Ontology Based Semantic Modeling for Chinese Ancient Architectures

Yong LiuCollege of Computer Science

Zhejiang University;CAD & CG State Key Lab

of China, Zhejiang UniversityHangzhou,310027

cckaffe@yahoo.com.cn

Congfu XuCollege of Computer Science

Zhejiang University;CAD & CG State Key Lab

of China, Zhejiang UniversityHangzhou,310027

xucongfu@cs.zju.edu.cn

Qiong ZhangCollege of Computer Science

Zhejiang University;CAD & CG State Key Lab

of China, Zhejiang UniversityHangzhou,310027

qzhang1973@yahoo.com

Yunhe PanCollege of Computer Science

Zhejiang University;CAD & CG State Key Lab

of China, Zhejiang UniversityHangzhou,310027panyh@zju.edu.cn

Figure 1: Ancient architectures in southeast China; thetop two photos are taken from Hefang Street in Hangzhou,and the other two are taken from Xitang town, ZhejiangProvince.

Abstract

Modeling complex architectures is quite challenging.We introduce a novel intelligent system, which can gen-erate semi-style or semi-structure Chinese ancient ar-chitectures automatically. By using an ontology basedapproach to analyze the styles of different architectures,geometry primitives (e.g. point, line, triangle, etc.) areconverted into semantic architecture components (e.g.window, gate, roof, etc.) as knowledge. The followingmodeling process can be performed at different seman-tic levels, and it is appealing to users having domainknowledge. This intelligent architecture modeling sys-tem has been successfully applied in the digital heritageproject for ancient architectures in southeast China.

IntroductionChinese ancient architectures boast a long history and greatachievements. As buildings in ancient China are mostly builtby wooden material, it is quite difficult to keep them in good

Copyright c© 2006, American Association for Artificial Intelli-gence (www.aaai.org). All rights reserved.

condition through long history period. In our digital heritageproject for ancient architectures in southeast China, the orig-inal goal is to build geometrical models of endangered ar-chitectures through the help of existing modeling softwares.Two good examples are Hefang Street, Figure 1, which usedto be the downtown area of the capital of South Song dynastyfrom 1217 to 1279, and Xitang, a beautiful riverside town,consists of several blocks of residential houses. Althoughthe scale of the above two examples looks pretty small, mod-eling individual houses manually for several street blocks isstill a big burden. It is not only time demanding but also re-quires deep domain knowledge regarding a variety of com-plex house styles involved. It should be very beneficial if themodeling process can be accomplished semi-automaticallyat semantical level by using accumulated domain knowledgeextracted from those architectures directly.

To achieve this goal, we try to apply an ontology-basedapproach on the architectures modeling process. By thisway, an end user can accomplish modeling process in amuch more natural way, paying more attention to the seman-tic relations among different components instead of focusingon geometrical details.

The rest of the sections are organized as follows. Sec-tion 2 gives a brief introduction to related works on archi-tectures modeling. Section 3 presents our ontology basedsolution. In this method, Chinese ancient architectures mod-eling is considered as an ontology design process. It consistsof multi-level projection ontology design and its implemen-tation in practice etc. Section 4 gives implementation detailsof the modeling system. Experiment results and a short dis-cussion are presented in section 5.

Related WorkGenerally speaking, there are two primary categories ofmodeling technologies, i.e. manual modeling and automatedmodeling.

In manual modeling, users usually adopt commercialmodeling softwares(e.g. AutoCAD, 3D studio max, Mayaetc.), to design and render desired heritage buildings. Al-though manual modeling can generate the most accurate andcomplicated models, it is very time-consuming. A typicalmanual modeling case was given by ChiuShui Chan et al.(Chan, Tong, & Dang 2003). It is used to build the model oftraditional Chinese architectures.

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The automated modeling can generate models with onlyseveral input parameters. Yoav I. H. Parish (Parish & Muller2001) introduced a stochastic, parametric L-system to gen-erate the geometries for buildings. A set of rules are set up tocontrol the transformation, scaling, extrusion and branchingof the buildings’ geometry. Another approach is to constructnew buildings by combining the basic units of houses, suchas roof, wall, window, gate etc. (Birch et al. 2001).

Text-to-scene technique (Johansson et al. 2005; Coyne &Sproat 2001) is another automated modeling approach. Itconverts the natural language descriptions to the modelingscenes via the ontology analysis. However, the complexityof the natural language processing limits its applications inpractice.

As we can see, most of existing modeling techniques aimat professional modeling experts. In order to take advan-tage of those systems, it is assumed that users are familiarwith the common concepts of solid modeling and computergraphics. However, in reality, for most of actual modelingapplications, such as movie production, city planning, etc.,users typically only have specific domain knowledge whilelack of computer science background.

Ontology Based Semantic ApproachProblem FormulationOur approach aims to implement an architecture modelingsystem which is capable of distinguishing different elementsand styles among varieties of buildings. Besides, the sys-tem should be able to generate numerous semi-structure orsemi-style architectures based on the semantic knowledgeextracted from existing buildings. An ontology based ap-proach is presented here to achieve the goals.

The general definition of ontology was given by Gruber(T.Gruber 1993). Ontology is typically used as the specifi-cation of a representational vocabulary for a shared domainof discourse which may include definitions of classes, rela-tions, functions and other objects. The architecture model-ing problem based on ontology can be formalized as follows:

Ontology of the Architectures The ontology of the ar-chitecture is a four-tuple, C =< D,W,R, V >. Here, Dpresents the domain of architectures, e.g. the Chinese south-east architecture domain; W is the domain space, it involvesall the instants referred by the domain; V is the ontology vo-cabulary set of the architecture domain; and R can be viewedas the relation set established on the vocabulary and the do-main knowledge.

In the definition, for any w, w = Fρ(V ), F is a functionof the domain, ρ ∈ R, then w ∈ W . We indicate F as thegeneralized function.

The modeling process can be described as designing anontology for Chinese ancient architectures. It can then beused to interpret the styles and structures of the architec-tures. A proper generalized function F to generate specificinstants in the same domain also can be derived thereafter.

Reusable Knowledge In practice, there are many knowl-edge overlaps between different domains. For example,two domains, C1 =< D1,W1, R1, V1 > and C2 =<

Table 1: The Characteristics of three participants in the ar-chitectures modeling

Participant Vocabulary Relationshipmachine address, opera-

tions, RAM etc.How to operate thedevices etc.

programmer point, line,polygon etc.

logic for the draw-ing of lines, poly-gons etc.

designer house,window,gate etc.

Organization of thecomponents etc.

D2,W2, R2, V2 >, D1 �= D2, W1 ∩W2 = φ,R1 ∩ R2 �=φ,V1 ∩ V2 �= φ. In knowledge management, these over-lapped relations (R = R1 ∩ R2) can be generalized easilyand reused in more domains.

In the domain of Chinese ancient architectures, it alsocontains quite much overlapped knowledge with the domainof modern architectures, such as the combination of dif-ferent building components. Thus, by carefully differingthose knowledge and reusing them can simplify the designof modeling system and apply them to other similar model-ing systems.

Scalability To reuse the ontology of Chinese ancient ar-chitectures and extend it to other types of architectures, itis important to consider the scalability during the design ofontology. We primarily focus on two ontology expandingconditions formalized as follows:

Condition 1. For the ontology C =< D,W,R, V >, itcan be revised as a new ontology, C′ =< D′,W ′, R′, V >,by changing the knowledge set R.

IF R′ = R ∪R0, R0 �= φ and R �= R0, then W ⊂W ′.IF R′ = R−R0, R0 �= φ and R0 ⊂ R, then W ′ ⊂W .Condition 2. For the ontology C =< D,W,R, V >,

assume the vocabulary transforming function G, G : V →V ′, then the ontology C can be revised as a new ontology,C′ =< D′,W ′, R, V ′ >.

In the design of the ontology for Chinese ancient archi-tectures, the above two conditions are quite representative.Firstly, since the domain of Chinese ancient architecturescan be considered as a specialized domain of normal archi-tectures, we can reuse the knowledge in the normal architec-tures domain and add specialized knowledge just like in thecondition 1. Secondly, since architectures are typically simi-lar in combination and topology, we can use the same knowl-edge of combination and topology as described in condition2, while using different vocabularies for different styles.

Ontology DesignMulti-level Projection Ontology Design ApproachThere are three participants, i.e. machine, programmer,and architecture designer involved in ontology design.Since they have different domain knowledge, they needdifferent ontologies illustrated in Table 1 at different levels.Three participants are all represented with the same domainconcepts of architecture models, however, they have theirown vocabularies and relationships.

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Since ontology used is dependent on the type of par-ticipant, it is necessary to have a method unifying theirontology. We adopt a multi-level projection ontology de-sign approach to represent their unified ontology at the se-mantic level. Here the semantic level processing is dif-ferent from other natural language based analysis systems,e.g. the semantic translation system (Navigli, Velardi, &Gangemi 2003). In our approach, the vocabulary set isestablished at the semantic level, same as the architecturedesigners’ ontology, while the control logic of these vo-cabularies are formal. Why not using natural languagejust like the text-to-scene systems (Johansson et al. 2005;Coyne & Sproat 2001)? Using natural language to describethe complex style and topology of architectures, especiallythe Chinese ancient styles shown in Figure 1, is really night-mare to the designers and programmers, because of the in-tricate geometrical shapes and textures in architectures.

The multi-level projection ontology design method,which is similar to the collaborative ontology design ap-proach (Holsapple & Joshi 2002), is implemented as fol-lows:

Step 1: Describe each participant’s ontology in the samedomain detailedly. At this step, the ontology from ma-chine is easier to represent compared with the other two. Itonly needs to know where to store the modeling data, andwhat value the data should be in the storage. While archi-tecture designers’ ontology design is more difficult, thereare quite many semantic components and temporal/ spatialknowledge about those components. The designers shouldpresent their semantic terms (components or vocabulariesof the Chinese ancient architectures) as detailedly as possi-ble, including the geometry shape, characteristics, functionof the components, and the style of the texture, etc. Thenontology design at programmer’s level can focus on the con-version between the other two.

Step 2: Reduce the objections of participants. In this step,the reduction can be applied to find the core concepts andterms about the architectures among all the participants.

Step 3: Find the knowledge overlap of their ontologies byprojecting them at the the same level. This step processesthe ontology synthesis problem. Although those threes alldescribe the same Chinese ancient architectures domain,their ontologies lie on different implementation levels. Inthe problem of Chinese ancient architectures modeling, weproject the designers’ semantic terms and their control rela-tionships into other two levels, and induce their representa-tions in the programmer’s ontology and machine’s ontologyrespectively.

Step 4: Make consensus at the semantic level iteratively.To design a consistent and consolidate Chinese architecturesontology, even though the previous steps try to resolve theinconsistent conditions, there still will be some inconsistentor non-unified terms or relationships, and even the conceptscannot cover all the instants in the domain. So we have torevise the previous ontology iteratively until the consensusreached. As the modeling system is aimed to the semanticterms, once the conflicts occur, the ontology will be revisedto fit the ontology of designers, then the consensus is main-tained at the semantic level.

Architecture

Ancient Arch Modern Arch

Chinese Ancient Arch Indian Ancient Arch European Ancient Arch

Cr(Dr, Wr, Rr, Vr)

Ca(Da, Wa, Ra, Va)

Cc(Dc,Wc,Rc,Vc)Ci(Di,Wi,Ri,Vi) Ce(De, We, Re, Ve)

Cm(Dm, Wm, Rm, Vm)

Figure 2: Domain concept tree of the Chinese ancient archi-tectures.

Hierarchical Ontology of the Chinese Ancient Architec-tures Our ontology based design process for Chinese an-cient architectures is carried out according to the hierarchi-cal architectures category as illustrated in Figure 2. As thedirect design for the special style of architectures may bequite difficult, we begin from the root of the domain tree.That is, firstly, we only consider the core domain ontologyof all the architectures, and expand it to the specific domainontology. The children specific domain ontologies can begenerated by reusing the domain knowledge of the parentnode in the tree, and those children with the same parent cantransform each other by implementing the scalability of theontology, as given in condition 1 and condition 2.

For example, the ontology of Chinese ancient architec-tures ontology Cc(Dc,Wc, Rc, Vc) is a child of root archi-tectures ontology Cr(Dr,Wr, Rr, Vr), then we have Rr ⊂Rc ,Wr ⊂Wc, and Vr ⊂ Vc. Obviously, Chinese ancient ar-chitectures and general architectures have the same vocabu-laries, such as window, gate, and wall, etc. The combinationrelationships among those vocabularies are also partly equal.In the domain tree, if we want to design the ontology of In-dian ancient architectures or European ancient architectures,we only need to make several revisions from the ontology ofChinese ancient architectures.

Ontology Implementation The ontology implementationmeans how to apply the defined vocabularies and knowledgeto real problems. In the other way, it can be viewed as theprocess of demonstrating the practical phenomena via in-tegrating vocabularies and knowledge. In our Chinese an-cient architecture ontology, the vocabularies almost equateto the natural component categories in the architectonics.And the knowledge relationships in our ontology consist ofthree parts, the components properties, operations, and spa-tial relationships. As the vocabularies and relationships aresemantic, the programmers will transfer them into the ontol-ogy that can be recognized by the machines via the multi-level projection approach.

Generally speaking, any type of the architectures is uni-

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User specified urban map

information

Semantic componentsMatch the size of Patches

and Blocks in roadmapBuilding the models

in XML format

User specified building

styles in DTD format

StyleChecking

Wrong style

RenderingInteractive adjusting by

Ancient Architecture SemanticModeling Toolkits

Output 3D Urban ImagesRight style

City Generator

Figure 3: Implementation diagram of the semantic modeling system.

fied by its style and structure. The architecture designers’experiences show that the styles of the architectures aremore concerned with the detailed component properties, e.g.the shape of the components, textures, decorations, while thestructures of the architectures are relatively consolidated, forexample, Chinese ancient architectures in southeast can beinduced as six typical combination patterns (Liu et al. 2005).

Then the task of modeling the semi-style and semi-structure of Chinese ancient architectures will focus on de-scriptions of detailed components and their spatial relation-ships respectively.

Design and Implementation of the SemanticModeling System

The detailed system diagram of the semantic modeling pro-cess is shown in Figure 3. There are three input parametersfor the city generator, the urban maps, the semantic compo-nents and the descriptions for styles and structures of build-ings. That means users can change these input parametersfor the specific modeling targets, which results in a betterscalability of the overall modeling system.

In the first step, users are supposed to specify the urbanmap for generation, and the semantic architecture compo-nents are also specified as the input data. Then the citygenerator module will match the roadmap with the build-ings’ block size and patch size automatically and generatethe corresponding models in the description format. Thestyle checking is processed by the city generator with theguidance of user specified building style format (DTD), andonly the right style models can be forwarded to the next stepfor rending. Before outputting the final model images, thecity generator also provides an interactive toolkit (AASMT)to adjust the models.

The stage of Style Checking in Figure 3 contains a de-fault verifier. The verifier in our modeling system is basedon the roadmap and the styles of buildings. The first rule isthat those houses should be positioned beside the road andnot on the road, so the road could be regarded as the dis-junction of the houses. The styles of the houses are decided

by their components and the combination of them. And thestyles of components can be dealt with easily, because theyare relatively finite. In fact, arranging these components ismost difficult to ensure the same styles for the whole house.The house is established by the production of the recursivegrammar, then each house can be individually written as asequence of the grammar terms. By this way, the style ensur-ing work will be considered as the term grammar checking,the verifying system only needs to check the sequences gen-erated by the production engine and to match the sequenceswith the predefined style term sequences.

Ontology Vocabularies and Semantic ComponentsThe key problem in semantic modeling is the definition ofsemantic portions. The vernacular houses in southeast Chinahave similar styles in the components, so the houses can bedisassembled into several reusable components, each spe-cial house could be regarded as the assembly of these com-ponents. In our method, these components constitute theterms of the recursive production controller, which controlsthe houses’ generation. While several components of thevernacular houses, such as conjunct walls, roofs and gate-window walls, have complex constructions and differentstyles compared with the other architectures.

Based on the general classification of architectonics, theontology vocabularies of Chinese ancient architectures canbe described as an object-oriented hierarchical framework:the city (or urban) can be divided into block, street, zone,road etc. The house can be divided into several portions in-cluding roof, base and wall etc., and each of these portionscan be subdivided into some sub-portions. Each portion isinterpreted through a series of attributes, such as its bound-ary area, sub-portions, basic geometry, texture styles etc.

Domain Knowledge, Topology and Combination ofComponentsIn fact, the representation of the semantic components ofvernacular house only finishes the first step of the seman-tic modeling. Another important step for semantic modeling

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City

CityModel

Name BoundedBy CityMember

Road* Block+

Name BoundedBy Name BoundedBy BlockMember

Patch+

Name BoundedBy PatchMember

( Allotment + | BoundingWall )

Name BoundedBy (yard| house | leftsidehouse | rightsidehouse )

Name BoudedBy HouseMember

Roof + Wall

(GateWindowWall | ConjuntWall )*

Figure 4: A DTD description for the semantic modeling.

is dealing with the domain knowledge of the architectures.Here we focus on the spatial relationships which includetopology control and combination control between those se-mantic components.

In some sense, the semantic modeling system can beviewed as a kind of language application system. The se-mantic components are the basic vocabularies. And the se-mantic controls on topology and combination are the lan-guage grammars. The right vocabularies assembling ran-domly cannot present the right meaning, only those right vo-cabularies assembling under the grammars can present theright meaning. So the semantic controls on topology andcombination are quite important to present and model thevernacular houses’ styles and structures correctly.

The topology control mainly concerns the plane positionrelationships among the houses and walls. And the combina-tion control mainly concerns the compounding relationshipsamong all the semantic components defined in the previoussection.

Obviously, the description of grammars is more difficultthan the description of vocabularies. In our approach, XMLbased description and DTD based verifying technique havebeen adopted (Liu et al. 2005).

Topology As the previous section mentioned, the topolo-gies of the Chinese ancient architectures are the two dimen-sional position problems. In our approach, there are sev-eral terms, i.e., road, block, patch, allotment etc. concerningwith those topology relationships. We mainly deal with thetwo dimensional shapes when calculating topology relation-ships.

As the Figure 3 shows, the topology processing beginswith the urban map, however, there is quite little informationin it, only the road-lines and wide blank areas are crossed bythe road-lines. In the other way, the modeling system is sup-posed to support area dispatching automatically. Then the

modeling system will compare the area of semantic com-ponents with the wide blank areas in map, auto-divide theblank areas along the road-lines and fit their sizes with thesemantic components of architectures.

When depositing houses in the divided areas, there areseveral rules regarded as the domain knowledge for thehouse depositing. For example, there is a default rule thatthe main gates of the house will face to the south, which is atradition of Chinese to obtain more sunshine.

Another special component in our semantic modeling sys-tem is the road in southeast China. Normally the spare spaceoutside the patches and blocks in the semantic componentscan all be viewed as the roads. However, this condition is notalways true for all the cases, sometimes, there may be someother additional components that lie on the spare space. Andthe styles of different roads in patches may be varied. So theroads should be distinguished and marked individually.

Combination Normally, other auto-modeling systems(Wonka et al. 2003) always focus on how to generate themodels fast. While our system tries to resolve the followingtwo problems at the ontology domain knowledge level, i.e.,how to combine the semantic components and what kind ofcombination is desirable?

How to combine the semantic components? In our ap-proach, we present a recursive grammar engine to drive thearchitecture modeling system. The terms in the productionrules are constituted of those components defined at seman-tic level. In this recursive system, the texture has becomeone kind of terms that are controlled by the production sys-tem. These productions can divide the whole city into basicelements, such as houses, patches, walls, and textures etc,which can increase the modeling efficiency greatly. How-ever, those recursive grammar systems also have their com-mon weakness, that it is hard to control their generation re-sults (Actually we have to design a verifying system to en-sure and check the generation results in our previous sys-tem). So we improve the rule recursive grammar systemby adding the ratio factor to each of these rules. Then thegeneration system controls the modeling process, it has toobey both the components’ ratios and the components’ com-bination relationships, which are controlled by the recursiverules.

What kind of combination is desirable? The grammarbased generation approach is difficult to control, it is moresuitable for the automated modeling than verifying whetherthe architecture style is rational. The simple constraint rulescannot describe all the styles of complex ancient architec-tures. On the other hand, once the artists illustrate the an-cient architecture by the rule terms directly, how can weknow the style of the architecture obeys the constraint rulesin those recursive systems? So a more expressive technol-ogy for the semantic modeling is desired.

As the topology and the semantic components are all de-scribed by the XML, the DTD has been used as a verifyingprotocol. Figure 4 gives a DTD verifying grammar for thecomponents’ combination. In Figure 4 the “BoundedBy” isthe minimal contained box of the components, “+” meansthe components can be duplicated one or more times, “*”

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Figure 5: Overview of the Chinese ancient architectures.

means the components can be duplicated zero or multipletimes, “?” means the components can be duplicated zero orone time, and “|” means the components are optional. Andthe arrow means the component at the beginning of the ar-row contains those components at its end.

In our modeling system, the control rules can be ex-pressed by XML and DTD as well. The urban models mightbe multi-instances, but the style of the models is the sameone. So the DTD supported by the XML technique can pro-vide a flexible control policy to regulate the whole style andintegrity of models.

Experiment Results and DiscussionsGiven limited space, we can only show a small sample ofthe results here. Figure 5 shows the visualization results oflarge numbers of semi-structure and semi-style Chinese an-cient architectures. In this Figure, we generate about 120buildings by our automated modeling system in no less than10 seconds. Every buildings in Figure 5 are all taken on thesimilar styles and structures. More examples can be foundon our project web site (Liu et al. 2005).

This paper has presented an ontology based approach toresolve the problem of modeling semi-structure and semi-style Chinese ancient architectures. Using the multi-levelprojection ontology design approach, we design a hierar-chical architectures ontology, which can reuse the domainknowledge and enable systematic semantic modeling at mul-tiple levels.

There are several possible directions for future work.First, we would focus on the machine auto-generation ofmore specific styles of the architectures or some modernarchitectures by expanding or revising the existed Chineseancient architectures ontology. Second, using the ontologymethods to analyze the architectures styles and structuresin further step, trying to define the formal concepts of thearchitectures styles and structures, and implement them inpractice. Finally, data mining algorithms can be used to gen-erate modeling data structures and extract useful knowledgeto make our system more effective.

AcknowledgementsThis research is supported by National Natural Sci-ence Foundation Key Project of China(No.60533090 andNo.60402010), and National Basic Research Program (alsocalled 973 Program, No.2002CB312106.)

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