Language Technologies Reality and Promise in AKT

Yorick Wilks and Fabio CiravegnaDepartment 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 XAnswering

Traditional Knowledge Management

Drowning in informationStarving for Knowledge

Information Extraction from TextQuestion Answering Text Summarization

Knowledge Management using HLT

HLT

Reports writtenin 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

LanguageQ> 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!