Trustworthy Semantic Webs

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22 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Trustworthy Semantic Webs

Building Geospatial Semantic Webs



Dr. Bhavani Thuraisingham

The University of Texas at Dallas


October 2006


Presented at OGC Meeting, October 4, 2006


Outline



Semantic Web

-
Definition, Components, Applications


Geospatial Semantic Web

-
Definition, Components, Applications

-
Collaboration with Prof. Latifur Khan and Students: Alam
Ashraful and Ganesh Subbiah at UT Dallas


Security

-
Collaboration with Prof. Michael Gertz at UC Davis and Prof.
Elisa Bertino at Purdue U.


Directions

What is the Semantic Web?



Machine understandable web pages; Activities on the web such as
searching with little or no human intervention


Vision of Tim Berners Lee,
www.w3c.org


Applications include interoperability, web services, e
-
business


Need for Geospatial Semantic Web

Data Source A

Data Source B

Data Source C

SECURITY/ QUALITY

* Semantic Metadata
Extraction

* Decision Centric Fusion

* Geospatial data
interoperability through
web services

* Geospatial data mining

*

Tools for
Analysts

Vision for Geospatial Semantic Web

0


GML, GML Schemas

Rules/Query

Logic, Proof and Trust

T
R
U
S
T

Other

Services

GRDF, Geospatial Ontologies

Protocols

P

R

I

V

A

C

Y


0
Adapted from Tim Berners Lee’s description of the Semantic Web

GML



Standardized geospatial schemas from OGC


A set of XML schemas to encode geospatial data







Most recent version (3.1.1) also allows images to attach geographic
data using GML


Application developers from disparate geospatial domains extend
the core schemas for their applications.


Reduces non
-
interoperability problems.


GML

Geometry Schemas

Topology Schema

Coverage Schema

Coordinate Schema

GRDF



GRDF
(Geospatial Resource Description Framework)


-
Adds semantics to data

-
Loosely
-
structured (easy to freely mix with other non
-
geospatial
data)

-
Semantically extensible

ComputerScience

Building

hasExtent

(33.98111,
-
96.4011)

(33.989999,
-
96.4022)

GRDF Example (Topology Ontology)

<owl:Class rdf:ID=“Edge"></owl:Class>


<owl:Class rdf:ID=“Node"></owl:Class>


<owl:Class rdf:ID=“Face">

<rdfs:subClassOf>


<owl:Restriction>


<owl:minCardinality
rdf:datatype="http://www.w3.org/2001/XMLSchema#int"


>1</owl:minCardinality>


<owl:onProperty>


<owl:DataTypeProperty rdf:ID=“hasEdge"/>


</owl:onProperty>


</owl:Restriction>



</owl:Class>



Geospatial Ontology

Upper
-
level ontologies

Mid
-
level ontology (GRDF)

Domain ontologies

Concrete Definitions of All Relevant Geospatial Concepts

Abstract Definitions of Main Geospatial Concepts

Hydrology

ontology

Cartography

ontology

Image

ontology

Geospatial Ontology


OWL
-
S for describing Geospatial
Semantic Web Services


Developing Geospatial domain
specific Ontology using OWL
-
DL
for Geospatial Semantic Web
services


Modular, Bottom
-
up Approach


Ontology shared between the
Service Provider and Service
Requestor


Input and Output parameters of
the geospatial web services
(WSDL) mapped to the concepts in
the OWL
-
DL Ontology




OWL
-
DL Geospatial Domain Ontology (Snapshot)


OWL
-
S Geospatial Semantic Web Service

Applications: Geospatial Web Services


DAGIS (Discovery of
Annotated Geospatial
Information Services)
Semantic Web Services
framework to provide an
integrated solution for
realizing the vision of the
Geospatial Semantic Web.


A single interface to search,
retrieve and update the
Geospatial data with secured
end
-
to
-
end semantics
.




Map Result for ‘Between’ Geospatial Operator

Query Results for ‘within’ Geospatial
operator

Applications: Geospatial Data Interoperability



Current state
-
of
-
the
-
art is static or semi
-
automatic


On
-
the
-
fly “Knowledge Discovery” requires
automated

data integration techniques


Proposed solution:GML Schemas and OGC
Standards solves Syntactic Heterogeneities


Semantics Issue: Existing frameworks lack
data semantics. Solution needed!


Geospatial Data Semantics enables
Knowledge Discovery

-
Discovery of explicit knowledge is good

-
Discovery of implicit/tacit knowledge is
great

-
Logic based inferential frameworks

DAGIS Integration Scenarios

Data Mining: Ontology
-
Driven Classification

Testing Image Pixels

SVM Classifier

Region Growing

Shortest Path Tree

Ontology Driven

Rule

Mining

Classified Pixels

Graph of
Regions

Graph of Near
Neighboring
Regions

High Level Concept

Training Image Pixels

Classification: Support Vector Machine (SVM)

Classifier

Test

set
-
1

Test

set
-
2

ML

90
%

40
.
09
%

SVM
-
Linear

91
%

67
.
5
%

SVM
-
Polynomial

89
.
3
%

50
.
7
%

SVM
-
RBF

89
.
6
%

54
.
7
%

Accuracy of Various Classifiers

Framework for Geospatial Data Security

Collaboration with UC Davis (Prof. Michael Gertz) and
Purdue U. (Prof. Elisa Bertino)

Security: Semantic Access Control


Architecture

Client

D

A

G

I

S

Geospatial Semantic WS Provider

Enforcement Module

Decision

Module

Authorization

Module

Semantic
-
enabled Policy DB

Web Service Client Side

Web Service Provider Side

Directions


Much of our research has focused on extending semantic web
technologies for geospatial data interoperability


Longer term approach is to start from scratch and develop
technologies specially for geospatial data


Security, privacy, misuse detection are all important considerations