=Paper=
{{Paper
|id=Vol-344/paper-6
|storemode=property
|title=Towards an Ontology-enabled Approach for Modeling the Process of Conformity Checking in Construction
|pdfUrl=https://ceur-ws.org/Vol-344/paper6.pdf
|volume=Vol-344
|authors=Anastasiya Yurchyshyna,Catherine Faron-Zucker,Nhan Le Thanh,Alain Zarli
|dblpUrl=https://dblp.org/rec/conf/caise/YurchyshynaFTZ08
}}
==Towards an Ontology-enabled Approach for Modeling the Process of Conformity Checking in Construction==
https://ceur-ws.org/Vol-344/paper6.pdf
Towards an Ontology-enabled Approach for Modeling
the Process of Conformity Checking in Construction
Anastasiya Yurchyshyna1, 2 Catherine Faron-Zucker1, Nhan Le Thanh1, Alain Zarli2
1 I3S, Université de Nice Sophia-Antipolis, CNRS, 930 route des Colles, BP 145, 06903
Sophia Antipolis, France, {Catherine.Faron-Zucker, Nhan.Le-Thanh}@unice.fr
2 CSTB, 290 route des Lucioles, BP 209, 06904 Sophia Antipolis, France,
{anastasiya.yurchyshyna, alain.zarli}@cstb.fr
Abstract. This paper presents an ontological method aimed at semi-automatic
checking the conformity of a construction project represented by RDF graph
against a set of construction norms formalized as SPARQL queries. The
reasoning is modeled by the matching of RDF representations of construction
projects to SPARQL conformity queries. We integrate meta-knowledge relative
to the checking process by annotating the conformity queries themselves and
organize them according to their annotations. The queries annotations also help
to guide the information/knowledge extraction and reasoning process and
explain the results of the validation process, especially in case of failure.
Keywords: Conformity checking, knowledge extraction in construction,
organization of the base of conformity queries, Semantic Web in Construction.
1 Introduction
The execution of construction products is nowadays characterised by complex rules
and regulations. However, their current representations are still mostly paper-based
(e.g. texts with diagrams, tables) and require a human interpretation [7].
Construction projects (e.g. public buildings) are commonly represented by the
Industry Foundation Classes (IFC) model, an object oriented data model for Building
Information Modelling. There is a standard XML representation for the IFC model
(ifcXML1), which is, however, insufficient to describe the complexity of the building
information flow: the IFC model is semantically richer than any XML language.
Our research aims at the development of a conformity-checking model based on
semi-formal representations of technical norms: we study how to represent and
organise them for the specific task of effective conformity checking. Our checking
model is based on the matching of norm representations with those of construction
projects. Its efficiency is explained by the ontological representation of regulation
1
http://www.iai-international.org/IFCXML/
22 Proceedings of CAiSE’08 Forum
knowledge and the conformity-oriented annotation of norms with meta-knowledge
improving the checking process and the explaination of its results.
2 Knowledge Representation Oriented Conformity Checking
The first phase of our knowledge acquisition method aims at acquiring formal
representations of technical construction norms relative to the accessibility of
disabled persons. We use the CD REEF, the electronic encyclopaedia of construction
texts and regulations, to extract a base of accessibility constraints, which we formalise
as SPARQL queries in terms of the IFC model. This is a manual process (the
knowledge extraction from texts is out of the scope of our research) conducted in
collaboration with construction experts (mainly from CSTB) who help to explicit the
domain knowledge. As a result, we are provided with a base of SPARQL queries
expressing non conformity constraints: e.g. “The minimum width of a door is 90 cm”
is formalized by:
select ?door display xml where
{ ?door rdf:type ifc:IfcDoor
OPTIONAL { ?door ifc:overallWidth ?width
FILTER ( xsd:integer(?width) >= 90)}
FILTER (! bound( ?width) )}
The second phase aims at the semi-automatic acquisition of an ontology oriented
conformity checking. This conformity-checking ontology is developed on the basis of
the concepts occurring in the acquired conformity queries. Primitive IFC concepts are
extracted from the ifcXML schema - solely those occurring in the conformity queries;
they are organized into an OWL Lite ontology based on the schema structure. The
conformity queries also make use of some non-IFC concepts. To integrate them in the
ontology, the intervention of a domain expert is necessary whose task is to define
these concepts with primitive IFC concepts. These definitions are represented by RDF
graphs (e.g. GroundFloor is a subclass of IfcBuildingStorey defined as an
IfcBuildingStorey situated on the level of entering into a building: the value of
property pset_BuildingStoreyCommon_EntranceLevel is TRUE).
The third phase of our method consists in the annotation of the conformity queries
themselves for effective checking. We associate them supplementary information,
which is helpful in the conformity checking process: e.g. information on the
regulation corpus from which queries are extracted. We automatically extract RDF
annotations of conformity queries from the CD REEF, which contains information
relative to regulations (in addition to the regulation itself): (i) characteristics of the
regulation: type of regulation text (e.g. Construction Code), level of application (e.g.
national); (ii) application domain (e.g. accessibility); (iii) destination of a building
(e.g. public administration building). The acquired RDF annotations are later
manually enriched by domain knowledge: (i) subject (e.g. entrance door); (ii)
construction common knowledge (obvious for domain experts: e.g. a hotel is a public
building, not a private house), etc.
The last phase is dedicated to the acquisition of a construction project
representation oriented conformity checking. Such representations are developed on
Proceedings of CAiSE’08 Forum 23
the basis of the initial IFC representation and guided by the acquired conformity-
checking ontology. First, we develop an XSLT stylesheet that filters the ifcXML
description of a construction project, transforms only the data relative to the
conformity checking ontology and finally builds an RDF graph representing the
project. This RDF representation may be further enriched with some non-IFC
concepts defined in the conformity-checking ontology (in second phase of our
method) in case their definitions appear as subgraph as the RDF graph representing
the project.
4 Conformity Checking Model
We adopt an ontological approach and the semantic web technologies [2] to develop
our reasoning model [7]. It is based on graph-based formalisms for knowledge
representation, which have declarative semantics, are logically founded, allow the
structured representation of knowledge and describe it at the different levels (e.g.
ontological and asserted knowledge). The basic reasoning operation for a query-
answer system is graph projection, formally defined as a labelled homomorphism
between graphs [3]. The reasoning thus consists in graph homomorphisms [1] [5] and
modelling of the checking process is close to the process of validation of knowledge
bases [6]. The elementary reasoning mechanism of our model is the matching of a
construction project representation with representations of conformity queries. We
check the negative constraint (e.g. “the width of the door is less than 90cm”): if such
matching is found for some elements, these elements cause the non-conformity of the
project.
Conformity queries are automatically classified and organized into a query base
by parsing their RDF annotations. The classification is done according to (i) external
information characterizing the query (e.g. regulation text); (ii) specialization-
generalization relations, which could be found in the graph patterns of queries.
By organizing the queries, we define the optimal scheduling of matching
procedures as a set of explicit expert rules. The expert reasoning is represented by the
query scheduling: (i) according to priorities holding between classes of queries (e.g.
queries extracted from acts are prior to circular ones); (ii) according to knowledge
specification: inside the same query class, queries representing more specialised
knowledge are treated in priority (e.g. an entrance door query is prior to a door query,
because if a construction project is non conform to the first one, it will be
automatically non conform to the second one); (iii) according to query annotations:
priority is given to the queries with most specific annotations.
The results of the checking process (validation/non-validation, explanation of non-
validation, no answer) are analysed to generate a structured conformity report
grouping conformity queries by classes. It is automatically generated on the basis of
annotations of classified queries. The conformity report lists queries that have failed
(i) because of non-matching; ii) queries which graph pattern is more general in
comparison to the ones previously that failed, (iii) queries which annotation
representing the condition of its application is more general in comparison to the
annotation of another failing query. Another possible reason of failure of the project
24 Proceedings of CAiSE’08 Forum
validation is that the representation of the construction project does not contain
sufficient information for matching. In case of such incomplete representations, it is
useful to precise the lacking elements (the sub patterns of the query which can not be
matched), so that a user could know the reason of non-verifiability and/or complete
the representation of the project.
5 Conclusion and Perspectives
We have presented the ontology-enabled model for the conformity checking process
of a construction project against conformity norms, based on matching of an RDF
representation of a project to a SPARQL conformity query. Conformity queries are
annotated and organized to improve the checking process and help in the
interpretation of checking results in terms of conformity in construction.
For validation of our conformity-checking approach, we develop the C3R2 system,
which relies on the CORESE [4] semantic engine that answers SPARQL queries
asked against an RDF/OWL Lite knowledge base.
Ongoing works focus on the incremental development of the C3R prototype and
its evaluation by domain experts.
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