Transcript 9 - University of Malta

CSA3080:
Adaptive Hypertext Systems I
Lecture 9:
Representing Data, Information, and
Knowledge I
Dr. Christopher Staff
Department of Computer Science & AI
University of Malta
CSA3080: Lecture 9
© 2003- Chris Staff
1 of 13
[email protected]
University of Malta
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
CSA3080: Lecture 9
© 2003- Chris Staff
2 of 13
[email protected]
University of Malta
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
CSA3080: Lecture 9
© 2003- Chris Staff
3 of 13
[email protected]
University of Malta
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”
CSA3080: Lecture 9
© 2003- Chris Staff
4 of 13
[email protected]
University of Malta
Data, Information, and
Knowledge
• Knowledge
– Knowing when to use information
• “When ordering a replacement part, specify the part
number and quantity required”
CSA3080: Lecture 9
© 2003- Chris Staff
5 of 13
[email protected]
University of Malta
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)
CSA3080: Lecture 9
© 2003- Chris Staff
6 of 13
[email protected]
University of Malta
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
CSA3080: Lecture 9
© 2003- Chris Staff
7 of 13
[email protected]
University of Malta
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
CSA3080: Lecture 9
© 2003- Chris Staff
8 of 13
[email protected]
University of Malta
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
CSA3080: Lecture 9
© 2003- Chris Staff
9 of 13
[email protected]
University of Malta
Semantic Web
“The Semantic Web is an extension of the
current web in which information is given welldefined 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/
CSA3080: Lecture 9
© 2003- Chris Staff
10 of 13
[email protected]
University of Malta
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
CSA3080: Lecture 9
© 2003- Chris Staff
11 of 13
[email protected]
University of Malta
Semantic Web Architecture
QuickTime™ and a TIFF (Uncompressed) decompressor are needed to see this picture.
From http://mail.ilrt.bris.ac.uk/~cmdjb/talks/sw-vienna/slide10.html
CSA3080: Lecture 9
© 2003- Chris Staff
12 of 13
[email protected]
University of Malta
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, …
CSA3080: Lecture 9
© 2003- Chris Staff
13 of 13
[email protected]
University of Malta