language technologies reality and promise in akt yorick wilks and fabio ciravegna department of...
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![Page 1: Language Technologies Reality and Promise in AKT Yorick Wilks and Fabio Ciravegna Department of Computer Science, University of Sheffield](https://reader036.vdocuments.net/reader036/viewer/2022062511/5514da38550346b0478b541a/html5/thumbnails/1.jpg)
Language Technologies Reality and Promise in AKT
Yorick Wilks and Fabio CiravegnaDepartment of Computer Science,University of Sheffield
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Overview
• HLT• Using HLT for Knowledge Management• Challenges for HLT in AKT
– Acquiring Knowledge– Extracting Knowledge– Publishing Knowledge
• Demos
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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
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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
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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
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Traditional Knowledge Management
Drowning in informationStarving for Knowledge
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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
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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
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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
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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
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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
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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?
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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
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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
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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
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Conclusions
• HLT provides many (potential) benefits for KM– Effectiveness– Cost reduction– Time reduction– Subjectivity reduction
• KM provides many challenges for HLT– User factors– Integration
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Demo
• Amilcare: – User-Driven Information Extraction from
Text– Future Technology– Built in AKT
• Trestle– Information Extraction– Current Technology
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Thank You!