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Ontology fusion in HLA-based collaborative product development
Sun, H.; Fan, W.; Shen, W.; Xiao, T.
http://www.nrc-cnrc.gc.ca/irc
Ont ology fusion in H LA-ba se d c olla bora t ive produc t de ve lopm e nt
N R C C - 5 3 2 9 2
S u n , H . ; F a n , W . ; S h e n , W . ; X i a o , T .
O c t o b e r 2 0 1 0
A version of this document is published in / Une version de ce document se trouve dans:
2010 IEEE International Conference on Systems, Man, and Cybernetics (IEEE
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Ontology Fusion in HLA-based
Collaborative Product Development
Hongbo Sun1,2, Wenhui Fan1, Weiming Shen2, Tianyuan Xiao1
1
National CIMS Engineering Research Centre, Tsinghua University Beijing, China
2
Centre for Computer-assisted Construction Technologies, National Research Council Canada London, Ontario, Canada
hongbo.sun@nrc.gc.ca, weiming.shen@nrc.gc.ca
Abstract—This paper presents a novel ontology fusion
approach which aims to establish a mutual understanding among HLA (High Level Architecture) -based distributed heterogeneous collaborative product development systems. The approach includes three steps: ontology mapping, ontology alignment and ontology merging. It adopts an axiom-based deduction ontology fusion strategy, and takes heavy weighted ontologies into consideration. It can find all the explicit and derived inter-ontology relations, and furthermore it reaches the active upper bounds of implicit equivalent inter-ontology relations searching. The proposed approach has great potential to improve the efficiency of preparation for HLA-based collaborative product development, reduce the work load for adaptive adjustment of ever-existing platforms, and enhance the applicability and flexibility of collaborative development systems.
Keywords—Collaborative product development, HLA - High
Level Architecture, ontology fusion.
I. INTRODUCTION
Typically, collaborative product development includes collaborative design, collaborative simulation and collaborative optimization. These all involve processes like CAD modeling, simulation and optimization, requiring data and information like CAD digital models, CAE analysis and optimization results. Collaborative product development is a typical distributed collaboration problem of heterogeneous systems. In the HLA (High Level Architecture) -based Collaborative Product Development, in a given simulation FED (FEDeration) files describe the exchange standard of data and information, and they are also the bridge of mutual understanding in collaborative simulation. But the construction of FOM (Federation Object Model) needs multi-disciplinary professional knowledge and technologies [1]. At the same time, ontology in knowledge engineering is the semantic basis of communication among domain entities. It can be used in automatic reasoning, knowledge representation and reuse [2]. The ontology-based approaches have been used to resolve the problem of heterogeneous data and information integration [3].
The semi-automatic construction of FOM can be considered as an ontology integration problem. Ontology integration is the consequence of ontology
heterogeneousness (syntax heterogeneousness and non-syntax heterogeneousness [3]). Ontology heterogeneousness can be classified into four layers: representation, terminology, conceptualized and the semantics layer. The representation layer contains different representation forms, and it can be resolved by formalization. In the terminology layer, different terms are adopted, that can be resolved by term mapping. In the conceptualized layer, the contents are different, and ontology theory takes effect here. In the semantics layer, because the same ontology owns different meanings according to the context, it is hard to resolve [4]. In collaborative product development, the difference in representation does not exist because participants of collaboration adopt the same ontology construction tools and language. However, because of multi-disciplinary coupled resolutions, regional distribution organizations and various participants, heterogeneousness on the terminology layer, conceptualized layer and semantics layer cannot be ignored.
Some well-known ontology integration tools include PROMPT [5], OntoMerge [6], MAFRA [7], GLUE [8] and OntoMap [9]. Most of these tools map ontologies only based on structures of conceptions and relations [3]., They are the most important elements of semantic parsing of ontology concepts and relations, but lack other ontology component-based functions, especially axiom-based mapping functions. This paper proposes an axiom-based deduction ontology fusion strategy.
Taking the formal concept analysis (FCA) method into consideration, before ontology merging, the formal background must be re-computed, and this computation is much harder in collaborative product development. Although formal concept analysis (FCA) can be successfully applied to the construction of ontology, in the procedure of ontology fusion it is not convenient to use this method.
In this paper, an axiom-based ontology fusion algorithm is introduced to support the construction of FOMs. The proposed approach has great potential for improving the efficient preparation for HLA-based collaborative development, reducing the adaptive adjustment of ever-existing platforms, while at the same
time enhancing the applicability and flexibility of collaborative product development.
II. THEORETICAL FOUNDATIONS
The feasibility of adopting ontology as the mutual understanding technology in collaborative product development is guaranteed by objective identity of collaborative product development, and the rationality is from the theory of conception lattice (Galois lattice).
A. Definitions
The concept of ontology is oriented from philosophy. It is the systematic explanation of existence, and it is used to describe the essence of things. The objective of ontology is acquiring, describing and representing knowledge in a given domain, providing a common understanding of knowledge in this domain, clarifying approved terminology, and giving clear definitions of these words and relationships on formalization models on different levels.
SOM (Simulation Object Model) in HLA federations can be recognized as definitions of federate ability and requirement ontology, and FOM can be deemed as an ontology definition of interested domains. Because every federate works in its own professional domain, when they are going to establish permanent, general and multi-objective integration systems, one of the basic problems to be resolved first in collaborative work is mutual understanding. However, the difference is that semantics cannot depend on formalization. That is to say, the problem cannot be resolved only by syntax methods.
At the same time, the mutual understanding models of collaborative product development have a common source, a given product model. Any model involved is specialized in some aspects, and also shares a common meta-data, binary stream, and any datum collaboration required is a given parse of a binary fragment. Furthermore, concepts of collaborative product development have partial order relations such as part-of or inherit-from. These partial orders have a common ancestor, the product ( . And all the products have a common ancestor, Thing; the minimum original concept ( defined under these partial orders is binary characters, and it is also the public descendants of these concepts. So the algebraic system defined in the concept set of collaborative product development and the partial order relations of these concepts is a conception lattice. This paper defines related concepts are as follows:
Definition 1: collaborative product development ontology
O C, HC, RC, HR, M, RM , A
Collaborative product development ontology O is defined as a seven tuple. C denotes a collaboration concept set of collaborative product development. HC defines a set of partial orders on concept set C . They inherit relationsamong the concepts involved. The concept sets and inherited relations, defined on that set, form a directed acyclic graph (DAG) whose source is the given model of collaborative product and whose sink is a binary fragment. RC denotes a set of non-inherited partial order relations on concept set C, and these partial order relations correspond to concept attributes. HR defines inherited relations on the partial order relation set RC. M is a series of
collaborative product meta ontology concepts that give a series of inheritable instances of RC. RM denotes a set of partial order relations under M; they describe the relations among elements in the meta ontology set, and they are also the basis for collaborative product ontology reasoning. A defines a set of axioms among the ontology concept set and meta ontology relation set and they provide the major premises of collaborative product development ontology reasoning.
Definition 2: collab u elopment ontology
fus
orative prod ct dev
ion
fuse SETO O,
( c, c O f , f , f f , f O , f O ,, O SETO , O SETO : fuse f , f , SETO O ) It is a partial order mapping from an ontology set of collaborative product development to one ontology. To any term c in the resultant ontology, it can find a corresponding term f in one ontology O which can be found in the prepared ontology set. At the same time, to use the term f there must be an equivalent term f in another ontology O of the prepared ontology set. The only results of ontology fusion can be one ontology O or null (as shown in Fig. 1).
Figure 1. Collaborative product development ontology fusion
The collaborative product development ontology fusion procedure is a mapping from a set of ontologies
O , O , … , O to one collaboration ontology O . During this process, expert instructions work as the mechanism and meta ontology MO controls the whole ontology fusion process.
The ontology fusion method introduced here includes three steps: mapping, alignment and merging.
Definition 3: co ment ontology
mapping
llaborative product develop map E O O E,
e f O
: map e, O , O f map e, O , O . O It is a partial order mapping based on vocabulary E and two ontologies. The term e E may be concept, relation or instance. The mapping may be equivalent relation, inherit relation or ownership. This mapping is not transmissible except it describes equivalent relations. So, in the common sense, this mapping does not involve more than two heterogeneous ontologies.
Definition u t e t ontology
alignment
4: collaborative prod c d velopmen align C C O O , ,
– align e, f c f: two concepts equal – align e, f c f: two concepts not equal
Collaborative product development ontology alignment function is an equivalent mapping based on the concept vocabulary C and two ontologies.
Definition 5: colla opment ontology
merging
borative product devel merge SE O,
O f , f O, O : merge f, SETO e
TO
e, e SETO
It is a partial order mapping from an ontology set of collaborative product development to one ontology. Any term e E in the resulting ontology, no matter if it is a concept, a relation or an instance, there must be a corresponding term f in one ontology of the prepared merging ontology set.
Equivalent graph is a directed graph (may be an unconnected one). It represents concept equivalent relations of concepts in an otology. The concept equivalent relations can be classified into two categories: structure equivalent and description equivalent relations. The structure equivalent relation is a group of one-to-many equivalent relations defined on the partial order relations (inheritance) of concepts or their attributes. A one-to-one equivalent relation is not included here. There are two sub categories of structure equivalent relations: inheritance equivalent relation and division equivalent relation. The description equivalent relation is a group of one-to-many equivalent relations defined on the partial order relations (ownership) between one concept and a set of its attributes, and the one-to-one equivalent relation is included here. What is more, the description equivalent relation has quantifier constraints.
Definition 6: inheritance equivalent relation
C C C … C
The inheritance equivalent relation describes a relation of a given sub concept C that can be uniquely determined by a group of ancestor concepts C , C , … , C . To a given concept C , there may be no inheritance equivalent relation concept set, or many equivalent relation concept sets.
Definition 7: division equ valent relation i
C C C … C
The division equivalent relation describes a group of complete divisions C , C , … , C of concept C . That is to say, any instance of C can find a corresponding instance of C , C , … , C . Specifically, the correspondence is not required to be unique here. To a given concept C , the division equivalent relation may not exist or exist many.
All of these equivalent relations mentioned above can be denoted by an m-in-arc with one degree (m is greater than 1), as shown in Fig. 2.
Figure 2. Equivalent relation denotation in equivalent graph
efi it e t
C . ,
D n ion 8: description equivalent r la on i
R . C R . C R C … R . C
| | | | | , 㧗
, … m
This gives the semantic equivalent relations between a concept C and a group of constrained attributes
R . C , R . C , R . C … , R . C . The constrained attribute group contains a set of attributes
R . C constrained by description logic. C denotes a given concept. R denotes the partial order relation from C to C . And represents the constraints. The constraints include quantifier constraints and numeric constraints. The universal quantifier constraint represents the relation value range and are all the instances of C ; existential quantifier constraint means that relation has at least one corresponding value in instances of concept C ; the negative constraint represents that there is no correspondence of relation in C instances and numeric constraint | | says relation owns a C instance number of , or ; is a non-zero natural number. As a matter of fact, description equivalent relations and inheritance equivalent relations are often used together, which makes descriptions equivalent relations described s a follows:
C C R . C C … R . C
In an equivalent graph, the constrained attributes are denoted by an identifier on the m-in-arc, as shown in Fig. 3.
Figure 3. Equivalent relation denotation mixed in equivalent graph
Equivalent and mutual exclusive graph is an enhanced graph G′ based on equivalent graph G with the exclusive relations added (no longer a DAG). The mutually exclusive relation between concepts C , C in collaborative product development ontology is a symmetrical relation, and any instance of C and its sub concepts will not be the instance of C and its sub concepts. The equivalent and mutual exclusive graph denotes these relations by between C and C . One mutual exclusive relation may contain another one. In that case, two ancestor concepts mutual exclusion implies descendant
concepts mutual exclusion. This mutual exclusive relation is named as a trivial mu al exclusive relation. tu
Structure graph is a graphical representation of inheritance relations HC in a collaborative product development ontology O C, HC, RC, HR, M, RM , A . It is a DAG with only one source, and it denotes the inheritance relations among concepts.
Definition 9: equivalent rel tion brida ge
CO CO
Equivalent relation bridge describes the equivalent relation between one term C O in ontology O and another term CO of a different ontology O . It presents itself in the form of a term pair (C O , CO ). An equivalent relation bridge is the main concept relation to be found in collaborative product development ontology mapping and ontology alignment.
Domain equivalent bridge axioms refer to a group of semantic equivalent relations (CO , CO ) from a concept set CO of ontology O to a concept set CO of ontology O , and they are the foundation of inferring in collaborative product development ontology mapping.
B. M atical Pro
Ordinarily, a b denotes the maximum lower bound of a, b , and a b represents the minimum upper bound of a, b , that is a b = GLB a, b , a b = LUB a, b . In a common sense, this paper uses a a … a denotes the maximum lower bound of sub-set a , a , … , a , and a a … a represents its minimum upper bound.
athem perties
Theorem 1: operations , on collaborative product
development ontolo nception e
properties as follows:
gy co lattice , hav
w: any a exist a a a
Idempotent la C, there , a a a
Commutative law: a y n a, b C , there exist a b b a, a b b a
Associative law: any C, there ex
, b c a, b, c c ist a b c a
b c a a b
Absorption law: any a, b C, there exist a a b a, a
a b a
It can be inferred from the above laws that the minimum upper bound and the maximum lower bound of any concept in collaborative product development concept set is itself. This is one of the most effective ways to align ontologies; when seeking minimum upper bound and maximum lower bound, the same operation is irrelevant to the order; the maximum lower bound of any concept with its ancestors is itself, and the minimum upper bound of any concept with its descendant is also itself.
Theorem 2: suppose , is a conception lattice of given collaborative product development ontology, is the
reverse of relation . Any , , , , there exist
z reflexivity: ; z anti-symmetry: ; ; z transitivity: z ; ; z ; ; z z ; o : ; z rank preservati n z distribution inequality: ; ty: z norm inequali
This conclusion can also draw from the above that the conception lattice of collaborative product development consists of two abelian monoids with the same basic elements.
The theorems above are an important source of basic axioms required by collaborative product development ontology reasoning.
III. ONTOLOGY FUSION ALGORITHM
As Fig. 4 shows, the main parts comprise three procedures: ontology mapping, ontology alignment and ontology merging. Ontology Alignment Input Output Ontology Mapping Ontology Merging
Figure 4. Collaborative product development ontology fusion framework
The main task of ontology mapping is to mark definite equivalent relations and mutual exclusive relations between any term pair of ontologies in a collaborative ontology set. And these relations are defined at the semantic level, and they can only be acquired by axioms (universal axioms, user defined axioms and data-type transformation axioms) or deductions from these axioms. This is different from Quick Ontology Matching (QOM, ontology mapping is part of ontology matching) which is based on literal semantic distance. This paper introduces an equivalent (mutual exclusive) graph-based domain axiom mapping algorithm.
(Semi) automatic ontology alignment is the central issue of interoperation among ontologies [10], so, as much as possible, equivalent relations among terms should be mined before the
interoperations. The main task of ontology alignment is to infer all the equivalent relations of term pairs in which two terms are from a different ontology. Because the ontology may not construct in a sufficient complete way, this procedure needs domain expert instructions to get perfect results.
Collaborative product development collaboration ontology merging is the important basis for collaborations among several ontologies. It corresponds to the procedure of negotiations to form a collaborative FOM among several SOMs in a HLA framework. This paper introduces an equivalent structure and a concept-equivalent, bridge-based collaborative product development ontology merging algorithm. The main task is to reorganize equivalent terms which are found in the procedure ontology alignment, and then form the collaboration ontology, FON (the same as FOM in HLA).
The first step of ontology fusion is ontology mapping, which creates a bridge-equivalent concept pair list and the bridge-mutual exclusive concept pair list of a given ontology set, based on domain axioms. It can be described by algorithm 1.
Algorithm 1. Ontology_mapping(O , DA)
Input: candidate mapping ontology set DA domain axiom set
Output: EC bridg ee quivalent concept pair list IC idge m
1 foreach do
br utual exclusive concept pair list O ,O in O
2 EC C O , CO //
3
find domain equivalent bridge axiom from DA IC E 4 Equivalent(Mutual_Exclusive)_Relation_Travel( ) /* xt G O 5 G Equivalent(Mutual_Exclusive)_Relation_Travel(O ) ract equivalent (mutual exclusive) graphs */
/*Simplify equivalent (mutual exclusive) graphs by deleting trivial equivalent (mutual exclusive) relations (Thing, data p equivalen trivial mutual exclusive relations and
endent co ept nodes)*/
ty e t,
6 Simplify( )
indep nc
G G
7 G Simplify(G )
/*According to C O , CO , mark C O of G , and r O in G , iteratively delete un-marked concepts of o -degree and their -out-arc */
ma k C
8 Bridge_simplify( )
zer in m
G G
9 G Bridge_simplify(G)
/*Inferring bridge equivalent relations, only equivalent graphs of G and G , G and G , are used here and all the discussions below are all ba ed upon structure equivalent relati s*/
s
10 fore ch unma ed concept C in G o
on
a rk d
11 if( e-one b d equivalent relation between ancestor conc of C n any concept of G , C ) then
on ri ge
ep a d
12 EC EC + (C , C ) / uplicate elements elim na d ts
/ d i te
13 elseif( one-one bridge equivalent relation between ancestor concepts of C and any concept of G , C , and
the attributes, constraints, partial order relations between concept and its attributes are also equal, the concepts in cons int pa also have corresponding equivalent bridg ncep .) en tra th e co ts th 14 EC EC + (C , C ) 15 end if 16 end
/* ferringIn bridg mutuae l exclusive relatio * ns /
17 foreach COI㧘COI in COI, COI , I i, j of G or
G do
18 foreach equivalent oncept o c f COI, COĨ in EC do
19 IC IC COI COĨ
//C OI|GOI is concept COI and all of its descen ent o the structur
//the same as COĨ |GOĨ .
d //according t e graph GOI of ontology OI,
20 end
21 foreach equivalent concept of COI, COĨ in EC do
22 IC IC C O COI
23 end 24 end 25 end
26 return EC, IC
This algorithm adopts a knowledge representation and top-down inferring mechanism, based on equivalent (mutual exclusive) graphs and structure graphs. Its inferring ability is determined by the completeness of the domain equivalent bridge axioms. Compared with most of mapping methods ever used, the main advantage of this algorithm is that most of the description features of heavy weight ontology are taken into consideration.And this algorithm can find all the explicit and derived bridge relations, but there are still many implicit equivalent bridge relations to be found.
The second step of ontology fusion is ontology alignment, which is developed to search implied bridge relations. After confirmation by the domain experts, these bridge relations are added into EC for further use.
Algorithm 2. Ontology_alignment( O , EC, IC, DTA)
Input: candidate mapping ontology set EC ridge equivalent concept pair list
IC bridge mutual exclusive concept pair list DTA qu a
EC
1 for O ,O O do //Extract structure graphs e iv lent data type axiom set
Outp tu : a n 2 Tra ( ) e ch i O 3 G vel G Travel(O ) 4 SCO ,SCO OC OC // Cartesian product of concept set in O and O
/* ordi to mutual e lusi idge relations SCO *
Acc ng xc ve br
simplify SCO , /
// ICOI|GOI is concept ICOI and all of its
//descendants according to the structure graph GOI of OI, //the same as ICOĨ |GOĨ .
6 RICO ,RICO
SCO , SCO IC OI ICOĨ
7 end if
/ din o e valent br e rel s simplify */
*Accor g t qui idg ation
RICO ,R CO
8 if OĨ in EC OI RICOI)then I
( ECOI, EC and EC
// ECOI|GOI is concept ECOI and all its ancestors //according to the structure graph GOI of OI, the same as // ECOĨ|G 9 RECO OĨ , RECO RIC O ,R ICO ICOI ICOĨ 10 end if
/*Inferrin e iva t e relatio s.*/
11 RMCO RE
g qu len bridg n , RMCO CO , RECO
12 foreach RECOI, RECOĨ in RECO , RECO do
13 if(data type construction is different according to data type meta class definition of meta ontology) then
//the difference of data type construction include data //ty un t nu e inc tency and data type //in pe i mb r onsis heritabl 14 RMCO e , RMCO RMCO , RMC O RECOI,RECOĨ 15 end if end 17 RUCO , RUCO Confirmed( RMCO ,RMCO ) // domain e perts rmatio
16
x confi n
19 return EC EC RUCO , RUC O , i j
18 end
This algorithm is defined on the structure graph-based knowledge representation and an attribute set comparison- based bottom-up inferring mechanism. The inferring capability is reliant on the comparison ability between attribute sets. Other than the results, most ontology alignment algorithms are term relations, the results of collaborative product development ontology alignment are only equivalent bridge relation mapping a set of term pairs in different ontologies. This paper proposes a heuristic information (attributes equal) based semi-automatic, bottom-up ontology alignment method. And because the heuristic information is involved, the algorithm risks making wrong judgments, so it needs a domain expert to confirm the candidate-equivalent bridge relations. It reaches the active upper bound of implicit equivalent bridge relations through searching, which greatly enhances the ability of collaborative product development and remarkably reduces manpower. But ontology alignment does not generate new ontologies; it only establishes a mapping set to support interoperations among ontologies, so ontology merging is used here to generate new ontology, based on the existing ones.
In a typical collaborative product development system, there always exist more than two sub-platforms. As mentioned
above, the conception lattice of collaborative product development consists of two abelian monoids. So the final collaboration ontology can be co structed by one-one integration ns.
n
Al
in tur
gorithm 3.Ontology_merging( O , EC)
Input: candidate mapping ontology set EC bridge equivalent concept pair list
Output: FON
1 foreachO in O do
//simplify structure graph of O according to bridge //equ alentiv concept pair list EC
2 G Equ valeni t tr vel(EC, a O )
3 end
4 FON G + Thing (as top concept) /*MergeG G a d n FON in turns*/
5 foreach G G o
6 let CO , O in EC
d
EC C
7 let CG= top node G
8 if( CG ,C of G
in EC )then
9 Ad dCG into bridge equiva
10 EC EC G , CG
lent concept chain of CG C
12 Add C
11 else
G
into FON as a direct child of Thing
13 end if
14 foreach nodeCG in Breadth_first_travel(G , CG do
15 if( CG ,CG in EC ) then
16 Add CG into bridge equivalent concept chain of CG
17 EC EC CG , CG
18 else
19 Add CG into FON as a brother node of G , and add all the in-arc of G
20 end if 21 end 22 end
23 return FON FON
This algorithm is defined on the equivalent structure graph- based knowledge representation and an attribute group comparison-based merging mechanism. Compared with lightweight ontology merging which is only based on structure and terms, the main advantage of this algorithm is that, when merging ontologies, the heuristic information, such as the equivalent structure graph and semantic equivalence of the attribute, is also taken into consideration. The efficiency and accuracy of ontology merging have been greatly improved.
IV. COMPARISON WITH RELATED WORK
The well-known ontology integration methods include Anchor-PROMPT, PROMPT, ONION, OntoMerge, GLUE, OntoMap, and OnMerge. Some of them are based on literal-based similarity computing methods (OnMerge, PROMPT, ONION, Anchor-PROMPT). Some of them are too simple, and weak in their description abilities (OntoMap). Some of them are instances-based merging (GLUE), others only adopt bridge
axioms. There are also some methods that only take terms and structures of lightweight ontologies into consideration [11,12]. This paper proposed an axiom-based deduction ontology fusion strategy, which employs heavy weight ontologies.
Let n denote the concept number in the ontology, and l represent the length of DA (Domain Axiom set). When integrating two ontologies, the existing algorithms’ complexities are: NOM - Naive Ontology MappingO n · log n , PROMPT O n · log n , Anchor-PROMPT O n · log n , GLUE O n and QOM - Quick Ontology Mapping O n · log n [10]. Compared with these algorithms, the complexity of our ontology mapping algorithm is O n · l , and the complexity of our ontology alignment algorithm is O n .
Although from the view of complexity analysis, this approach is not the best one. However, it can find all the explicit and derived bridge relations. What is more, it reaches the active upper bound of implicit equivalent bridge relations searching.
In HLA based collaborative product development, the most difficult issue is not to establish a collaborative platform, but to adaptively adjust ever-existing sub-platforms and to negotiate among multi-disciplinary domains. After collaboration ontology is introduced, the efficiency of preparation for HLA-based collaborative development can be greatly improved, the change of an ever-existing platform can be reduced, while, at the same time, the applicability and flexibility of collaborative product development can also be remarkably enhanced.
V. DISCUSSION AND FUTURE PLANS
As Fürst said, “The current challenge is not to design, develop and deploy domain ontologies but to define semantic correspondences among multiple ontologies covering overlapping domains.” [3] Collaborative product development needs to pay more attention to ontology operations in order to support multi-disciplinary development.
The main obstacles in ontology fusion include instance and concepts confusion, corresponding top concepts, modeling habits differences, synonyms, and coding formats [3]. Because there are no instances in collaborative product development ontology fusion, there is no need to worry about the confusion problem. And the foundation of this method is not literal semantic distance, so the problem of using similar words has no effect here. And since the data types used are defined in Meta ontology, the coding format is all the same. Because the top concept is given product model, so there is no doubt that top concept is unique. In the scope of one collaboration project, collaboration ontologies are built in the same way by the same group of people, so there are no modeling habits differences. Lastly, synonyms need experts to distinguish them, as
mentioned above. So, compared with other ontology fusion methods, the proposal method is more reliable.
The objective of this research is to establish a semantics-based environment that supports collaborative product development. There are still many challenges in this research area, including the dynamic adjustment of collaborative ontology, FON (Federation Ontology), modeling and consistency checking of initial ontologies, evolution of Meta Ontology, and so on. These will be reported in separate papers.
ACKNOWLEDGMENT
This work is supported by Chinese national high-tech research and development program (863 program, grant no. 2009AA110302) and Chinese nature science foundation (grant no. 60874066).
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