Ontology-based Information Extraction from Tourism Web ... - Neries

farmpaintlickInternet και Εφαρμογές Web

21 Οκτ 2013 (πριν από 4 χρόνια και 22 μέρες)

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FAW



Inst. für Anwendungsorientierte Wissensverarbeitung

Earthquake Engineering

Workshop in
eScience

Applications for Seismology

March 7
-
9 2011, Edinburgh

On finding Links between

Information Systems
and Knowledge
Based Systems

in Civil Engineering
and Seismology / Earthquake
Engineering

a.Univ.
-
Prof. Dr. Josef Küng

2

Facts and Figures

FAW

About the Institute


History

-

1990 founded as a research institute

-

1991 first year in
Hagenberg

-

1997 regularly institute of JKU

-

2005 foundation of FAW
-
GmbH


-

2005 EU
-
FP6
-
Project SAFEPIPES

-

2008 EU
-
FP7
-
Project IRIS

-

2010 EU
-
FP7
-
Project NERA



Team
(FAW
-
Institut
)

-

currently 15 persons in research and development



R&D

-

more than 100 successful finished projects and co
-
operations

-

among others currently we are coordinating (together with Dr. Wenzel, VCE)


the large EU
-
FP7 project IRIS (Integrated European Industrial Risk Reduction System)

(c) FAW


Johannes Kepler Universität

|

Information and Knowledge

3

Information

FAW

Current Research Domains


Information Modeling


Adaptive modeling tool


Modeling dynamic aspects of processes



Information
-
Integration


Semantic data integration (in the grid)



Datawarehouses


Loading Processes (e.g. automatic regression tests)



Information
-
Extraction


Intelligent (semantic and rule based) extraction


of structured information out of unstructured web pages




(c) FAW


Johannes Kepler Universität

|

4

Knowledge


Semantic Technologies, Ontologies



Using Topic Maps and Ontologies to support


queries and decisions



Ontology Enineering



Case Based Reasoning


Similarity queries in Case Based Reasoning



Application of Case Based Reasoning


Structural Health Monitoring




Application of Case Based Reasoning


in passive and active Decision Support








(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Domains

5


our famous example: tiscover
[1]

FAW

Past Research Work

Introduction



Web Based Destination
-
Management
-
System



Access to complete and up
-
to
-
date information about Tourism Holiday Destinations



Booking Functions



System Provider:

Tiscover

AG Innsbruck



Development:

FAW
-
Hagenberg

Tiscover

AG
Hagenberg




(c) FAW


Johannes Kepler Universität

|

6


our famous example: tiscover
[2]

FAW

Past Research Work

tiscover is more than a web page





(c) FAW


Johannes Kepler Universität

|

Public Terminal

(
AccessPoint
)

Reservation
&

CallCenter

Customized

Booking Engine

Internet

home/office

7


ad Information: AMMI

[1]

Meta Modeling Tool
(
A
daptive
M
odeling tool for
M
eta models and it
I
nstances)




(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work

8


ad Information: AMMI

[2]

Instance Modeling View



(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work

9


ad Information: AMMI

[3]

Administration Module




(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work

10


ad Knowledge: EU
-
Project IRIS
[1]

FAW

Current Research Work

Introduction



IRIS


Integrated European Industrial Risk Reduction System


Oct. 2008


Mar 2012, about 40 Partners, mainly form civil engineering domain,

4 partners from IT
-
Domain, one associated partner form Japan (University of Tokyo ) and US (Drexel University,
Stanford University)


Motivation


Within Current practices in risk assessment and management for industrial systems are characterized by its
methodical diversity and fragmented approaches. Integration is needed.


The large collaborative project IRIS is proposed to identify, quantify and mitigate existing and emerging risks to create
societal cost
-
benefits, to increase industrial safety and to reduce impact on human health and environment.



Basic Concept


The basic concept is to focus on diverse industrial sector’s main safety problems as well as to transform its specific
requirements into integrated and knowledge
-
based safety technologies, standards and services.



WP7: Monitoring, Assessment, Early Warning, Decision Support


FAW has its main task in this work package


setting up the decision support system.



(c) FAW


Johannes Kepler Universität

|

11


ad Knowledge: EU
-
Project IRIS
[2]

FAW

Current Research Work

(c) FAW


Johannes Kepler Universität

|

General

Structure

12

Overall Goal


Find the early warning point


(c) FAW


Johannes Kepler Universität

|


ad Knowledge: EU
-
Project IRIS
[2]

FAW

Current Research Work

13

Decision Support System



Passive Decision Support


Providing the right information at the right time to the decision maker

in order to support him/her.

(i.e. via Data Warehouses or via good organized (good accessible/searchable) document bases



Active Decision Support


A system, that uses some AI (Artificial Intelligence) methods

to elaborate a proposal to the decision maker or to do a decision autonomously.



(data mining, neural networks, support vector machines, decision trees, case based reasoning, ... )


-
> Within IRIS we work in both directions


Active Decision Support
-
> Case Based Reasoning


Passive Decision Support
-
> Semantic Networks




(c) FAW


Johannes Kepler Universität

|


ad Knowledge: EU
-
Project IRIS
[3]

FAW

Current Research Work

14

Active Decision Support System



Case
-
based Decision Support

(Example: Assessment of Simple Structures (Lamp Posts)



Data


Design (Type, Height, Material, ... )


Measurement (Set of selected eigenfrequencies ,

vibration measured after a stimulation)


Visual Inspection (Condition of post and stand,

Scratches, oxidation, condition of concrete)



Task


Classification of lamp post’s condition




(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work


ad Knowledge: EU
-
Project IRIS
[4]

15

Active Decision Support System



Results


Currently case base consists of 800 measurements of different lamp posts


Above 90% “correct” classifications


Improvement of results:


End
-
user can adjust parameters (attribute weights, predefined distances)


results are improving


Identify and exclude “unrepresentative cases” (where connection (parameter values


classification result) is
irreproducible)


In some ways the inspection process could be adapted (e.g. less “free
-
text” attributes)




In contrast to complex structures like e.g. bridges, an automated assessment of more
simple structures, as lamp posts are, looks very promising

(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work


ad Knowledge: EU
-
Project IRIS
[5]

16

Passive Decision Support System



Combining Semantic Nets and Search Engines
[1]


(Example: VCDECIS)



This system builds a

basic level of a wide scoped

passive Decision Support System



Organization/management

of an institution‘s content (documents)

to enable easier retrieval of knowledge

(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work


ad Knowledge: EU
-
Project IRIS
[6]

17

(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work


ad Knowledge: EU
-
Project IRIS
[7]

Passive Decision Support



Combining Semantic Nets and Search Engines
[2]


(Example: VCEDEIS )



Components


Search engine


Topic Map (3 layer), currently transferred to OWL


Web Portal


Document upload platform


Topic Map navigator incl. full
-
text search













Content
Topics

Topics

Content

18

(c) FAW


Johannes Kepler Universität

|

FAW

Current Research Work


ad Knowledge: EU
-
Project IRIS
[8]


Decentralized Approach



Each group can operate its own

Knowledge Base (KB) and

Decision Support Systems


IRIS Knowledge Base provides interface to partner KBs


Web Portal to access and administrate IRIS KB


Decision support (data assessment)

mainly relies on local measurement data

and on local background information (KB)


OWL will be the language

Knowledge Representation

(at higher level)












IRIS Ontology Landscape

IT
-
Framework, Current Big Picture

FAW

EU
-
FP7
-
Project IRIS

19

|

(c) FAW


Johannes Kepler Universität

CBR
-
Cycle
(Aamodt&Plaza1994):



Case Base
: General knowledge (knowledge
base, e.g. models, reports, rules …) and
already known cases



Retrieve
: Search


Retrieve the most similar case or cases


Reuse
: Adaptation


Reuse the information and knowledge
in that case to solve the problem


Revise
: Verification


Revise the proposed solution


Retain
: Learn


Retain the parts of this experience
likely to be useful for future problem
solving


Case Based Reasoning in General

Case Based Decision Support
[1]

FAW

EU
-
FP7
-
Project IRIS

20

|

(c) FAW


Johannes Kepler Universität

CBR for IRIS



Adopted to IRIS
-
Demands



More flexible


(to be used in different Domains)


Our new CBR
-
Framework for IRIS

Case Based Decision Support
[1]

FAW

EU
-
FP7
-
Project IRIS

21

|

(c) FAW


Johannes Kepler Universität

General Statements on Cloud Computing

Classical Computing



Buy & Own
:


Hardware, System Software,


Applications


(often to meet peak needs)


5


Install, Configure,

Test, Verify, Evaluate




Manage
:



. . .



Finally, use it



€€€€€

-

high Cost

Cloud Computing



Subscribe




Use












-

pay for what you use,



based on
QoS

(Quality of Service)

every 18 Month?

Long Term Vision ‘The IRIS Cloud’
[1]

FAW

EU
-
FP7
-
Project IRIS

22

|

(c) FAW


Johannes Kepler Universität

General Statements on Cloud Computing

Definition
1


A Cloud is a type of parallel and distributed system consisting of a collection of
inter
-
connected and
virtualised

computers that are
dynamically provisioned

and
presented as one or more unified computing resources based on
service
-
level
agreements

established through
negotiation

between the service provider and
consumers.


Cloud Services


Software as a Service
(e.g. Google Mail, … )


Platform as a Service
(e.g. Google App Engine, Microsoft Azure, … )


Infrastructure as a Service
(e.g. Amazon.com, … )


Ownership and Exposure


Public/Internet Clouds
(3
rd

party Cloud Infrastructure and services, available on subscription basis)


Private/Enterprise Clouds
(Cloud runs within a company’s data center, for internal and/or partners use)


Hybrid/Mixed Clouds
(mixed usage of private and public clouds)




1

Rajkumar

Buyya
, Cloud Computing and Distributed Systems
(CLOUDS) Lab, Dept. of Computer Science and Software Engineering,
The University of Melbourne, Australia


Long Term Vision ‘The IRIS Cloud’
[2]

FAW

EU
-
FP7
-
Project IRIS

23

|

(c) FAW


Johannes Kepler Universität

IRIS Private Cloud

Long Term Vision ‘The IRIS Cloud’
[3]

FAW

EU
-
FP7
-
Project IRIS

24

|

(c) FAW


Johannes Kepler Universität

IRIS Private Cloud and Mediator

Long Term Vision ‘The IRIS Cloud’
[4]

FAW

EU
-
FP7
-
Project IRIS

25

|

(c) FAW


Johannes Kepler Universität

IRIS Private Cloud and Consumption

Long Term Vision ‘The IRIS Cloud’
[5]

FAW

EU
-
FP7
-
Project IRIS

26

|

(c) FAW


Johannes Kepler Universität



Decision Support (WP7)



-

State: Enhanced Case Based Reasoning Framework


is in an implementation stage


Work on Active Decision Support is promising



-

Plan:

Continue on CBR, Active Decision Support


Knowledge Base and Prototypes (Proof of Concepts)




Data / Knowledge Integration (WP6) and


Risk Informed Design (WP8)



-

State:


IRIS System Landscape is in a stable version


Work on Integration Ontologies is ‘well on track’


(e.g. Bride Ontology is almost finished)


-

Plan:

Continue on Ontologies, keep integration in mind,


(if time, think and work more on the IRIS
-
Cloud
)

State, Plan for Next Steps

FAW

EU
-
FP7
-
Project IRIS

27

|

(c) FAW


Johannes Kepler Universität