Introduction to Web Science

1. Introduction to Web Science Knowledge Management

2. Introducing Knowledge Management • What do you understand by KM? • Why is it important? • 2 senses ... • The business sense • The Computer Science sense

3. The origins of Knowledge Management (1) • Originated 15,000 years ago with writing • Create enduing records • Rules • Transactions • Cumulative knowledge • 5000 years ago in Mesopotamia • Too many clay tables ... • Setup the first library

4. The origins of Knowledge Management (2) • 500 years ago, the printing press made things much easier ... • 50 years ago, computers started a new revolution ...

5. Modern Knowledge Management Wisdom ...

6. The Problem • We are drowning in information and starving for knowledge • Infosmog: The condition of having too much information to be able to take effective action or make an informed decision • The flooding of data is overwhelming

7. When we have access to more information than we can use, the focus naturally shifts on how to us it ... That’s where Knowledge Management comes into play ...

8. Aspirations • Knowledge services should get • the right information, • to the right person/system, • in the right form, • at the right time • Turn information into knowledge • In some cases turning data into enriched, annotated information • Supporting the knowledge life-cycle

9. KM helps when ... • People are doing manual work and then they have to transfer results to an Information System (IS) • When an IS is used but the process is still inefficient • Think about some examples ... Travel agents

10. How can we define KM? (1) • The systematic management and useof the knowledge • The leveraging of collective wisdom to increase responsiveness and innovation • The use of computer technology to organise, manage, distribute electronically all types of information customised to meet the needs of the users

11. How can we define KM? (2) • The acquisition, management and distribution of relevant information to the parties who need to know • The retention, exploitation and sharing of knowledge that will deliver sustainable advantage • Buzzword used to describe a set of tools for capturing and reuse of knowledge

12. Parenthesis: Book suggestion ...

13. Definitions • Data • raw uninterpreted bits, bytes and signals • Information • data equipped with meaning • Knowledge • information applied to achieve a goal, effect an action, make a decision

14. Data • Knowledge needs data • Data can be classified as • Conversational • Exchanged between humans or group of them • Observational • Collected from the environment • Experimental • Collected as a result of an intervention in the environment

15. Observational Data • Buyer behaviour • Weather patterns • Cultural characteristics • Product characteristics • Market size • Usage?

16. Experimental Data • Person does something that causes the environment to change/respond thus generating new data • Body language • Test Results • Feedback • Usage?

17. Conversational Data • Participants exchange and alter each other’s store of data • Face-to-face • Letters • Chats • Blog • Usage?

18. Exercise ... How would you find the average age of students in class?

19. Answer • Observational • Take a look and estimate age based upon their appearance • Experimental • Take a random sample age of a few and then extrapolate the average • Conversational • Ask each one their age and calculate average

20. Data boundaries • In reality they are artificial, but they help us understand data better ...

21. Information Data that has meaning to the person/system who posses the data

22. Information Mismanagement • Knowledge cannot be managed if information is not managed first • Failure to supply right information at right time causes delays and distractions • Too much information • Too little information which is helpful

23. Different kind of knowledge (1) • Generic Knowledge • Social skills • Principles • Task-specific Knowledge • Functional skills • Technical concepts • How are they held? • How are they transferred? • What are the implications?

24. Different kind of knowledge (2) • Local Knowledge • Technical concepts • Who decides? • How are they internalised? • What are the implications? • Global Knowledge • Ethics & principles • Where do they come from? • How are they expressed? • How are they changed? • Implications?

25. Why did this need for KM grow? • Virtually free information created more information customers • There was a shift from supply to demand • Information Systems are failing to deliver

26. When to use KM? • To take (informed) decisions that • change rapidly • require subjectivity • To set and change rules • When information systems don’t help • When we need assistance

27. Paths of Knowledge Tacit Internalisation Externalisation Explicit

28. What is tacit knowledge? • A kind of knowledge that is in human’s mind • Can be expressed partly or fully • People aware of its existence but feel difficult to express it • In certain situation, people hold it • Some tacit knowledge is personal • Other is power • In some situation, people aware of it, they want to express it but cannot find appropriate or common words to express

29. 90% of knowledge is tacit, the rest is explicit

30. 90% of knowledge is tacit, the rest is explicit

31. Example of tacit knowledge (1) • Think about ‘wine tasting’ a white chardonnay, how do you describe your perception?

32. Example of tacit knowledge (2) • Different people may give different description … • According to pro … • bright, pale gold, clean, fresh nose with some grassiness, light and fresh with clean fruit, good acidity • pale, bright, cream and minerals on the nose starting to open out, medium to full bodied, dry with almost pungent chardonnay fruit, excellent acidity and a long finish, well balanced • According to me, tastes ok 

33. Sending knowledge • Able to communicate (System is accessible and speaks the same language) • Want to communicate (sees benefits and trusts recipients) • Recognise knowledge • Recognise knowledge • Want to communicate (sees benefits and trusts recipients) • Able to communicate (System is accessible, speaks the same language)

34. Receiving knowledge • What to receive • Able to receive • Able to judge source • Able to interpret information • Able to value information • Able to reuse information • What to receive • Able to receive • Able to judge source • Able to interpret information • Able to value information • Able to reuse information

35. Guidelines of KM • Both people and systems must be involved (the tacit factor) • Reward and motivate those who share knowledge • Ensure that all stakeholders share knowledge • Focus where knowledge creates value • Beware of quick fixes • Innovate channels to spread knowledge • Identify and monitor knowledge

36. Supporting the Knowledge Life Cycle

37. Challenges: Acquisition • Diversity of sources • Distributed nature • Problems of scale • Acquisition rationale and annotation • Incidental KA is the Holy Grail

38. Challenges: Modelling • What to model? • How to model? • How enriched? • How personalised?

39. Challenges in the K Life Cycle: Retrieval • Retrieval paradigms • Queries • Scope and extent of search • Nature of search

40. Challenges in the K Life Cycle: Reuse • What does reuse mean? • What can be reused? • How to identify reuse options? • How to model/capture for reuse?

41. Challenges in the K Life Cycle: Publishing • Dynamic document/content construction • Richly linked content • Integrating authoring, reviewing and presentation • Personalised presentation

42. Challenges in the K Life Cycle: Maintainance • How to capture and model for maintenance? • What model of custodianship? • Change control, certification and re-certification • Decommissioning

43. From Knowledge Management to Web Science

44. What is Web Science? • Research Initiative • Created in August 2006 • By • Tim Berners-Lee • Wendy Hall • James Hendler • Nigel Shadbolt • http://webscience.org • Aims to create the science of the web!

45. Challenges of Web Science • Huge • Dynamic • Spread into various disciplines (entertainment, politics, culture, etc) • Need to integrate large amounts of different data • Decentralised • The social aspect of the web • Trust, control, rights, preferences

46. Web Architecture • Simple technologies which • Connect efficiently an information space • Highly flexible and usable • Scalable • Uses URIs at its base • Problems • What is the topology of the web? • What are its limitations? • Websites vrs webpages? • Estimations? • 20% of pages are less than 11 days old • 50% of pages are less than 3 months old • Rest, over a year old

47. Engineering the Web (1) • New innovations • Pervasive Technologies, P2P, Grid, Personalisation, Multimedia, ... But we’re still very limited ... • The Semantic Web • Facilitate discovery and use of data • Information Vrs Data Retrieval

48. Engineering the Web (2) • Pitfalls ... • Consistency • Reliability • Trust • Identities • Give examples ... • The SW will tackle this issue by • Bringing together vast amount of data • Relational Databases, Unstructured Data • And allow the inference of correct data

49. Engineering the Web (2) • Pitfalls ... • Consistency Population of Malta? • Reliability Who wrote in Wikipedia? • Trust Give out personal details? • Identities Am I chatting to the same person? Am I still in the same site? • Give examples ... • The SW will tackle this issue by • Bringing together vast amount of data • Relational Databases, Unstructured Data • And allow the inference of correct data

50. Conclusion • We’ve learnt what is Knowledge Management • We’ve seen where it is evolving • In the next lessons • We shall explore the different parts of the Knowledge Life Cycle in detail