=Paper=
{{Paper
|id=Vol-1741/paos2016_paper3
|storemode=property
|title=An Automatic Database Generation and Ontology Mapping from OWL File
|pdfUrl=https://ceur-ws.org/Vol-1741/paos2016_paper3.pdf
|volume=Vol-1741
|authors=Jirapong Panawong,Taneth Ruangrajitpakorn,Marut Buranarach
|dblpUrl=https://dblp.org/rec/conf/jist/PanawongRB16
}}
==An Automatic Database Generation and Ontology Mapping from OWL File==
https://ceur-ws.org/Vol-1741/paos2016_paper3.pdf
An Automatic Database Generation and Ontology
Mapping from OWL File
Jirapong Panawong1, Taneth Ruangrajitpakorn2,3 and Marut Buranarach3
1
Faculty of Management Science, Nakhonratchasima Rajabhat University,
Nakhonratchasima, Thailand
2
Department of Computer Science, Faculty of Science and Technology,
Thammasat University, Pathum Thani, Thailand
3
Language and Semantic Technology Laboratory, National Electronics and
Computer Technology Center, Pathum Thani, Thailand
Abstract. To promote ontology application development, some of the technical
processes should be simplified with a supportive tool and an automatic method.
This work presents a method to automatically generate a database schema from
OWL file to prevent schema conflicts. Moreover, a mapping configuration can
be created to associate an ontology and the generated data schema within the
process. This method is designed to be compatible with an existing Ontology
Application Management (OAM) Framework. With the proposed method, on-
tology labels are used to name data field name in database generation. The
method allows any languages for ontology labels, but English ontology labels
are recommended in this work since table names will be understandable. From
testing, the produced mapping configuration to map ontology schema to data-
base schema worked equivalently to human in terms of correctness but much
faster in time consuming.
Keywords: Ontology application; Ontological database; Instantiation; Mapping
Support
1 Introduction
In ontology application development, developers require to excel in several fields of
expertise including knowledge engineering, semantic web technology, programing,
inference engine tool, etc. In order to promote a development of ontology-based ap-
plication, simplicity in processes is a must to help developers to reduce expertise re-
quirements. In general, we can sum up processes as ontology creation, instance prepa-
ration, ontology-instance mapping, and developing application using those data and
ontology. Each of these processes requires a good amount of time and effort to com-
plete and verify.
Nowadays, several tools to support ontology application development are availa-
ble. For ontology creation, an ontology editor such as protégé [1] and Hozo [2] are a
great help, and their output as OWL format [3] file can accordingly be used in appli-
cations. Additionally, several inference engines to be used with ontology, such as Jena
[4] and JESS [5], are public and work smoothly. However, these tools still require
practices in order to utilise them fully. In the recent date, a tool called Ontology Ap-
plication Management (OAM) tool [6] was published and open for usage. The tool
was designed to support in ontology application development in several processes for
users who are an expert in a domain but are not excel in programing and technical
concepts in semantic technology. These include 1) user-interface to map ontology and
database schema as instantiation, 2) semantic search platform without background
technical knowledge of related fields such as RDF [7], OWL and URI, and 3) recom-
mender system platform with a simple user-interface to create rule without knowledge
about inference engine syntax. The tool is a great help to a community of ontology
application development as several applications in many domains were developed
based on OAM tool, for example [8], [9] and [10].
Although OAM tool assists in many processes in development, one of the difficult
parts for expert is the ontology-instance mapping. This part though is not a system-
technical process; it is necessary and directly effect to overall performance of a se-
mantic search and recommender application. Moreover, there are many cases that an
ontology of the domain is created before data are gathered. Hence, this work aims to
find a method to generate a database from OWL file and automatically map schema
of the generated database to ontology schema. We expect that this will reduce burden
of users to design a database compatible to their ontology and to map schema of both
sources. To fill in instances, the design of database is exportable to a spreadsheet
format file expected to be familiar and friendly to non-technical users. Lastly, this
work is intentionally designed to fit compatibly with OAM tool.
2 Background
In this section, Ontology Application Management (OAM) tool is reviewed. The On-
tology Application Management (OAM) framework is an application development
platform aims to simplify creation and adoption of an ontology-based Semantic Web
application [11]. This framework is an integrated platform that supports both RDF
data publishing from databases based on domain ontology and processing of the pub-
lished data in ontology-based Semantic Web applications, i.e. semantic search and
recommender system applications. Moreover, the framework provides some reusable
and configurable data and application templates customisable for different domain
ontologies using configuration GUIs. The OAM framework introduces intermediate
layers between user application and existing Semantic Web programming and devel-
opment environment.
OAM framework uses ontology as a central structure for publishing RDF data from
database and as a means to access the published RDF data. The layers introduced by
OAM aim to hide complexity of the underlying Semantic Web data standards and
models. Fig. 1 shows a layered architecture of the OAM framework.
Fig. 1. A layered architecture of the OAM framework [11]
The framework is implemented on top of existing Semantic Web data and application
platforms that are D2RQ [12], Jena’s RDF data storage and Jena’s reasoning engine
[4]. The OAM framework uses relational database to ontology data mapping, recom-
mendation and application templates on top of these systems. The user can use the
provided management tools via web browsers (google chrome recommended) in cre-
ating and managing an ontology-based Semantic Web application. For advance usage,
users of OAM can choose to use the Java API function in application development.
Moreover, a recent upgrade of OAM included a rule management using spread-
sheet [13]. This allows the users to use their own vocabulary and spreadsheet applica-
tion in managing recommendation rules. The framework was validated to be success-
ful tools for supporting by being utilised in several ontology application projects.
3 Methodology
This work aims to create a database compatible with a given ontology file and auto-
matically generate a configuration file of mapping for OAM tool. For an overview,
there are three main processes shown in Fig. 2.
Fig. 2. An overview of processes for an automatic database generation
and ontology mapping from OWL file
3.1 Reading OWL File
Firstly, we read the inputted OWL file [3] and parse the annotations of OWL. From
OWL, we capture two kinds of ontological unit: concept and concept relation. For
step-wise, we apply an algorithm shown in Fig. 3.
Algorithm: Parsing of OWL class and property
1 while unread class ≠ 0
2 read and record concepts URI
3 if class contains property
4 if property = data_property
5 record property name and its data_type
6 if cardinality of property >1
7 mark the property for multi-value
8 if property = object_property
9 record property name and its class_constraints
10 if cardinality of property >1
11 mark the property for multi-value
Fig. 3. An algorithm to parse OWL class and property
In detail of the algorithm, parsing starts from the root class (thing class for protégé
or Any class for Hozo). Next, properties of the class are read and recorded. For prop-
erties, types of property are all the matter in further processes so object-property
(part-of) and data-property (attribute-of) are recorded separately along with their con-
straints. Once we run through all properties, we search for sibling classes and sub-
classes and keep the iteration until all classes are read.
3.2 Table Generation
Since we aim to be compatible with OAM, MySQL relational database is chosen as
our target format. With the captured classes and their relations, their existence is con-
sidered. For each class, a table is created, and their properties are generated as table
columns. A data property is considered as a field to input data in while an object
property is required to be linked to another table using a foreign key following a class
constraint
For a multi-value property, an extra table is generated for either data or object
property. Foreign key will be assigned instead of direct value. This multi-value con-
straint forms a many-to-many relation of data. A table name and column tags are gen-
erated following a label of an ontology class and relations. The generated database
table and columns along with a label of the ontology class are written into a log file to
be used in a further process. Please be noted that only own properties of a class are
considered since inherited properties from the superclass can be handled by ontology
processing in OAM. Moreover, the naming of tables and table columns can only be
done with an otology with English label. For those ontologies without English label,
number will be generated instead for identification, such as table1 and column4. An
illustration of table generation is shown in Fig. 4.
Fig. 4. Images showing ontology classes (a) and the generated
database schema associated with the ontology (b)
From examples in Fig. 4-a, we can see that the ontology (from Hozo ontology edi-
tor) contains a main class “Publication” with four properties. Let us focus on this
class first. For two data properties (indicated as A/o) of the class, two columns of
table ‘publication’ shown in Fig. 4-b are created for storing the string value for
“pub_id” and “title” relation. These database columns are generated with a data type
‘VARCHAR’ according to ontology data type ‘string’. An object property relation
(marked as P/o) to relation “data_source” is generated to another column field re-
quired for a foreign key to link to another table ‘datasource’ generated in association
with “Data_Source” class from the ontology. Last, a class “Person” in ontology is
generated into another separated table ‘person’ in database shown in Fig. 4-b. This
relation, however, is marked with multi-value (as ‘_mv’ after relation name) since its
cardinality is 1 or greater (shown as ‘1..’ in ontology editor interface in Fig. 4-a). This
requires many-to-many relation; thus, another table is specially created as ‘publica-
tion-person’. In this separated table, id of publication instance and id of person
instance are linked.
3.3 Generating a Mapping Configuration
From the log file kept information about original ontology class and its generated
table, a mapping configuration can be generated. Since a database schema is directly
created from ontology schema, we would not find a case of a conflict of schemas. For
a template of a mapping syntax, we follow OAM mapping configuration. By adding
ontology label and the name of table/table column, we gain an automatic way to gen-
erate a mapping configuration compatible with OAM. Fig. 5 shows a generated map-
ping configuration file.
Fig. 5. Some parts of a generated mapping configuration file following
examples from Fig.4
However, this method can only generate a mapping configuration following origi-
nal OWL and the generated table schema. For editing, users are asked to make change
of the mapping via OAM user interface.
4 Experiments and Discussion
To approve a potential of the proposed method, we set up two experiments. The first
one is to check an accuracy of an automatically generated mapping configuration by
comparing to a mapping done by human. The second experiment is to examine how
supportive this method give based on time consumption in mapping ontology-data.
For the first experiment, we asked a user who has experience with OAM to per-
form mapping of ontology to the generated database. The given ontology contains 61
classes, 18 object properties and 27 data properties. We then compare a mapping con-
figuration file from human and our generated mapping configuration file. In compari-
son, class mapping and relation mapping are separately counted. The comparison
result is shown in Table I.
Table I. A comparison result of mapping between human and automatic generation
Type Accuracy
Amount Percentage
Class mapping 61/61 100%
Object property mapping 17/18 94.44%
Data property mapping 27/27 100%
Sum 95/96 98.96%
From the result shown in Table I, performance of the proposed method was relia-
bly good. The only difference of the mapping result was the case of a cardinality of
property is one or more. For the case, human mapped this class constraint to the target
class and all of the subclasses while the generated mapping only gave a mapping to
the target class. However, the outputs of both would give the same result in applica-
tions since ontology processing in OAM can handle the tree spanning based on the
given OWL. Thus, the result though is different, but the both mappings are legit and
acceptable.
The second experiment was set to compare time used for mapping. Three ontolo-
gies are chosen to represent an effect based on ontology size. Details of the three on-
tologies are given in Table II.
Table II. Details of three ontologies as a size representative for time-wise experiment
Ontology Size Classes Object Properties Data Properties
Small 15 5 5
Medium 61 18 27
Big 183 82 71
In this experiment, time is counted in minutes used in only mapping process while
a fraction of minutes is considered as a minute. The mapping includes class mapping,
subclass mapping and property mapping. A person to map these ontologies is a user
who has used OAM in his 2 projects and continuously been using OAM for over 11
months. A result of time consumption in mapping between human and the proposed
method are given in Table III.
Table III. A result of time consumption in mapping
Ontology Size Human Proposed Method Time difference
Small 38 minutes 1 minutes 36 minutes
Medium 109 minutes 2 minutes 107 minutes
Big 192 minutes 5 minutes 187 minutes
From the results in Table III, we can see that time in use for mapping by human
was much higher than the automatic method. However, we notice that the time con-
suming in average per class was lower the more he mapped since the most time con-
suming period was when he started mapping by looking through and learning of on-
tology classes and database. On the other hand, the automatic method skipped the
understanding process and went directly to labels.
For further validation, those generated mapping configuration files with some data
in the database were tested in OAM semantic search application, and they worked
fine in actual usage. This proves that the generated mapping can help in a develop-
ment of an ontology application. For a summary from the experiments, the proposed
method can perform equivalently to human in terms of quality but much greater in
time consuming.
5 Conclusion
In this work, we propose a method to support ontology application development by
automatically generated database schema and the ontology-data mapping. It was de-
signed to be compatible with OAM framework to support users who may not excel in
programing and technical implementation. The method requires an OWL file from
ontology editor to generate a database schema along with a mapping configuration
file. The method is though applicable to any language of ontological label, but Eng-
lish language is recommended for easier modification in further usage. From the test-
ing, the method can yield a high quality mapping of ontology-data schema with much
less time consuming comparing to human.
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