A GRAPH-BASED TOOL FOR THE TRANSLATION OF XML

DATA TO OWL-DL ONTOLOGIES

Christophe Cruz and Christophe Nicolle

Laboratory Le2i, UMR-5158 CNRS, Université de Bourgogne, B.P. 47870, 21078, Dijon Cedex, France

Keywords: Ontology population, Ontology enrichment, OWL ontology, XML data, RDF, Semantic annotation.

Abstract: Today most of the data exchanged between information systems is done with the help of the XML syntax.

Unfortunately when these data have to be integrated, the integration becomes difficult because of the

semantics’ heterogeneity. Consequently, leading researches in the domain of database systems are moving

to semantic model in order to store data and its semantics definition. To benefit from these new systems and

technologies, and to integrate different data sources, a flexible method consists in populating an existing

OWL ontology from XML data. In paper we present such a method based on the definition of a graph which

represents rules that drive the populating process. The graph of rules facilitates the mapping definition that

consists in mapping elements from an XSD schema to the elements of the OWL schema.

1 INTRODUCTION

Ontologies are widely used to capture and organize

knowledge about a particular domain. In addition,

the definition of ontolgies is used as an index to

retrieve specific data (Garcìa, 2005), to infer new

knowledge (SWRL, 2004), to semantically annotate

multimedia data (Castano, 2007), to find out Web

Services automatically (Martin, 2007), or to match

knowledge with other knowledge for a more general

purpose.

Ontologies are aimed at representing knowledge

about a specific domain that are understandable by

both developers and computers. For this, ontologies

enumerate concepts and relations between concepts

(Guarino, 1998) and define properties, functions,

constraints and axioms (Studer, 1998). The major

issues in ontology development include ontology

representation, ontology acquisition, evaluation and

ontology maintenance (Zhou, 2007). Ontology

representation is the main issue in ontology

development because its representation has to be

understandable by computers and humans.

Consequently, an ontology representation language

should provide representation adequacy for humans

and inference efficiency for computers. Ontology

dialects based on description logic (DL) provide a

frame-based knowledge representation and profit

from the expressiveness of DL reasoning systems.

Ontology acquisition refers to the process of the

ontology creation such as concepts, relations,

individuals and axioms. From an empirical point of

view, there are two kinds of ontology modeling

processes. The first one is the ontology modeling,

which is traditionally carried out by knowledge

engineers or domain experts. Actually, these

ontologies are built by humans for humans. The

second one is in fact the point of view of the

semantic Web according to which ontologies are

built automatically by computers for computers

within sources such as dictionaries, Web documents

and database schemas. It has to be noticed that the

resulting ontologies are still understandable by

humans. As a result, ontology acquisition can benefit

significantly from ontology learning (Ding, 2002).

Ontology evaluation aims at enhancing the quality of

ontologies in order to improve the interoperability

among systems and to increase the adoption of

ontologies. Ontologies can be evaluated in different

ways (Staab, 2004) using measures such as

completeness, consistency and correctness (Gomez-

Perez, 1995). Ontology maintenance concerns the

organization, the search and the update process on

existing ontologies. The constant evolution of the

environment of ontologies makes it very important

for ontologies to be evaluated and maintained (Sure,

2002) in order to keep up with the change.

This article presents an automatic population

process from XML data to OWL ontologies, a

361

Cruz C. and Nicolle C..

A GRAPH-BASED TOOL FOR THE TRANSLATION OF XML DATA TO OWL-DL ONTOLOGIES.

DOI: 10.5220/0003629603610364

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 361-364

ISBN: 978-989-8425-80-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Table 1: Synthesis of related projects.

Paper XSLT

Automatic XSD

mapping

Automatic XML

instances

Multiple XSD

integration

OWL-DL

Mapping in

RDF

XSD2OWL and

XML2RDF

Ferdinand & al. no yes yes no yes no yes

García & al. no yes yes no no no yes

Bohring & al. yes yes yes no yes no no

Rodrigues & al. yes no yes yes yes no no

Aninic & al no no no no yes yes no

Kim & al. yes no no no yes no yes

Bedini & al no yes no no yes no no

Cruz & Nicolle no no yes yes yes yes no

process which is based on a manual mapping

between the XML schema elements and the OWL

schema elements. If the OWL schema does not

contain the required elements then the ontology has

to be enriched by the system manager. The ontology

enrichment is the activity of extending an ontology

by adding new elements (e.g. concepts, relations,

properties, axioms) (Castano, 2007). Our enrichment

process consists in annotating knowledge which is

contained in XML schemas in order to define the

ontology schema (Faatz, 2004). Some automatic

processes from ontology learning can be used but

this point is beyond the scope of this paper. The

ontology population is the activity of adding new

instances or individuals to an ontology (Castano,

2007).

2 BACKGROUND

In order to populate an ontology, it is first necessary

to define which elements in an XML document will

be processed. In addition, it is also necessary to

identify the nature of the XML element in order to

generate the individuals of the corresponding

ontology. The first step consists in defining mapping

rules which define the mapping of an XML element

to an OWL element. If the required OWL elements

do not exist in the ontology then the ontology has to

be enriched accordingly. Once the mapping rules

have been defined, the second step consists in

populating (automatically) the ontology by using

XML documents validated by the XML schema.

We extend the solution described in (Rodrigues,

2006) by mapping several XML schemas in an

existing OWL ontology (Cruz, 2008), which consists

in defining mapping rules between each XML

schema to a common existing OWL ontology. In

addition, we allow users to define partial mapping of

XML schemas in order to enrich and populate a

relevant OWL-DL ontology for a specific data

management process. Furthermore, we define the

transformation rules in RDF that allow a more

flexible and a more fine-grained rule definition in

order to allow a partial reuse of annotated XML

schemas and data type conversions. Our method

allows also the definition of advanced rules of

transformation in RDF that can be reused for other

mappings. The most important point is that rules are

represented by a graphical graph which is managed

directly by the user. This graph-based rules method

facilitates the definition of rules and the

corresponding results. Some work has been done

specifically on the translation of XML schemas into

OWL ontology (Garcìa, 2005), (Do, 2007),

(Ferdinand, 2004), (Bohring, 2005), (Rodrigues,

2006), (Anicic, 2007), (Kim, 2007), (Bedini, 2008),

(e.g. table 1)

Table 1. summarize all properties of studied

projects . “XSLT” means that the method uses an

XSL style sheet such as XML data for the

conversion process. “Automatic XSD mapping”

means that the user cannot intervene in the mapping

process between XML schema elements and the

OWL ontology. “Automatic XML instances” means

that if instances are generated by the studied method

then the process is automatic. “Multiple XSD

integration” means that the project allows users to

integrate several XML schemas to an OWL

ontology. “OWL-DL” means that the generated

ontology is a description logic ontology which

allows inference and consistency checking.

“Mapping in RDF” means that the method uses RDF

to specify the mapping between schemas. The last

column implies that if the value is “yes” then XML

schemas are mapped to an OWL ontology and the

instances of the XML schemas are translated in an

RDF document. It means that instances are not OWL

instances. The last row of the table presents the

properties of our method. Our method does not use

XSLT because the process is too complex to be used

with an XSLT processor (e.g. regex). The mapping

is done manually but the population of the ontology

is automatic. We also allow the user to integrate

KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development

362

Figure 1: Snapshot of the GXSD2OWL plug-in.

several XML schemas into an existing OWL-DL

ontology. In addition, in order to specify the

mapping between schemas and the ontology, we use

the RDF language in order to permit an advanced

management of mapping rules. Finally, the instances

of the ontology are obviously defined in the model

of the ontology schema.

3 THE GXSD2OWL TOOL

The principle of our solution consists in annotating

and linking the semantic level (OWL schema) and

the schematic level (XML schema). The graphical

interface used to realize this is incorporated in the

tool “protégé” from Stanford as a plug-in (e.g. fig 1)

in order to populate an existing ontology. Once the

graph of mapping rules has been defined, the

population process is automatic. The user has only to

select a list of XML documents which can be

validated by the XSD schema.

The links between XSD annotations are

“subElement” relationships which are added

automatically by the process because these

relationships already exist in the XSD schema. In

addition, links between OWL annotations are also

added automatically because these relationships

already exist in the OWL ontology.

4 CONCLUSIONS

This paper presents a flexible method to enrich and

populate an OWL ontology for the integration of

XML data. Basic mapping rules and advanced

mapping rules are defined by users and can be

reused for other conversions and populations of

ontologies. This conversion is the first part of our

work. The second part consists in improving the

process and in making some suggestions in order to

facilitate the mapping to the user.

ACKNOWLEDGEMENTS

Authors would like to thank Yoan Chabot, for its

contribution.

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