ppt - Advanced Knowledge Technologies

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6 Νοε 2013 (πριν από 3 χρόνια και 11 μήνες)

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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


Speed: Prompt Identification of
critical factors



Quantity: more information can
be accessed by people



Quality: only relevant
information is accessed by
people


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!