1. Language Technologies Reality and Promise in AKT Yorick Wilks and Fabio Ciravegna Department of Computer Science, University of Sheffield

2. Overview HLT Using HLT for Knowledge Management Challenges for HLT in AKT –Acquiring Knowledge –Extracting Knowledge –Publishing Knowledge Demos

3. 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

4. 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

5. HLT in AKT Knowledge acquisition retrieval publishing Text miningX Information Extraction XX from Text ClassificationXX SummarizationX Text GenerationX Question XX Answering

6. Traditional Knowledge Management Drowning in information Starving for Knowledge

7. 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

8. 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

9. Knowledge Acquisition: Current Practice Methodology largely manual –Protocol analysis in industry –Manual management in Yahoo (100 people) High cost Based on human introspection

10. 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

11. 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

12. 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 QuestionNL Answer A> Lie in the sun

13. 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?

14. 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

15. 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

16. 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

17. Conclusions HLT provides many (potential) benefits for KM –Effectiveness –Cost reduction –Time reduction –Subjectivity reduction KM provides many challenges for HLT –User factors –Integration

18. Demo Amilcare: –User-Driven Information Extraction from Text –Future Technology –Built in AKT Trestle –Information Extraction –Current Technology

19. Thank You!

20. User Factor in HLT and KM Need: –KM needs simple application definition Solution: –Moving application definition towards users Knowledge engineers Naïve users Why? –HLT is ONE of the tools for KM –Effective usage if: HLT systems can be ported by non HLT experts

21. HLT Techniques Integration 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

22. HLT areas for KM Text mining: –Discovering relations in documents –Ontology learning Information extraction Text classification Text summarization NL generation Question/answering (…)

23. Knowledge Acquisition From texts to knowledge –Ontology constructions Document-based ontology definition Document classification Text mining Texts

24. KA: further references C. Brewster, F. Ciravegna, Y.Wilks Knowledge Acquisition for Knowledge Management: Position Paper IJCAI-01 Workshop on Ontology Learning

25. ML 4 IE: further references IJCAI-01 Workshop on Adaptive Text Extraction and Mining The user factor: –F. Ciravegna and D. Petrelli User Involvement in customizing Adaptive Information Extraction from Texts: Position Paper. Use of NLP in Adaptive IE –F. Ciravegna (LP) 2, an Adaptive Algorithm for Information Extraction from Web-related Texts

26. KA cycle Texts } Initial Ontology Result validation Ontology Learning/Refinement Ontology User role: Initial text selection Proposing initial ontology Validating/modifying proposed ontology System role: Processing texts Extracting/Modifying ontology Retrieving new relevant texts Proposing ontology

27. Information Extraction Texts Tailoring Information Extraction Systems Template Learning /Definition Populating with instances Document classification Information Extraction