COMP3410 DB32: Technologies for Knowledge Management

1. COMP3410 DB32:Technologies for Knowledge Management Introduction By Eric Atwell, School of Computing, University of Leeds (including re-use of teaching resources from other sources, esp. Stuart Roberts, School of Computing, Univ of Leeds)

2. Office: 6.06a e.s.atwell@leeds.ac.uk http://www.comp.leeds.ac.uk/eric/ http://www.comp.leeds.ac.uk/nlp/ Eric Atwell

3. Knowledge in Knowledge Management the nature of knowledge, definitions and different types Knowledge used in Knowledge Based Systems, DataBases Knowledge and Information Retrieval / Extraction Analysis of unstructured data in text/WWW IR: finding documents which match keywords / concepts IE: extracting DB-fields (terms, facts) from documents Matching user requirements, advanced/intelligent matching Mining WWW as source of data and knowledge Knowledge Discovery Collating data in data warehouse; transforming and cleaning Cross-industry standard process for data mining (CRISP-DM) OLAP, knowledge visualisation, machine learning, data mining Analysis of WWW-sourced data Topics in TKM

4. Lectures: *NORMALLY*… Monday 12-1 LT23 Wednesday 10-11 LT23 VLE announcements of changes: No lectures Week 7 Half Term; possible illness etc… Assessment 2 coursework exercises, 5-page reports (30%) Exam: 2 hrs, closed book (70%) Students are expected to perform to an acceptable level in both exam and coursework components in order to pass. Lectures and Assessment

5. A break from the lecture / (?lecturer…?) up to 5 minutes (MAX) for: Interesting development/application in KM E.g. TKM company, product, research project, website, users/market, news story, … E.g. RELEVANT YouTube video … Email eric@comp.leeds.ac.uk to volunteer For peer appreciation! Does not “count” in Grade Commercial Breaks

6. Blackboard VLE Web site: http://www.comp.leeds.ac.uk/db32 /home/www/db32 Weekly Hardcopy Documentation Powerpoint slides Selected papers to read before next lecture Electronic copies of slides available from web-site after the lecture – but don’t print your own! Further background reading; suggestions welcome Learning Resources

7. What is knowledge? What does it mean to manage / discover knowledge? How can information technology help? Three Questions

8. A range of definitions: CED: Collins English Dictionary LDOCE: Longman Dictionary of Contemporary English: “simpler” definitions for English language learners (and Language Engineering) Online definitions: Google, Wikipedia Knowledge in Knowledge Management Knowledge in Knowledge Based Systems What is Knowledge?

9. 1. The facts, feelings or experiences known by a person or group of people. 2. The state of knowing. 3. Awareness, consciousness, or familiarity gained by experience or learning. 4. Erudition or informed learning. 5. Specific information about a subject. 6. Sexual intercourse (carnal knowledge). Which types of knowledge can a computer handle? Collins English Dictionary:

10. 1. the facts, skills, and understanding that you have gained through learning or experience 2. knowing that something has happened or is true 4. information that you have about a particular situation, event etc. See also general knowledge, common knowledge, working knowledge Which of these can a computer handle? LDOCE: Longman Dictionary of Contemporary English

11. - We have a version of LDOCE in a database actually, a text-file, with mark-up showing records, fields structure http://www.comp.leeds.ac.uk/eric/ldoce 1978 LISP Markup predates XML, HTML etc: ((headword) (<field-no> <data>) (<filed-no><data>) … ) … This illustrates some challenges of DATA MINING: UNDERSTANDING, CLEANSING, MODELLING… (see CRISP-DM methodology for Data-Mining) LDOCE as a text database

12. Three meanings: the state of knowing or to be acquainted or familiar with (“know about”) the capacity for action (“know how”) codified, captured and accumulated facts, methods, principles and techniques. Based on: F Nickols. 2000. The Knowledge in Knowledge Management. KM Yearbook. Knowledge in Knowledge Management

13. Three meanings: the state of knowing or to be acquainted or familiar with – a DB does not “know about” its data the capacity for action – a DB does not “know how” to do anything codified, captured and accumulated facts, methods, principles and techniques. – maybe DB could store this? Based on: F Nickols. 2000. The Knowledge in Knowledge Management. KM Yearbook. Knowledge in Knowledge Management

14. Atwell, E S (editor) Knowledge at Work in Universities - Proceedings of the second annual conference of the Higher Education Funding Council's Knowledged Based Systems Initiative, 146pp University of Leeds Press. 1993. No definition of KBS, except by examples… KB: facts and logical rules for inferring new facts Eg: if (sun=yes) and (humidity=low) then play=yes …but also Info Retrieval, language/speech/image Knowledge Based Systems

15. Knowledge that has been articulated product specifications scientific formulae computer programs patents documented best practice Handbooks Could be stored in a DB (if we can solve problems of data capture / transformation, …) Explicit Knowledge

16. Knowledge that cannot be articulated (eg in a DB) is called tacit knowledge how to ride a bicycle how we recognise a face How to understand an English sentence / document how to create a work of art You could say AI is trying to recast Tacit knowledge as Explicit knowledge – eg rules to process English sentences Tacit Knowledge

17. Knowledge that could be articulated but hasn’t (yet) … is called implicit knowledge. knowledge engineers and systems analysts are trained to identify implicit knowledge and to help experts articulate their knowledge. Could be stored in a DB (if we can solve problems of data capture / transformation, …) Also … Implicit Knowledge

18. The knowledge an organisation has about itself and its environment (“meta-level knowledge”?) Shared beliefs, norms and values. A framework within which organisational members construct reality Required to understand and use facts, rules and heuristics To make inductions in the same way as others in order to enable concerted action Also … Cultural Knowledge

19. From Data to Knowledge Belief-Structuring KNOWLEDGE high Cognitive-Structuring * beliefs* justification INFORMATION Order/Structure * meaning* significance Physical-Structuring DATA * sensing* selecting low SIGNALS low Human Agency high

20. Data mining: finding patterns (knowledge) in data Knowledge discovery: finding knowledge (in data) Database: stores data Information management: making use of data Knowledge Management: finding and making use of patterns in data, “taming the data” Different perspectives of data?

21. DB32 is not about Database technologies! DB32 is about acquiring data, cleansing data, extracting useful structure or knowledge from data BUT not just data in databases… … most data/information on WWW is unstructured TEXT, though HTML/XML markup may help (a bit) … so we need to extract and clean data from WWW into DB-like format (fields/attributes, records, tables) for data-mining. Conclusion

22. Can you distinguish knowledge from information? Think up examples of explicit, tacit, implicit and cultural knowledge that exist in the School of Computing. Do we manage knowledge by managing information, or is there more to it than that? Can knowledge be ‘created’ or only acquired? Think of some applications of TKM. http://www.youtube.com/watch?v=uz0KXaflY2w Does this really “explain knowledge management”? Questions to think about…