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Description of Research Area

Description of Research Area. June 22nd. Today we look at --. Motivation & Assertions Related work Where I fit in. Motivation & Assertions. My Research Area. Top level Information Science Information Retrieval (IR) Topic Area Personal Information Management (PIM)

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Description of Research Area

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  1. Description of Research Area June 22nd

  2. Today we look at -- • Motivation & Assertions • Related work • Where I fit in

  3. Motivation & Assertions

  4. My Research Area • Top level Information Science Information Retrieval (IR) • Topic Area Personal Information Management (PIM) • Focus Area Associative Information Retrieval (AIR) • With contributions in – Cognitive / Biomimetic IR (Cogs-IR) Contextual Event Indexing

  5. Motivating Problems for IR Difficult to – • manage large & growing information corpora. • build, interpret, and maintain complex representations. • find relevant and appropriate information. • specify accurate & effective queries. Desire to avoid - • expensive extraction of expert knowledge • burden on users of ongoing feedback

  6. My Research Claims • Storing and retrieving information in a biomimetic way will improve user access and understanding. biomimetic: Imitating, copying, or learning from biological systems. Overarching claims – • All information processing is a form of cognitive prosthesis. • The goal of information retrieval is memory prosthesis.

  7. Research Goals • To create a framework for IM that is ---

  8. Assertions • Biomimetic representation is good. • Human information management is primarily mnemonic – a function of memory processes. • However information management behaviour is culturally biased: need to model function of human memory. • To function as memory does, processing should be incremental, and online(i.e. while-you-wait). • Approach needs to be pragmatic. • Memory is fundamentally dependent on context. ...and now to convince you of this...

  9. Related Work In computer science, cognitive science, and information retrieval

  10. Idea • There are remarkably similar paradigms in each of these fields. • Can these similar paradigms be used constructively to improve information management?

  11. For each discipline... • That area’s key findings and contributions • An overview of its scope • Thesis-related work

  12. Computer Science (CS) • The study and application of computability • Key contributions • A formal definition of computability: solvable and intractable problems • Programming languages • Culturally significant technologies • The enabling of new types of research

  13. Computer Science (CS) • The study and application of computability hard soft areas: algorithms systems AI HCI numerical languages databases software engineering parallel & distributed knowledge representation machine learning robotics infovis graphics user modeling

  14. Computer Science (CS) • The study and application of computability hard soft areas: algorithms systems AI HCI numerical languages databases software engineering parallel & distributed knowledge representation machine learning robotics infovis graphics user modeling

  15. Related Work in CS • Not in general. For relevant areas – Human-centred - • KR work is in semantic networks, which are neither simple nor automatic. • UM is ethnographic, focused on behaviour. Not human-centred - • DB work is mostly process-oriented (esp. XML, schemas) and offline (data mining) • ML focuses on tools (Bayes, Markov, ACO, genetic algorithms, etc.)

  16. However, there is some niche work on memory context in HCI...

  17. Memory Prosthesis in CS-HCI Could be a subset of contextual computing. Typically comes in two flavours – Direct user intervention & programming, e.g. – • CybreMinder: A Context-Aware System for Supporting Reminders – user-specified temporal & situational context [Dey & Abowd 00] • Autominder: A Planning, Monitoring, and Reminding Assistive Agent – “cognitive orthotic”, monitors routine daily plans [Pollack et al. 02] Passive but basic indexing, e.g. – • The wearable remembrance agent: a system for augmented memory – tagging w/ available context data [Rhodes 97] • Next-generation personal memory aids – wearable audio recorder; timeline w/ physical location [Vemuri & Bender 04] … and tend to be purpose-built expert systems Goals: not simple or automatic, not adaptive

  18. What I get from CS • The tools to implement and test an approach. • Alternative approaches for comparison.

  19. Cognitive Science (Cogs) • The study of mind or intelligence • Key contributions Empirical models and theories of human – • memory, planning, and decision-making • ontology and epistemology • bias, persuasion and coercion • language

  20. Cognitive Science (Cogs) • The study of mind or intelligence language processing perception & action areas: attention development memory

  21. behavioural functional Cognitive Science (Cogs) • The study of mind or intelligence language processing perception & action areas: attention development memory levels: physical

  22. behavioural functional Cognitive Science (Cogs) • The study of mind or intelligence language processing perception & action areas: attention development memory levels: physical approaches: symbolic connectionist dynamic

  23. behavioural functional Cognitive Science (Cogs) • The study of mind or intelligence ‘connectionism’ = neural language processing perception & action areas: attention development memory levels: physical approaches: symbolic connectionist dynamic

  24. Related Work in Cogs • Models of memory function : neural vs symbolic • Models of knowledge : continuity vs. features • Context : cues and summaries

  25. Connectionist / Neural Models Neural networks are often used for function approximation, classification, and clustering. Nodes = processors Links = weights Good for – • Robustness • Online learning • Large datasets

  26. Our goals vs. Neural Networks Neural networks are tricky to use, and a relatively good understanding of the underlying theory is essential. Significant experimentation is required to select and tune an algorithm for training on unseen data. Inappropriate: Too fine-grained for a large peer model. Goals: not simple or inspectable, and not built automatically.

  27. Better: Symbolic Associative Networks • Relatedness of discrete concepts Nodes = objects Links = weight Like NNs at higher,symbolic level [Collins & Loftus 75]

  28. Associative networks fit our goals • Simple, inspectable • Maps directly ontoIR tasks Goals: none violated! but how do we build knowledge in the first place? [Anderson 83]

  29. Related Work in Cogs • Models of memory function : neural vs symbolic • Models of knowledge : continuity vs. features • Context : cues and summaries

  30. How do we represent the world? 3 options for building knowledge: • Continuous value function over the entire world – clearly impractical ! [Smith 96] • Semantic networks [esp. Brachman & Levesque 86] • Discrete Attribute models • Feature-vector methods [Deerwester et al. 90] • Qualia in multiple dimensions [Osgoode 52] • Feature-set theory [Smith, Shoben, & Rips 75]

  31. Semantic Networks • Focus: objects& complex relations Nodes = objects Links = relations • Popular for expert systems & linguistics. • Goals: not simple, automatic, or scalable. [Cohen & Kjeldsen 87]

  32. Wrong Ontology ! • In semantic networks, objects are defined by their relations to other objects. • Instead of defining objects by who they know, let’s define objects by what they are.

  33. 1st try – Qualia in multiple dimensions Objects are defined by opposing qualities. [Osgood 52] • Defines each object with a set of Likert scales. • Each scale describes a dimension of knowledge, e.g. wet-dry, and light-dark. Key drawback – Valuations must be performed by human subjects. Goals: not automatic or scalable

  34. 2nd try – Feature Vectors Latent Semantic Analysis [Deerwester et al. 90] • Reduces a large, sparse term-document matrix to a smaller, low-rank concept-approximation matrix. • Combines terms ad hoc to create orthogonal concept dimensions • Can be incremental & online. Key drawback – Resulting dimensions can be difficult to interpret: mathematically but not semantically justifiable. Goals: not inspectable

  35. Best: Feature Set Theory Objects are defined by discrete attributes. [Smith, Shoben, & Rips 74] • Objects are identified by discrete defining and characteristic attributes. • Objects are similar to the degree that they share attributes. [Tversky 77] • Ideal for the document domain, since document’s keywords are its discrete semantic features. Goals: no conflicts

  36. Related Work in Cogs • Models of memory function : neural vs symbolic • Models of knowledge : continuity vs. features • Context : cues and summaries

  37. 3. Context • Cue-based retrieval, encoding specificity [Tulving 75] • The human brain functions primarily to model the statistical regularities of the environment. [Schooler 91; Munakata 04] • Again, ideal for the document domain, where – • retrieval depends on the specification of features (keywords) • ideally, what is retrieved will depend on what you’re doing

  38. What I get from Cogs • Objects defined by discrete features. • Objects are related if they share features. • A navigable associative network.

  39. This implies “Cognitive IR” If objects = documents & features = terms maps directly onto information retrieval tasks:search with features, link to related objects

  40. Context as Discrete Features • Context can be described as features in the environment. • An internal semantic context is supplemented by an external environmental context.

  41. Memory Prosthesis Since terms = cues... • A Cogs-IR system can act as a cue manager and a cue scaffold.

  42. Information Retrieval • Science of searching for documents, within documents, and for document descriptors. • More recently – • Multimedia • Web-based search • User context

  43. Key Contributions of IR • Search engines as the dominant tool for information access • Statistical language analysis – v. successful • Evaluation as a research area in itself, using large test corpora and human valuations • Acknowledgement of the importance of users and interaction

  44. Where does Information Retrieval fit in? • “Although IR has had a very strong relationship with library and information science, its relationship to computer science (CS) and its relative standing as a sub-discipline of CS has been more dynamic. IR is quite an old field, and when a number of CS departments were forming in the 60s, it was not uncommon for a faculty member to be pursuing research related to IR.” [Croft 2003]

  45. Information Retrieval • The study of document organization theory tools applications semantics ontology epistemology language models similarity, classification indexing, summarization relevance, ranking evaluation recommender systems Web services privacy, security information management

  46. Information Retrieval • The study of document organization theory tools applications semantics ontology epistemology language models similarity, classification indexing, summarization relevance, ranking evaluation recommender systems Web services privacy, security information management

  47. IR Knowledge Models with term independence [Kuropka 2004]

  48. IR Knowledge Models with term independence [Kuropka 2004]

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