The Application of Ontology in Semantic Discovery for GeoData Web Service

doi:10.4236/cn.2013.53B2121

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Communications and Network, 2013, 5, 678-680

http://dx.doi.org/10.4236/cn.2013.53B2121 Published Online September 2013 (http://www.scirp.org/journal/cn)

The Application of Ontol ogy in Semantic Discovery for

GeoData Web Service

Mingwu Guo1,2

1Key Laboratory of Precise Engineering and Industry Surveying, National Administration

of Surveying, Mapping and Geoinformation, Wuhan, China

2Wuhan Geomatic Institute, Wuhan, China

Email: [email protected]

Received July 2013

ABSTRACT

GeoData Web service is an important way to achieve the integration and sharing of heterogeneous geospatial data at

present. However, due to the complexity of GeoData and no sematic supporting Webservice discovery, it is very hard

for data users to accurately find the GeoData WebService they really want. In order to make it easy for users to quickly

and accurately find the G eoData Web Service they want in semantic level, this article firstly, constructs MetaData On-

tololy, and uses MetaData Ontology to describe the related semantic information for GeoData Web Service. Then it

comes up with a new way of computing the degree of semantic similarity among concepts based on Ontology. Finally,

it realizes the automatic discovery for GeoData Web Service based on semantic matching. The experiment result shows

that the way in this article can dramatically improve the accuracy and intelligence of GeoData Web Service discovery.

Keywords: GeoData WebService; Semantic Discovery; Ontology

1. Introduction

At present, GeoData Web service is an important way to

achieve the integration and sharing of heterogeneous

geospatial data. In order to access the geospatial data

they need, GeoData users usually have to try to find the

GeoData Web Service fro m the Intern et. But us ually, it is

very difficult for GeoData users to accurately find the

GeoData Web Service they need [1]. The complexity and

diversity of GeoData itself is the one reason. Another pri-

ncipal reason is that, present GeoData Web Service main-

ly follows the OGC specif ication, such as WMS, WFC or

WCS, etc. [2]. And to find the data service can only be

achieved by the matching of keywords for the service,

rather than by semantic query. The research of semantic

description and semantic discovery for GeoData Web

Service is absent. So, this article researches the semantic

description and discovery of GeoData Web Service and

puts forwa r d a solution based on Me taData Ontology.

2. MetaData Ontology and Its Semantic

Description for GeoData Service

In order to make the computer understand GeoData se-

mantically, and to make it easy for computers and users

to find their needed geospatial data service in semantic

level, this article constructs MetaData Ontology. The Me-

taData Ontology describing geographical data service from

all aspects, in semantic level, such as the URL of the

service, the name of data service, the layer name, the data

extent of the data service, the data category, the coordi-

nate system, the project system and so on. The main

structure of the constructed MetaData Ontology is pre-

sented in Figure 1.

Partial Description of MetaData Ontology in OWL

Language as follow:

<owl:Class rdf:ID=“MetaData”/>

<owl:DatatypeProperty rdf:ID=“DataName”>

<rdfs:domain rdf:resource=“# MetaData”/>

<rdfs:range

rdf:resource=“http://www.w3.org/2001/XMLSchema#str

ing”/>

</owl:DatatypeProperty>

<owl:DatatypeProp erty rdf:ID=“Url”>

<rdfs:domain rdf:resource=“# MetaData”/>

<rdfs:range

rdf:resource=“http://www.w3.org/2001/XMLSchema#str

ing”/>

</owl:DatatypeProperty>

<owl:ObjectProp erty rdf:ID=“DataExtent”>

<rdfs:domain rdf:resource=“# MetaData”/>

<rdfs:range rdf:resource=“# CoordinateExtent”/>

</owl:ObjectProperty>

M. W. GUO

679

<owl:Class rdf:ID=“CoordinateExtent”/>

<owl:ObjectProp erty rdf:ID=“leftLowPoint”>

<rdfs:domain rdf:resource=“#CoordinateExtent”/>

<rdfs:range rdf: re s ource=“ # Point”/ >

</owl:ObjectProperty>

<owl:ObjectProp erty rdf:ID=“ProjectSystem”>

<rdfs:domain rdf:resource=“# MetaData”/>

<rdfs:range rdf:resource=“# ProjectSystem”/>

</owl:ObjectProp e r ty>

…………………………..

3. The Automatic Discovery for GeoData

Web Service Based on MetaData Ontology

3.1. The Procedure of the Automatic Disco very

for GeoData Web Service

This article puts forward the system structure (in Figure

2) of semantic description and automatic discovery for

GeoData Web Service based on MetaData Ontology. The

system structure is made up of “G eo-data layer”, “Core

layer” and “Application layer”.

In the Geo-data layer, the data provider shares their

Geo-data and makes semantic register through semantic

registration module of Geo-data Web Service in core

layer. When the data users in application layer request

data service, they can send request to core layer, where

the request can be semantically analyzed and automati-

cally matched by semantic matching module. At last, the

data users get the access method of data service that

meets their requirements. Users can bind and call the

data service in their ow n applications, thus achieving the

integration and sharing of data service.

3.2. The Computing of Semantic Similarities

between Concepts

The semantic matching is the core of automatic discov-

ery for GeoData Web Service. However, the semantic

matching between concepts is achieved by the computing

of semantic similarity between concepts. Generally, the

concepts in Ontology form a tree structure [3]. In the tree

structure, when two concepts have larger characteristic

contact, they will have larger semantic similarity. When

two concepts have larger semantic coincidence, the y will

have larger semantic similarity. When two concepts have

shorter semantic distance, they will have larger semantic

similarity. Based on these features, this article comes up

with a new hybrid model (shown below) computing se-

mantic similarity between two concepts on the basis of

conceptual characteristic, semantic contact ratio and

1 2c1 2d 12r 12

Sim(C,C) S(C,C). S(C,C). S(C,C)

αβγ

=++

semantic distance.

Figure 1. MetaData Ontology.

Figure 2. Frame Graph of Semantic Description and Automatic Discovery for GeoData Web Service based on MetaData On-

tology.

M. W. GUO

680

αβγ

++=

and

represent the degree

of characteristic similarity, the similarity of semantic

distance and the similarity of semantic contact ratio be-

tween concept C1 and concept C2 respectively.

is their weighs respectively, the appropriate of which

can be obtained by many expe ri ments .

1) The computing of the characteristic similarity

The characteristic similarity between C1and C2 is:

( )( )

( )

c12

12 12

S(C,C)

C ,C/1C,C/

a babba

αα

∩

=∩+ +−

a and b represent the descriptive set (synonym set and

characteristic set) of C1 and C2 respectively,

ab∩

means the element number of the intersection set of a

and b,

means the element number that belongs to

set a but not set b [4].

Scale factor

is determined by the depth of con-

cepts in the layer structure. The algorithm is:

( )

( )( )

( )

( )( )

,1,

depth A

depth Adepth B

depth A

depth Adepth B

depthAdepth B

≤



ΑΒ =

−>



depth(A) means the shortest distance from Concept A to

root [5].

2) The computing of the semantic distance similarity

The semantic distance similarity between C1 and C2 is:

d is the shortest distance of concept C1 and C2 in Ontol-

ogy tree, and it is a positive integer.

is an adjustable

parameter.

3) The computing of semantic coincidence similari-

ty We can suppose that: R is the root of Ontology tree.

And a and b are the two arbitrary nodes (the concept in

Ontology) in the tree. Nodes (a) is the nodal set that goes

through from a up to R. | Nodes (a) | means the element

number in nodal set. Nodes (a)



Nodes (b) means the

intersection set of Nodes (a) and Nodes (b). Nodes (a)



Nodes(b) means the union set of Nodes (a) and

Nodes (b). Then the semantic coincidence similarity be-

tween concepts a and b is [6]:

( )

() ()

,() ()

NodesaNodes b

S abNodes aNodes b

=



4. Experiment and Conclusion

To confirm the feasibility of the method on the semantic

discovery for GeoData Web service given in this article,

a demo program of semantic description and automatic

discovery for geographical data service were developed.

This demo program realized the read-write, analysis and

reasoning of Ontology with the help of the third software

Jena. Then it created many MetaData Ontology instances

and a series of common geographical Ontology by soft-

ware Protégé. A series of GeoData Web services were

registers semantically in this Demo program. Many ex-

periments show that the precision ratio of geographical

data service discovery by this demo program is much

higher than that of traditional key words search.

This article constructed MetaData Ontology to describe

Geo-data Web Service and then put forward an effective

method on the computing of semantic similarity between

concepts. A system structure of semantic description and

automatic discovery for GeoData Web Service based on

this MetaData Ontology was given and a demo program

was developed. The experiments show that, the method

in this article can dramatically improve the precision

ratio and intelligence of the discovery of geographical

data web service. So the method in this article is helpful

to promote the popularization of geographical informa-

tion application.

REFERENCES

[1] J. H. Zhao, “Study on GeoData Integration Based on On-

tology,” Hehai Univerty, Nanjing, 2008.

[2] M. Yi, “Research on GISService Based on Service-

Oriented Architecture,” East China Normal University,

Shanghai, 2008.

[3] A. Yang, “Research on Geo Web Services Based on On-

tology,” Wuhan University, Wuhan, 2005.

[4] L. Zhou, “Research of Similarity Computing and Ontol-

ogy Mapping Approaches,” Dalian Maritime University,

Dalian, 2008.

[5] J. G. Wang and F. Z. Su, “Study on Spatial Knowledge

Querying Methods Based on Ontology,” Geo-Informatic

Science, Vol. 6, 2004, pp. 93-99.

[6] H. T. Uitermark, “Ontology-Based Geographic Data Set

Integration,” Delft University of Technology, Netherlands,

2001.