METHODOLOGY TO SUPPORT SEMANTIC RESOURCES

INTEGRATION IN THE CONSTRUCTION SECTOR

Simona Barresi, Yacine Rezgui, Farid Meziane

School of Computing, Science and Engineering,University of Salford, Salford, United Kingdom

Celson Lima

CSTB, Sophia Antipolis, France

Keywords: Ontology, Semantic Resources, Schema Matching.

Abstract: Ontologies, taxonomies, and other semantic resources, are used in a variety of sectors to facilitate

knowledge reuse and information exchange between people and applications. In recent years, the need to

access multiple semantic resources has led to the development of a variety of projects and tools aiming at

integrating existing resources. This paper describes the methodology used during the FUNSIEC project, to

develop an open infrastructure for the European Construction sector (OSIECS). This infrastructure aims

towards facilitating integration among Construction related semantic resources, providing a base for the

development of a new generation of e-services for the domain.

1 INTRODUCTION

Ontologies represent an important branch of

traditional philosophy concerned with “the set of

things whose existence is acknowledged by a

particular theory or system of thought” (Guarino et

al. 1999).

Various definitions of ontology have been

proposed; the most quoted being the one formulated

by Gruber (1994), who defined an ontology as a

formal, explicit specification of a shared

conceptualization. Therefore, an ontology defines

the basic terms and relations that form the

vocabulary of a topic area, as well as the rules for

combining these terms and relations between terms

(Neches et al. 1991).

Semantic integration of heterogeneous databases

(Partridge 2002), content-based retrieval of yellow

pages as well as product catalogues (Guarino et al

1999), and management of corporate memory

(CoMMA 2000), are just some of the areas that have

increasingly exploited the benefits deriving from the

use of Semantic Resources (SRs)

and ontology

engineering in general.

Specifically in the construction sector, the need

for improved communication and understanding

between projects' stakeholders has led to an

increased development of domain specific

ontologies and SRs.

Construction is a knowledge intensive industry

with its unique work settings and virtual

organization like modus operandi (Rezgui 2001).

This industry is heterogeneous and highly

fragmented, consisting of numerous small and

medium enterprises (SMEs) working together on

various building projects. One of the major

consequences is the difficulty in effective and

efficient communication among partners during a

building project, or between clients and suppliers of

construction products. Several initiatives have tried

to overcome this problem by developing a variety of

SRs focused on Construction related terms.

However, these initiatives tend to be country

specific and not adapted to the multi-national nature

of the sector. Also, these resources tend to be

Semantic Resource is an expression coined in the SPICE

project to refer to ontology-similar entities, such as

dictionaries, taxonomies, etc.

Barresi S., Rezgui Y., Meziane F. and Lima C. (2006).

METHODOLOGY TO SUPPORT SEMANTIC RESOURCES INTEGRATION IN THE CONSTRUCTION SECTOR.

In Proceedings of the Eighth International Conference on Enterprise Information Systems - ISAS, pages 94-101

DOI: 10.5220/0002463500940101

 SciTePress

specialized for dedicated applications or engineering

functions, e.g. product libraries.

In order to improve communication and

information exchange between the various

stakeholders during a construction project and to

enable the development of a new generation of e-

services for the sector, accessing a single semantic

resource is no longer adequate. New initiatives,

targeting the interoperability and integration of

existing Construction related SRs are therefore

needed.

This paper describes the methodology used to

develop an Open Semantic Infrastructure for the

Construction Sector (OSIECS). The purpose of

OSIECS is to map Construction domain semantic

resources between each other. This paper presents

the research carried out within the FUNSIEC

project. FUNSIEC was funded under the European

eContent programme and aimed at evaluating the

feasibility of building and maintaining OSIECS. The

FUNSIEC consortium consisted of CSTB, the

University of Salford, and UNINOVA.

2 METHODOLOGIES AND

TOOLS

As reported in the literature (Corcho et al. 2003;

Fernandez-Lopez 1999), various methodologies and

tools have been developed in the field of ontology.

Such as a variety of methodologies for building

ontologies (Aussenac-Gilles 2001; Blazquez et al.

2001; Holsapple and Joshi 2002; Kayed and Colomb

2002; Pinto et al. 1999; Pinto and Martins 2000),

methodologies for ontology reengineering (Klein

2001), methodologies for ontology learning (Kietz et

al. 2000), methodologies for ontology evaluation

(Gruninger and Fox 1995; Guarino and Welty 2000;

Kalfoglou and Robertson 1999), methodologies for

ontology evolution (Klein et al. 2002; Klein and

Fensel 2001; Noy and Klein 2002), and

methodologies and techniques for ontology

mapping, merging, and alignment (Kalfoglou and

Schorelmmer 2003; Noy and Musen 2000).

The establishment of a consensual and unified

methodology is difficult, as is suggested by the

existence of a variety of methodologies, each

developed for a specific purpose. Possible reasons

for these difficulties could be related to the lack of

maturity of the field (Fernandez-Lopez 1999) or to

the problems of adapting a unique methodology to a

variety of different applications, sector and settings.

Environments supporting the development and

management of ontologies through graphical

interfaces have also proliferated, some providing

tools for specific functions, such as integration and

merging, or reason capabilities.

The latter category includes OntoEdit (Sure et al.

2002), OilEd (Bechhofer et al. 2001), Protégé

(Grosso et al. 1999) – which support not only OIL,

but also other models such as RDF_Ontolingua

(Farquhar et al. 1996) and Ontosaurus (Lenat and

Guha 1990). Furthermore, as described in (Noy and

Musen 2000), the former category (environments

supporting ontology merging) includes OntoMorph

(

Chalupsky 2000) Prompt (Noy and Musen 2000), and

Chimaera (McGuiness et al. 2000). Other mapping

and merging related techniques and tool reported in

the literature include FCA-Merge (Stumme and

Maedche 2001), Glue and IF-Map (Kalfoglou and

Schorelmmer 2003).

In the context of FUNSIEC, schema matching

represents a fundamental operation. In fact, a

semantic infrastructure supporting integration must

inevitably deal with the problems inherent to

heterogeneous SRs, which may differ in both

structure and terminology. Through schema

matching two schemas are compared and the

mapping between elements that correspond

semantically to each other is produced (Li and

Clifton 1994; Milo and Zohar 1998; Rahm and

Bernstein 2001). However, schema matching is

considered to be a time consuming and error prone

process due to the fact that it is still predominantly

performed manually. A comprehensive taxonomy,

covering many of the existing approaches to

automatic schema matching, is proposed by Rahm

and Bernsteina (2001).

3 SEMANTIC RESOURCES IN

THE CONSTRUCTION

SECTOR

Among the multitude of SRs developed in the

Construction sector, ranging from domain

dictionaries to specialized taxonomies, some of the

most notable efforts include the BS6100, bcXML,

the ISO 12006-3, and the IFC (Industrial Foundation

Classes).

The BS6100 (Glossary of Building and Civil

Engineering terms), produced by the British

Standards Institution, is a rich and complete

glossary. It provides a comprehensive number of

synonyms per term that can contribute towards any

ontology development effort in the sector.

The bcXML (eConstruct 2001) is an XML

vocabulary developed by the eConstruct IST project

for the Construction industry. The bcXML provides

the foundation for the development of the

METHODOLOGY TO SUPPORT SEMANTIC RESOURCES INTEGRATION IN THE CONSTRUCTION SECTOR

bcBuildingDefinitions taxonomy, which can be

instantiated to create catalogue contents. Through

bcXML, eConstruct has enabled the creation of

"requirements messages" that can be interpreted by

computer applications to then find suitable products

and services that meet those requirements.

The ISO 12006-3 (ISO 2004) defines a schema

for a generic taxonomy model, which enables the

definition of concepts by means of properties, to

group concepts, and to define relationships between

concepts.

The IFC model, developed by the IAI

(International Alliance for Interoperability), has

produced a specification of data structures with the

aim of supporting the development of the ‘Building

Information Model’ where all the information about

the whole life cycle of a construction project would

be stored and shared among the actors involved.

All of the above resources, although different in

terms of formalism, scope, details and applicability,

can be used in a complementary manner. Providing

an infrastructure to map these resources helps to

overcome problems related to SRs' different

formalism and inconsistencies, and enables effective

reuse of existing Construction related SRs. This in

turn facilitates the efficient use of knowledge within

the sector and can support the implementation of e-

services for the Construction domain.

4 FUNSIEC METHODOLOGY

As previously stated, numerous methodologies for

ontology mapping, merging and alignment are

reported in the literature. Determining the most

appropriate methodology to be applied is dependent

on the nature, individual characteristics, and

applications of the domain in question. In the case of

an open semantic infrastructure, such as OSIECS,

the applied methodology has to satisfy the following

requirements: (i) Make use of already established

and recognized semantic resources. (ii) The

infrastructure should be flexible and comprehensive

enough to accommodate different business

scenarios. (iii) The infrastructure is a living system

and should allow for future expansion (including

expansion of SRs or inclusion of new SRs). (iv) The

end-user perspective and evaluation should be

considered when planning expansion.

Consequently, building upon the strength of

numerous established methodologies, a new

methodology was developed to guide the

specification of the OSIECS infrastructure.

The FUNSIEC methodology (Figure 1)

comprises of the following phases: domain scoping,

candidate semantic resources identification,

conversion and similarity detection (OSIECS

Kernel), OSIECS meta-model and model

construction, testing and validation, and

maintenance.

The following sections provide a description of

these stages. For pragmatic reasons, the conversion

and similarity detection (OSIECS Kernel) and the

OSIECS meta-model and model phases are here

discussed as a single phase.

4.1 Domain Scoping

Scoping the domains (e.g. knowledge management,

e-procurement, etc.) to be covered by OSIECS was

facilitated by the use of typical scenarios that the

infrastructure was expected to handle. The use of

scenarios facilitates the description of the domain to

be covered, how OSIECS was expected to be used,

and which type of information it was expected to

provide.

Two example scenarios are: (i) A designer

developing a CAD drawing (IFC compliant) needs

to also know the regulations to be followed in

his/her project. In this case OSIECS would provide a

link between the IFC tool and the e-COGNOS tool.

(ii) An expert looking for information on the fire

resistance of a given brick also needs to receive

information on alternative products (suppliers,

prices, etc.). OSIECS would then provide a link

between the e-COGNOS tool and the e-Construct

tool.

4.2 Semantic Resources

Identification

The results of the first stage of the methodology

aided the process of selecting the SRs to be included

in OSIECS. For instance, knowledge management

was recognized as one of the domains to be covered

by the infrastructure, E-COGNOS was therefore

included among the OSIECS components, because

Figure 1: Phases of the FUNSIEC Methodology.

ICEIS 2006 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

of its focus on construction concepts related to the

consistent knowledge representation of

(construction) knowledge items.

Existing Construction related SRs were selected

for inclusion by considering their domain and a

series of other features, such as their availability,

cost, formalism, and underlining language. The SRs

included in OSIECS are the e-Cognos ontology, the

IFC model, the bcBuildingDefinition taxonomy, and

the STABU LexiCon. The latter is a vocabulary of

terms for the Construction industry and as such an

implementation of ISO DIS 12006-3.

4.3 Conversion and Similarity

Detection - Meta-model and

Model Construction

After selecting the SRs to be included in OSIECS,

syntax related problems (data heterogeneity) were

addressed by converting each SRs’ meta-schemas

and schemas into the Web Ontology Language

(OWL). This conversion facilitated the processes of

dealing semi-automatically with semantic

heterogeneity and detecting similarities between

SRs' schemas. The conversion process produced the

"rules of conversion" from each original formalism

into OWL, which were used to create the OWL

version of SRs' meta-schemas and schemas. At this

stage, human intervention was required to identify

the formalism used in SRs, study the semantics of

the formalism, and identify syntactic elements in

OWL corresponding to the syntactic elements of the

formalism used in the SRs.

The next step in the construction of the OSIECS

meta-model and model was to detect and validate

the similarities existing among SRs' meta-schemas,

and subsequently the ones existing among the

different SRs' schemas. Two components were used

for this purpose, a Detector of Mappings and a

Validator. The Detector of Mappings used an

inference engine (FONDIL

) to compare SRs' meta-

schemas and schemas and to create lists of

equivalent or subsumed concepts. The Validator

component was then used to check the similarities

detected. The latter was a semi-automatic process,

which required the intervention of human experts to

ensure that the results of the validation process were

correct and to add new similarities if required.

FONDIL is available at

http://195.83.41.67/ondil/connect.html

4.4 Testing and Validation

The testing and validation phase was directed at

verifying the completeness of the infrastructure in

terms of the conceptualization of targeted domains,

assessing the relevance of concepts and

relationships, and verifying the consistency and

coherency of concepts. To test and validate the

OSIECS infrastructure a series of dedicated services

and scenarios were implemented.

4.5 Maintenance

The final phase of the methodology is an ongoing

process aimed at correcting and updating the open

semantic infrastructure during its working life.

Maintenance is required to eliminate errors or

deficiencies in the infrastructure and to update and

enrich the domains covered by OSIECS, through the

integration of new SRs. In order to achieve this

integration, new mappings and methods have to be

considered

5 THE OSIECS TRIAD

By the use of the FUNSIEC methodology the

OSIECS Triad was implemented, specifically the

OSIECS Kernel, the OSIECS meta-model, and the

OSIECS model. This section outlines the

architecture of the OSIECS Kernel, a semi-

automatic tool used to create both the OSIECS meta-

model and model. As mentioned, the OSIECS

Kernel covers two levels, the meta-schema and

schema levels.

The Kernel consists of the following

components: the Syntax Converter, the Semantic

Analyser, the Converter, the Detector of Mappings,

and the Validator. The operation of the OSIECS

Kernel is depicted in figure 2. The role of the experts

is to verify the results produced by the Syntactic

Converter and the Semantic Analyser, as well as to

help validate the lists produced by the FONDIL

system.

Syntax

Converter

Semantic

Analyser

Converter

Detector of Mappings (FONDIL)

Validator

OSIECS KERNEL

Experts

OSIECS

Metamodel

OSIECS

Model

Syntax

Converter

Semantic

Analyser

Converter

Detector of Mappings (FONDIL)

Validator

OSIECS KERNEL

Experts

OSIECS

Metamodel

OSIECS

Metamodel

OSIECS

Model

OSIECS

Model

Figure 2: The OSIECS Kernel.

METHODOLOGY TO SUPPORT SEMANTIC RESOURCES INTEGRATION IN THE CONSTRUCTION SECTOR

The OSIECS meta-model and model are built by

using the meta-schemas and schemas of the

previously selected Construction related SRs. The

Syntax converter and the Semantic analyser work

together, using the meta-schemas and schemas of the

four selected SRs as input, to produce the rules of

conversion. These rules are then used by the

Converter to guide the production of the OWL meta-

schemas and schemas for each of the SRs in the

Kernel. The Detector of Mappings is played by the

FONDIL system (briefly introduced below), which

works with the OWL-converted meta-

schemas/schemas to produce a list of equivalent or

subsumed entities. These entities are then analysed

and assessed by the Validator (Lima et al. 2005c).

Helped by the appropriate software tools, the

experts play an essential role in the creation of

OSIECS meta-model/model. They participate at

both levels, taking care of: (i) the manual analysis of

the SRs and their respective meta-schemas/schemas;

(ii) the analysis of the rules of conversion; (iii) the

assessment of the detection of similarities; (iv) the

inspection of the validation process; and (v) the

assessment of the final output.

5.1 The FONDIL System

The FONDIL system is responsible for the detection

of similarities among meta-schemas/schemas within

the OSIECS Kernel. In general, FONDIL provides

inference services for Description Logic-based

ontologies (Le Duc 2004). The expressiveness of

such ontologies allows formalisation of the

semantics of modelling languages (e.g UML,

EXPRESS) and makes these semantics as explicit as

possible. It is worth emphasising that formal and

explicit semantics are crucial to automated

deduction.

The FONDIL system is composed of three

modules, namely ontology management, mediator,

and inference engine. The heart of the FONDIL

system is an inference engine that uses structural

algorithms for non-standard inferences and

optimised algorithms for standard inferences Le

Duc, 2004; Lima et al. 2005b). FONDIL uses the

inference engine to deduce new knowledge, using

ontologies as the primary source of knowledge. The

knowledge deduced is essentially new relations

among the ontological concepts. FONDIL initially

considers that the ontology manager needs some

help to exploit all the possible relationships among

the concepts within a single ontology. This help is

even more necessary when considering several SRs

that were (likely) developed independently from

each other. The relationships among them (if they

exist) are usually implicitly defined. These

relationships can be viewed more as knowledge to

be detected rather than knowledge to be predefined

in the SRs. FONDIL's function within OSIECS is to

assist in the refinement of the semantic mappings

detected among the SRs (Lima et al. 2005b).

5.2 Syntactic Conversion and

Semantic Analysis

The mapping process involves three main aspects of

SRs: the structures, the syntax, and the semantics.

To solve syntax problems, the recommended

solution is to represent the original SRs in neutral

format; this can be achieved through conversion if

necessary. The converted versions are then free of

syntactical problems. Structural and semantic-related

problems are solved through a semi-automatic

process ( Lima et al. 2005a).

Before describing the process of creation of the

OSIECS meta-model/model, it is worth noting that

the meta-schemas used to form OSIECS were

originally represented in different formalisms, as

follows: EXPRESS is used in ISO 12006-3 and IFC,

and UML is used in e-COGNOS and bcXML.

The Converter works with the meta-

schemas/schemas in their original formats and

produces the corresponding OWL versions. The

experts play a very strategic role in this phase, since

they analyse the SRs’ meta-schemas/schemas and

create a set of conversion rules (written in Java) that

allows conversion of each entity from their original

format into OWL. This transformation must preserve

the semantics of the converted entities. This set of

rules is used by the JavaCC

tool, which generates

“transformers” capable of automatically translating

any meta-schema/schemas written in the original

format into OWL. During the OSIECS development,

two “transformers” were generated to support the

translations of both EXPRESS and UML to OWL.

5.3 Detection of Mappings

As previously stated, the Detector of mappings uses

the FONDIL inference engine to detect the

similarities between each pair of concepts belonging

to two different SRs. The similarity between two

concepts is defined in four levels, according to its

granularity, Let us consider two concepts C1 and C2

belonging to two meta-schemas. Firstly, the

inference engine verifies if they are equivalent

according to the OWL semantics. In case of

equivalence, this result will be sent to the Validator.

Otherwise, those concepts are sent to the

JavaCC is available at https://javacc.dev.java.net

ICEIS 2006 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

Subsumption Detection component that will check if

one concept is subsumed by the other. If these

concepts are not subsumed to each other, the

similarity between them is evaluated by the

Intersection Detection, LCS and Difference

Detection components, which will deal with

intersections, unions and differences among the

concepts. This allows a more accurate detection of

similarities between the two concepts. The

similarities between the meta-schemas must be

validated in order to produce the OSIECS meta-

model.

5.4 Matching the Entities

The similarities found in the previous stage are used

over the schemas of the SRs, following a

specialisation process. For instance, let A be an

entity from the e-COGNOS meta-schema and B an

entity from the ISO 12006-3 meta-schema. Thus,

S(A, B) represents a similarity between those

entities. This similarity is then matched to the

entities of the correspondent e-COGNOS and

LexiCon schemas, S’(a, b). All the entities matched

at the schema level of the selected SR compose the

OSIECS model

5.5 OSIECS Meta-model and Model

Basically, the OSIECS meta-model and model are

mapping tables that identify and establish the

semantic correspondence between the entities

forming the SRs. The OSIECS meta-model is the set

of tables mapping the meta-schemas of the SRs

forming OSIECS, while the OSIECS model is the

set of tables mapping the schemas of SRs forming

OSIECS. Both meta-model/model were created

during the FUNSIEC project and evaluated by the

experts involved in the creation process.

Figure 3 shows a partial view of the OSIECS model

illustrating equivalence mapping and subsumption

mappings between bcXML and e-COGNOS.

6 E-SERVICES PROVISION

Although OSIECS primary purpose is to map

construction related SRs, it also facilitates the

provision of construction related e-services. Current

software application-based collaboration requires

integration through shared semantic resources.

FUNSIEC argues that attention needs to be paid to

direct support for business transactions and

processes. This would enable organizations to

migrate their legacy/commercial application

systems, articulated around proprietary semantic

resources, to higher order interoperable applications

supporting real business processes (Rezgui and

Meziane 2005).

The OSIECS Kernel can provide e-services over

SRs for the construction sector. The initial list of

OSIECS e-services concentrates on the enhancement

of the OSIECS meta-model and model, including the

following services: (i) Automatic conversion of SRs

written in EXPRESS/UML (both meta-schemas and

schemas) to OWL. (ii) Verification of the

‘compatibility level’ of a given SR (represented in

EXPRESS/UML) regarding the OSIECS meta-

model/model. (iii) Mapping report amongst SRs

represented in OWL (Barresi et al. 2005).

Considering that OSIECS can be promoted and

adopted, at least for the experimentation level, future

e-services are to consider the creation and

publication of e-catalogues, the creation and

management of SRs (taxonomies and ontologies, in

this case), and semantic mapping amongst SRs.

7 CONCLUSION

This paper presented part of the research carried out

in the FUNSIEC project. By using the FUNSIEC

methodology experts successfully developed the

OSIECS Kernel, mapped four SRs between each

other, and created the OSIECS meta-model and the

OSIECS model. The FUNSIEC methodology is

expected to be used in the future to integrate new

SRs to the existing pool of resources already

included in OSIECS. This will require new

mappings and methods to be considered.

The FUNSIEC approach began with the

characterisation of the domains, selection of

pertinent SRs and the subsequent analysis of their

meta-schemas/schemas. The SRs selected to form

OSIECS, were converted into OWL. This process

was performed semi-automatically by experts that

extracted a set of conversion rules to feed the

JavaCC, which created the respective transformers

that were consequently used to perform the

conversion of the SRs into OWL. The converted

Figure 3: Partial view of the OSIEC model.

METHODOLOGY TO SUPPORT SEMANTIC RESOURCES INTEGRATION IN THE CONSTRUCTION SECTOR

meta-schemas were semantically compared and

mapped using the FONDIL system. The final output

was the OSIECS meta-model. The OSIECS model

was then produced using the same process.

Finally, another important aspect of the OSIECS

Kernel is its ability to support a multitude of e-

services. For the time being, the OSIECS Kernel

provides e-services targeting the enrichment of the

OSIECS meta-model/model. More services are

expected to be included in this list in the near future.

ACKNOWLEDGMENTS

The authors would like to thank the members of the

FUNSIEC consortium for their valuable

contributions to the research as well as the financial

support from the European Commission under the

IST and eContent programs.

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