ppt - Advanced Knowledge Technologies

Language Technologies


Reality and Promise in AKT



Yorick Wilks

and Fabio Ciravegna

Department of Computer Science,

University of Sheffield

Overview

•
HLT

•
Using HLT for Knowledge Management

•
Challenges for HLT in AKT

–
Acquiring Knowledge

–
Extracting Knowledge

–
Publishing Knowledge

•
Demos

Human Language
Technology

•
Goal

–
Building systems able to process Natural
Language in its written or spoken form

•
Methodology

–
Use of Language Analysis

•
Technologies
(examples)
:

•
Information Extraction from Text

•
Human
-
computer Conversation

•
Machine Translation

•
Text Generation

HLT for KM in AKT

•
Use of HLT for
Acquiring
,
Retrieving

and
Publishing

Knowledge

•
Expected main benefits

–
Cost Reduction

–
Time needed for KM

–
Improving knowledge accessibility

•
Accessing/Diffusing/Understanding

•
Main challenges:

–
User factor

–
Integration


HLT in AKT


Knowledge acquisition retrieval publishing

Text mining


X

Information


Extraction



X


X



from Text








Classification


X


X

Summarization






X

Text Generation






X

Question





X


X

Answering






Traditional Knowledge Management

Drowning in information

Starving for Knowledge

Information Extraction
from Text

Question Answering

Text Summarization

Knowledge Management using HLT

HLT

Reports written

in natural language

•
Direct access to knowledge
when in textual format

?µ
Speed: Prompt Identification of
critical factors

?µ

Quantity: more information can
be accessed by people

?µ

Quality: only relevant
information is accessed by
people

?µ
Knowledge Sharing

University of Sheffield

Akt Challenges

•
Document classification

•
Text mining

Acquisition

Texts

Populating


with

instances

Extraction

•
Document classification

•
Information Extraction

Ontologies

•
Document Generation &
Summarisation

•
Agent Modelling

Publishing

HLT and KA in AKT

•
Use of text mining for:

–
Learning ontologies

•
taxonomies

•
Learning other relations

•
Main challenges

–
Integration of different techniques

–
Keeping track of changing knowledge

–
User factor:

•
interaction for setup and validation



Knowledge extraction

Information Extraction from Text

–
Populating ontologies with instances

•
Information Extraction from Text

–
Advantages:

•
Direct access to knowledge when in textual
format

•
Speed: Prompt Identification of critical factors

•

Quantity: more information can be accessed
by people

•

Quality: only relevant information is accessed
by people

•
Knowledge Sharing

Knowledge Extraction (2)

Question Answering

–
Retrieving knowledge from repositories

•
Question/Answering

–
Advantage:

•
Direct information access via Natural
Language



Q>

How

do

you

get

a


perfect

sun

tan?

NL
-
based

Question

NL

Answer

A>

Lie

in

the

sun

The user factor

•
Adaptivity for new application definition

–
Use of
Machine Learning
for new applications

•
Moving new application building towards non
experts

•
Time reduction

•
Criticality

–
The user factor in training the system:

•
What information/task can the user
provide/perform for adapting the system?

•
How can users know if the system does actually
what required?

Publishing Knowledge

•
Goal

–
getting knowledge to the people who
need it in a form that they can use.

•
Means:

–
Generation of texts from ontologies:

•
Knowledge diffusion

•
Knowledge documentation

–
Text summarisation

–
Generation of texts dependent on user
knowledge state

Knowledge diffusion

•
Advantages:

–
letting knowledge available:

•
In the form needed by each user

•
Expressed with the correct language type

•
Expressed with the correct level of details

•
Expressed without repetition of what is
known.

–
Skill reduction in querying ontologies


HLT infrastructure

•
KM requires a number of HLT techniques
to work together

•
Complex tasks require complex
interactions

•
Integration is then a main issue

–
How do you integrate the strength of each
technology to build an effective system

–
Working against current research paradigm


Conclusions

•
HLT provides many (potential) benefits
for KM

–
Effectiveness

–
Cost reduction

–
Time reduction

–
Subjectivity reduction

•
KM provides many challenges for HLT

–
User factors

–
Integration

Demo

•
Amilcare:

–
User
-
Driven Information Extraction from Text

–
Future Technology

–
Built in AKT

•
Trestle

–
Information Extraction

–
Current Technology

Thank You!