CSA3080: Adaptive Hypertext Systems I

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

2. 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 2 of 13 cstaff@cs.um.edu.mt

3. 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 3 of 13 cstaff@cs.um.edu.mt

4. 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” 4 of 13 cstaff@cs.um.edu.mt

5. Data, Information, and Knowledge • Knowledge • Knowing when to use information • “When ordering a replacement part, specify the part number and quantity required” 5 of 13 cstaff@cs.um.edu.mt

6. Surface-based to Deep SemanticRepresentations • 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) 6 of 13 cstaff@cs.um.edu.mt

7. 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 7 of 13 cstaff@cs.um.edu.mt

8. 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 8 of 13 cstaff@cs.um.edu.mt

9. 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 9 of 13 cstaff@cs.um.edu.mt

10. 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/ 10 of 13 cstaff@cs.um.edu.mt

11. 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 11 of 13 cstaff@cs.um.edu.mt

12. Semantic Web Architecture From http://mail.ilrt.bris.ac.uk/~cmdjb/talks/sw-vienna/slide10.html 12 of 13 cstaff@cs.um.edu.mt

13. 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, … 13 of 13 cstaff@cs.um.edu.mt