CSA3212: User-Adaptive Systems

1. Dr. Christopher Staff Department of Intelligent Computer Systems University of Malta CSA3212: User-Adaptive Systems Topic 3: Information & Knowledge Representation

2. Aims and Objectives Adaptive Hypertext Systems need Hypertext, User Modelling, and Domain Modelling, and a mechanism for comparing the user model and the domain model General purpose AHSs tend to use IR techniques to represent the domain ITSs frequently use deeper “semantic” representations, eg, conceptual graphs

3. Aims and Objectives We informally introduce IR and hypertext, to compare their objectives, assumptions, similarities and differences We’ll also talk about UM, and its relationship with IR and hypertext

4. Aims and Objectives Once we know what an UAS user’s interests are, we can find relevant information in the document collection Guide user along path Show relevant document to user Make recommendation to user Select next item in curriculum to teach

5. Aims and Objectives We’ll be looking at Objectives and Assumptions of Information Retrieval, Hypertext, and User Modelling Data, Information, and Knowledge Information Retrieval (several models) Approaches to dealing with general knowledge Surface-based approaches to some types of problem

6. Part I: Objectives and Assumptions of IR, HT, and UM

7. Objectives of IR To represent documents in a collection To facilitate document retrieval from the collection User query represents information need Matching algorithm compares user query to document representations Matching documents presented as “relevant” Results may be ranked in order of relevance

8. Spectrum of Indexing Methods

9. Objectives of Hypertext A (not so new) reading system Represents an information space (typically as a graph) Related information can be “linked” Users navigate through hyperspace by traversing links Enables users to choose which path to follow

10. Assumptions of IR The user can describe the information need The information need can be (sufficiently) described using keywords/terms A document matching the query will be suitable for the particular user (expert v novice) A single document contains the information

11. Assumptions of Hypertext The user can find a relevant document by following links Links will connect related information Related information is linked!

12. IR/Hypertext Similarities Users can seek information IR: Query matching Hypertext (HT): Browsing Collections of documents IR: Similar documents will have similar representations (keywords)? HT: Similar documents will be linked? NB: Doesn’t imply all linked docs are similar!

13. IR/Hypertext Differences User interaction: HT: Follow link - most systems don’t directly support search IR: Submit query - Most systems don’t directly support linking Relevant info: IR: relevant info stored in single document HT: can be spread over multiple, linked documents

14. IR/Hypertext Differences Organisation: HT: graph (or network), in which related documents are linked (at best) IR: (at best) clusters of similar documents, (at worst) no organisation.

15. User Modelling Represent interesting “features of the user” [Brusilovsky96] Used in many different domains Reference: Kobsa, A. (1993). User Modeling: Recent Work, Prospects and Hazards, in M. Schneider-Hufschmidt, T. Kühme and U. Malinowski, eds. (1993): Adaptive User Interfaces: Principles and Practice. North-Holland, Amsterdam, 1993. (http://fit.gmd.de/~kobsa/papers/1993-aui-kobsa.pdf) http://www.ics.uci.edu/~kobsa/

16. User Modelling Many different ways of representing interests, goals, beliefs, preferences However the user is modelled, the information that he/she can be given is only as good as the representation of the domain!

17. Conclusion Information Retrieval, Hypertext, and User Modelling underpin most general-purpose User Adaptive Systems We’ve taken a look at the objectives, assumptions, similarities, and differences between IR and HT

18. Part II: Data, Information, Knowledge

19. Background We’ve briefly mentioned some of the user information that we might want to represent We also need to be able to represent information about the domain so that we can reason about what the user’s interests are, etc.

20. Background In 1945, Vannevar Bush writes “As We May Think” Gives rise to seeking “intelligent” solutions to information retrieval, etc. In 1949, Warren Weaver writes that if Chinese is English + codification, then machine translation should be possible Leads to surface-based/statistical techniques

21. Background Even today, about 60 years later, there is significant effort in both directions For years, intelligent solutions were hampered by the lack of fast enough hardware, software Doesn’t seem to be an issue any longer, and the Semantic Web may be testimony to that But there are sceptics

22. Background Take IR as an example At the dumb end we have “reasonable” generic systems, but at other end, systems are domain specific, more expensive, but do they give “better” results?

23. Background 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 later, but here we give an informal introduction

24. 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”

25. Data, Information, and Knowledge Knowledge Knowing when to use information “When ordering a replacement part, specify the part number and quantity required”

26. 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)

27. 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 adored by John”, “John cares deeply for Mary”, etc. Sometimes complex reasoning is also needed

28. 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

29. 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

30. 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/

31. 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, …

32. Background At what point does it cease to be cost effective to attempt more intelligent solutions to the IR problem?

33. Background Is “Information” Retrieval a misnomer? Consider your favourite Web-based IR system... does it retrieve information? Can you ask “Find me information about all flights between Malta and London”? And what would you get back? Can you ask “Who was the first man on the moon?”

34. Background With many IR systems that we use, the “intelligence” is firmly rooted in the user We must learn how to construct our queries so that we get the information we seek We sift through relevant and non-relevant documents in the results list What we can hope for is that “patterns” can be identified to make life easier for us - e.g., recommender systems

35. Background Surface-based techniques tend to look for and re-use patterns as heuristics, without attempting to encode “meaning” The Semantic Web, and other “intelligent” approaches, try to encode meaning so that it can be reasoned with and about Cynics/sceptics/opponents believe that there is more success to be had in giving users more support, than to encode meaning into documents to support automation

36. However... We will cover mostly surface-based and also some knowledge-based approaches to supporting the user in his or her task IR and IR techniques Dealing with General Knowledge

37. Information Retrieval We will discuss IR models... Boolean, Vector Space Model, Extended Boolean, Phrase Matching, Probabilistic Model ... and surface-based techniques that can improve their usability Relevance Feedback Query Reformulation

38. Knowledge Conceptual graphs support the encoding and matching of concepts Conceptual graphs are more “intelligent” and can be used to overcome some problems like the Vocabulary Problem

39. Part III: Information Retrieval

40. Aims and Objectives Aims and objectives of IR Boolean, Extended Boolean, Vector Space, Phrase Matching, Probabilistic models

41. Aims and Objectives You should end up knowing the major differences between various matching algorithms And what each algorithm considers to be a relevant document… Bear in mind that we will use IR in AHS to find information relevant to our user so that we can present it/lead the user to it…

42. Aims and Objectives of IR To facilitate the identification and retrieval of documents that contain information relevant to an information need expressed by a user We are particularly interested in the retrieval of information from unstructured data

43. Boolean Information Retrieval Developed in 1950’s A document is represented by a collection of terms that occur in the document (index) The unique terms occurring in the collection is called the vocabulary A document is represented by a bit sequence with a 1 representing a term that is present, and 0 otherwise

44. Boolean Information Retrieval How is the query expressed? User thinks of terms that describe an information need Formalises query as a boolean expression (Term27 OR Term46) NOT (Term30 AND Term16)

45. Boolean Information Retrieval How does the matching algorithm work? Each term in the vocabulary has a set (or postings list) of documents that contain the term For each term in the query, the postings list is retrieved Set operations (union/disjunction/intersection) All documents in the results set are returned

46. Boolean Information Retrieval

47. Does Boolean IR work? Boolean IR is typically applied to a document surrogate And is used with tremendous success in RDBMS Most general purpose IR systems in use on the Internet are derived from BIR with some extensions…

48. Does Boolean IR work? BIR works, and works well, when the vocabulary is reasonably small… … when there is no ambiguity in the meaning of terms … when the presence of a term in a document is significant … when the absence of a term from a document means that the document cannot be about that term

49. Questions Arising… Is this reallyinformation retrieval? Just because a document contains term x, does it mean that the document is about term x? What about concepts? What makes it possible for us to know that a fish cake is not a dessert? That “she is the apple of my eye” does not make her a piece of fruit?

50. Questions Arising… How can we tell that the IR system works?