1. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 1 of 13 cstaff@cs.um.edu.mt CSA3080: Adaptive Hypertext Systems I Dr. Christopher Staff Department of Computer Science & AI University of Malta Lecture 9: Representing Data, Information, and Knowledge I

2. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 2 of 13 cstaff@cs.um.edu.mt Aims and Objectives We’ve discussed the aims and objectives of IR and hypertext –Both enable the user to find information If the user knows how to describe it, or If the user knows where to find it Adaptive systems actively assist the user to locate information Later, we’ll see how are users interests may be represented

3. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 3 of 13 cstaff@cs.um.edu.mt Aims and Objectives If we assume that a user’s interests are known to an adaptive system… … the adaptive system needs to know something about the domain to know how to adapt it sensibly We will return to this in CSA4080 when we discuss Intelligent Tutoring Systems, but here we give an informal introduction

4. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 4 of 13 cstaff@cs.um.edu.mt Data, Information, and Knowledge Data –simple/complex structures –Arbitrary sequences “Chris”, 280963, “b47y3” Information –Data in Context “Author’s name: Chris” “Boeing left wing Part no: b47y3”

5. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 5 of 13 cstaff@cs.um.edu.mt Data, Information, and Knowledge Knowledge –Knowing when to use information “When ordering a replacement part, specify the part number and quantity required”

6. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 6 of 13 cstaff@cs.um.edu.mt Surface-based to Deep Semantic Representations Surface-based models tend to use data/information Deep semantic models tend to use knowledge Information retrieval systems (Extended/Boolean, Statistical) “know” about term features within documents Additionally, statistical models “know” the distribution of terms throughout the collection Using NL statistics about the distribution of terms in language may give further information (not about terminology, though)

7. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 7 of 13 cstaff@cs.um.edu.mt Surface-based to Deep Semantic “Dumb” IR systems can find documents containing “John”, “loves”, “Mary”, but cannot answer the question “Does John love Mary?” –“John loves Mary” will miss “Mary is loved by John”, “John cares deeply for Mary”, etc. –Sometimes complex reasoning is also needed

8. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 8 of 13 cstaff@cs.um.edu.mt Surface-based to Deep Semantic “Normal” hypertext (e.g., WWW) “knows” that some documents are linked Lack of link semantics –Why/for what reason have these documents been linked? –Can make assumptions Can deduce link types (e.g., navigational, contextual, etc), but better if type was explicit

9. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 9 of 13 cstaff@cs.um.edu.mt Surface-based to Deep Semantic Semantic networks connect data nodes using typed links (e.g., isa, part_of, …) Can do complex reasoning by examining relationships between nodes If a hypertext had typed links, would it be a semantic network? –“Knowledge” and “information” are largely embedded within unstructured text –If exposed, then, potentially, a hypertext can be used to represent and reason with information and knowledge

10. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 10 of 13 cstaff@cs.um.edu.mt Semantic Web “The Semantic Web is an extension of the current web in which information is given well- defined meaning, better enabling computers and people to work in cooperation.” [Berners-Lee2001] References: –Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, in Scientific American, May 2001 –http://www.w3.org/2001/sw/

11. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 11 of 13 cstaff@cs.um.edu.mt Semantic Web Semantic Web,and Web technologies are covered in more detail by Matthew We’ll later return to solutions to AHS which are closer to surface-based, but we’ll spend some time considering the Semantic Web

12. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 12 of 13 cstaff@cs.um.edu.mt Semantic Web Architecture From http://mail.ilrt.bris.ac.uk/~cmdjb/talks/sw-vienna/slide10.html

13. University of Malta CSA3080: Lecture 9 © 2003- Chris Staff 13 of 13 cstaff@cs.um.edu.mt Back to surface-based approaches One of the challenges facing the Semantic Web is making the knowledge and information contained in existing Web pages explicit Partly concerned with exposing relational data in textual documents But also, opinions, beliefs, facts, …