 11 (Requirements sign ontology, RSO).
Requirements sign ontology RSO consists of 3 elements
<DSorl, Concept, Control>, where DSorl represents in-
put in domain requirements service language; C repre-
sents the set of extracting concepts from DSorl; Con-
DPO.C; Control represents control structure
among matched service ontologies mainly including se-
quence, choice, split-union, any order, cycle.
CO are a sub-ontologies set with different sources in
which involve dynamic finding and matching ontologies
of published services, and RSO serves as mediator and
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability129
conducts the process of generating CO for service-oriented
Definition 12 (connecting ontologies, CO).Connecting
ontologies (CO) consists of <RSO, DPO, Mapping-Onto-
Set>, where RSO represents requirements sign ontology;
DPO represents problem-oriented domain problem on-
tology; Mapping-Onto-Set represents matched sub- on-
tology set of different source .
Based on sub-ontology extracting algorithm and the
direction of RSO, requirements semantic of CO firstly
execute the matching for DPO. The rest of unabsorbed
parts by DPO for CO run ontologies finding and match-
ing from multi-domain services in network to satisfy
requirements semantic for stakeholders. General speak-
ing, the matched ontologies always denote some sub-
ontologies of ontologies with respect to multi-domain
services, and they are semantically matching with RSO,
namely Oi (i=1~n). Then, as seen in Figure 3, connecting
sub-ontology O0 of DPO and sub-ontologies Oi of on-
tologies for multi-domain services according to RSO that
acts as the center will dynamically generate CO. Ac-
cordingly, dynamically generated CO not only contain
O0 which is domain-oriented and tightly couple with
DPO, but also do it include some services ontologies Oi
for different domain i and loosely couplin g with RSO. A
few of unmatched services based CO will be solved by
customizing manufacture.
2.3 Domain Problem Ontology
According to Definition 10, Domain Problem Ontology
(DPO) is really a composite sub-ontology in terms of
problem by extracting from Domain Ontology and RGPS
requirements assets that express as OWL format. DPO is
very important in the creating process of CO and acts as
problem vision for CO. Creating CO firstly need adopt-
ing and matching with DPO, so the quality of DPO is
crucial for the success of appropriate and preferred match
regarding the contract ontology (i.e. CO ) of all circles
for software web clustering.
R GPS Assets
services p ool
R efined requirements
Provid e rs
matching results
or just-in-time
process compositi on &
application deployment
on v&v
O1 O2
coupling lo osely c oupling
Distributing services
Dom ain ontology-based
requirements acquiring
Figure 3. Connecting ontologies
We believe that: 1) semantic distance is only necessary
and fundamental measure method for semantic interop-
erability capability; 2) for semantic interoperability
measurement, semantic distance is not sufficient condi-
tion; 3) not only do semantic interoperability capability
relate to similarity but also tightly associate with the
contracted standard (i.e. CO) for both sides and really
CO is sufficient condition for interoperability.
Generating DPO can adopt two fashions: semi-automated
method directed by domain experts and fully automated
method. We have realized the first fashion in our domain
modeling tool designing to acquiring RGPS assets and
automated fashion is now designing and optimizing. For
automated fashion, we considered problem as follo ws: 1)
the relation between DPO extracting depth (traverse
depth) and CO matching degree with RSO; 2) the rela-
tion between DPO extracting depth (traverse depth) and
extracting time cost.
For the above issues, we work out an experiment for
evaluating these relations.
2.3.1 Experiment Design
Regarding low-scale Transport ontology (concepts num-
ber below 200) and OWL formatted R, G and P, experi-
ment will evaluate the capability between DPO extract-
ing depth associated with CO matching degree and time
spending. Firstly, using Algorithm 1, 4 ontologies in-
cluding Transport ontology, R, G and P [9], will be exe-
cuted in accordance with the word “travel” and its syno-
nym and outcome will be merged to generate DPO by
Algorithm 2. RSO can be obtained by requirements ac-
quiring tool [13] that we have implemented. Matching
degree is manually achieved by domain experts between
RSO and DPO.
2.3.2 Result Evaluation and Discussion
In the simulate experiment, the initial value of DPO ex-
tracting depth is 1. Through changeable extracting depth,
we can get different matching degree and time cost for
different depth value in order to analysis the influence of
depth for entire CO generating process. Figure 4 is the
result for diffe rent depth val ue.
According to the result, higher depth value will have
higher matching degree with RSO. When DPO extracting
depth is higher, the scale of DPO sub-ontology is also
biggish correspondingly. Considering the principle of
space locality, the reuse probability of DPO will evi-
dently increase to enhance the matching degree with
RSO. But higher depth value will lead to more time
spending for creating DPO. At the same time, matching
degree do not obviously enhance when the depth value
increase from 6 to 8. It shows that only increasing depth
value is not always efficient for improving matching de-
gree. Since adopting sound depth value is very important
for DPO to optimize the matching performance. The time
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability
Figure 4. DPO extracting simulation result
cost of the experiment is higher than large-scale single
ontology extracting in [12] because the experiment adds
the spending of merging process.
The drawback of this experiment is low-scale original
ontology, so future work will execute on large-scale on-
tologies to obtain valuable result for real-wor ld.
2.4 Interoperability Extending Integrating
Environment for Requirements Semantic
Based CO
Regarding travel problem in urban traffic domain, simu-
lation tests for acquiring requirements semantic b ased on
CO [14] have shown that the semantic interoperability
extending approach provides semantic information car-
rier for networked software and furnishes semantic goal
for on-demand service aggregating. But now both RSO
perfection and CO dynamic generating mainly rely on
manually participating and customizing by requirements
analyzers frequently, and quantitative measurement is
absent for denoting semantic distance and interoperabil-
ity level. Farther studies are listed as follows: 1) interop-
erability extending integrating environment for require-
ments semantic; 2) measurement system for requirements
semantic interoperability.
2.4.1 Requirements Semantics Distance for Chinese
Now, software requirements semantics mainly adopts
ontology encapsulation style, and requirements matching
will reduce to similarity comparing among entities. Basic
elements of entity inclu de concept, relation and instance.
Main measurement feature of concept are: concept name
(no semantics, only consider linguistic and literal simi-
larity, such as some distance formula [15]), concept se-
mantics similarity, concept structure. Main measurement
feature in relation involve property name, domain and
range. Instance is auxiliary measurement for con cept.
Semantics distance refers to a measurement of seman-
tics similarity or association between two semantic enti-
ties. Semantic entities involving th is paper are key words
of documents. In general, semantics distance is a real
number in [0,
). Semantics distance has tight associa-
tion with word similarity. Between two words, the bigger
semantics distance is, the lower semantics similarity is
and vice versa. They can be built a simple correspon-
dence that need satisfy some conditions as follows: 1)
similarity is 1 when semantics distance is 0 between two
words; 2) similarity is 0 when semantics distance is in-
finity between two words; 3) between two words, the
bigger semantics distance is, the lower semantics similar-
ity is (monotony descend).
For two words w1 and w2, similarity expressed as
Sim(w1,w2), semantics distance is Dis(w1,w2), then one
can define a simple transfer relation that satisfy the above
Sim(w1 ,w2)=
(, )Dis ww
is a adjustable parameter that embody the words’
distance value when similarity is 0.5. In the most cases,
directly computing the words’ similarity is difficult, so
distance measurement can be calculated in advance and
then transfer the similarity for words.
In general, thesaurus is the basis of the semantics dis-
tance measurement throughout computing MSCA (the
Most Specific Common Abstraction) to acquire. To cal-
culate semantics distance, one must use a comprehensive
and exact structural semantic resource repository.
Hownet (http://www.keenage.com) that involves more
complete semantics knowledge conten t and is referred in
some Chinese information processing is suitable for this
Hownet includes two main definitions: concept and
sememe. Concept is a description for vocabulary’s se-
mantics and every word can be expressed several con-
cepts. Concept applies a knowledge representation lan-
guage that uses sememe as vocabulary to describe.
Differentiated from the other thesaurus (e.g. Wordnet),
Hownet don’t reduce concept to a tree-like hierarchical
architecture and that try to depict every concept using a
series of sememes. Hownet adopts 1500 sememes which
are divided into some categories as follows:
1) Event; 2 ) entity; 3) attribu te; 4) aValue; 5) quantity;
6) qValue; 7) SecondaryFeature; 8) syntax; 9) EventRole;
10) EventFeatures.
For these sememes, they can be reduced to 3 groups:
group 1 is called basic sememe to describe semantics
feature for single concept containing sememes from
category 1 to category 7; syntactic sememe only include
category 8 to describe syntactic feature for words; group
3 contain category 9 and 10 called relation sememe to
denote relation between concepts (similar to lattice rela-
tion from lattice syntax).
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability131
Semantics distance d1(p1,p2) between two sememes p1
and p2 is the path length from p1 to p2 in the s ememe hi-
erarchy structure.
For concept S1 and S2 which they have only one se-
meme in Hownet, semantics distance d1(S1,S2) is called
the first basic sememe; except from the first basic se-
meme expression, for concept S1 and S2 which their se-
mantics in Hownet is a set of basic sememes, d2(S1,S2) is
defined as this part’s semantic distance.
Corresponding to relation sememe description, its
value is a feature structure. Considering every feature for
the feature structure, its attribute is a relation sememe
and its value is a basic sememe or a concrete word. This
part of semantics distance for two concept S1 and S2 de-
note as d3(S1,S2).
For every feature of the above feature structure, if its
value is a set in which the element of the set is a basic
sememe or a concrete word, d4(S1,S2) can be designed to
describe the part of relation signal sememe’s semantics
distance for co nce pt S1 and S2.
Naturally, for the first basic sememe d1(S1,S2), S1(S2)
have a element-sememe p1(p2) in Hownet, then d1(S1,S2)
For the other basic sememes, if S1 includes m se-
memes, S2 includes n sememes, then
D2(S1,S2)= (2)
[( ,)],0,0
avg dppmn
where p1i is the sememe of S1, p2j is the sememe of S2.
The following is a java program for calculating rela-
tion sememe:
private double disMap(
Map<String, List<String> map1,
Map<String, List<String> map2)
{ if (map1.isEmpty() || map2.isEmpty())
Math.abs(map1.keySet().size() - map2.keySet().size());
double min = DEFAULT_DISTANCE;
for (String key : map1.keySet( )) {
if (map2.containsKey(key)) {
List<String> list1 = map1.get(key);
List<String> list2 = map2.get(key);
double sim = disPriList(list1, list2);
if (sim < min) {
min = sim;
return min;
Similarly, we can also get the java program for calcu-
lating relation signal sememe’s semantics distance.
Considering the above-mentioned factors, for two
concepts S1 and S2, semantics distance is defined as
d(S1,S2)= (3)
(, )
where i
4) is adjustable parameter and
=1, 1
; if di=0, then
will assign other item proportionally. The act for
global similarity from d1 to d4 is descending order. Since
the first basic sememe expression reflects the main fea-
ture for concept, its weigh value should be defined com-
paratively bigger and larger than 0.5 usually.
Based on semantics distance between Chinese con-
cepts, we can calculate semantics distance between two
sentences w1 and w2 for Chinese SORL [16], where w1
contains m concepts (S11, …, S1m), w2 has n concepts
(S21, …, S2n).
If w1 is context-unaware and S1i is unknown, then
Dis(w1, w2)=min Dis(S1i, S2j), 1im, 1jn.
If w1 is context-aware and S1i is definite, then Dis(w1,
w2)=min Dis(S_{1i}, S_{2j}), 1jn.
Similarity measurement between two ontologies will
be calculated based on the above parts according to
weight value synthetically. The relation between ontol-
ogy similarity measurement and connecting ontologies
can be induced as follows: firstly the extracting operation
for ontologies is processed to adopt limited candidate
ontologies; then calculating ontology similarity among
ontologies will be run in order to choose th e most similar
ontologies for matching.
On the basis of studying in this section, we have de-
signed Chinese semantics distance measurer and matcher
for software requirements semantics matching measure-
ment on connecting ontologies to build a measurement
ground for connecting ontologies generating.
2.4.2 Integrating Environment
This section presents the design of interoperability ex-
tending integrating environment for requirements seman-
tic based CO in Figure 5. Applying sub-ontology ex-
tracting algorithm, DPO can be generated from require-
ments asset that has been produced by domain modeling
tool in the phase of requirements elicitation. DPO and
domain requirements asset together become reusable
asset for requirements acquiring and modeling tool.
Within the requirements acquiring and modeling tool,
semantic matcher, which can execute matching operation
with semantic distance measurement tool to achieve the
matching for role, goal, and process of requirements as-
set, will be added. Main functions of semantic distance
measurer include: measure semantic distance between
two concepts; measure semantic distance between two
ontologies; measure semantic distance between two ser-
vices. Existing basis is: 1 ) thesaurus: WordNet (English),
HowNet (Chinese); 2) similarity calculating based on
two thesaurus.
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability
Figure 5. Requirements semantics interoperability extend-
ing integrating environment based-on CO
Adopted approach is: calculating two concepts simi-
larity from words similarity; calculating ontologies simi-
larity based on concepts similarity; calculating services
similarity based on concepts similarity.
To generate CO, the function of CO generator is
driven and conducted by the control structure of RSO,
and it will use semantic matcher and interoperability
level evaluator. It can automatically co mplete th e task for
looking up reusable resources with CO generating algo-
rithm purposed in the above part to the more extent.
After received CO, interoperability level evaluator,
which will evaluate semantic interoperability level, able
to decide the preference grade for candidate services and
forecast the QoE of users.
We have designed and implemented a series of tools
for supporting service identifying and composition based
on CO and DPO. Relative prototype and validation of the
proposed approach have also partly achieved. Experi-
ment has demonstrated that the proposed approach is
useful for service finding and integrating. The snapshot
of primary tools and Prototype system for context of traf-
fic travel problem domain can see from Figure 6.
3. Related Work
Application of ontology in RE starts from domain engi-
neering. As reusable core resources in product line, do-
main requirements [17] mainly solve requirements mod-
eling issue for component- oriented software system.
Dr. Jerome Euzenat from INRIA Grenoble Rhone-
Alpes in France has studied semantic interoperability
issues based ontology mapping [18,19] and acts as prin-
cipal in NeOn project of EU FP6 plan. In June 2008, In-
Figure 6. Prototype context and tools of traffic travel prob-
lem domain
formatics of EU startup semantic interoperability central
plan for Europe and set up first session in Brussels aim-
ing at realizing semantic data interoperability for E-gov-
ernment in Europe. Open source SILIME project of MIT-
Semantic Interoperability of Metadata and Information in
unLike Environments attempts to semantic interoperabil-
ity for data resources (such as data library).
The studying of connecting ontologies is new direc-
tion in the world. Initial investigation studies original
domain-level ontology for heterogeneity and explores
how to create new ontology for covering original on-
tology with collaboration and consistence, and also
containing ontology grouping technology (for example
ontology mapping, ontology aligning, ontology merging
etc.). In 2007, the paper by Shuaib Karim [20] pre-
sented a CO application framework that need not cover
original ontology and focus on studying transfer princi-
ple and intermediate concept among original ontologies.
Cregan Anne [21] proposes to build semantic interop-
erability by CO and gives some CO examples of gene
ontology in 2008. However, in Cregan Anne’s paper,
connecting manners of CO, incentive of connecting,
method and critical content of building semantic inter-
operability are absent. We also notice that Linked Open
Data [22] initiative has become the existing foundation
for federal Web of Data.
Now, together with CO and RE, the investigation of
Copyright © 2010 SciRes JSEA
Building Requirements Semantics for Networked Software Inter o perability
Copyright © 2010 SciRes JSEA
requirements semantic interoperability extending for
networked software with respect to service-oriented
computing just begins to proceed, and a great deal of
theoretical and technological issues will require to solve.
4. Conclusions
This paper explores ontology-based RE, for interopera-
bility extending of requirements semantics; we present
CO approach to improve requirements modeling under
the condition of distributed services aggregation with
loosely coupling and different domain. Some formal
definition and generating algorithm of CO are given.
With the novel approach, a integrating environment and
measurement system based on CO is designed and im-
Further work can be classified as follows: studying par-
tial meaning of semantic interoperability for networked
software requirements; build CO based on Linked Open
Data infrastructure; empirical testing for integrating envi-
ronment with multi-domain, such as financial risk assess-
ment, environment protecti on and so on.
5. Acknowledgments
This research has been partly supported by the National
Basic Research Program of China (Grant No. 2007CB-
310801) and the National Natural Science Foundation of
China under Grant No.60970017 and 60903034.
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