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Information seeking

Information seeking. Information-seeking models Search strategies Search tactics. Information-seeking (IS) models. Holistic view of IS process Possible types of IS model User-centred Descriptive Process-oriented System-centred Prescriptive Object-oriented Other.

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Information seeking

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  1. Information seeking • Information-seeking models • Search strategies • Search tactics Jane Reid, AMSc IRIC, QMUL, 30/10/01

  2. Information-seeking (IS) models • Holistic view of IS process • Possible types of IS model • User-centred • Descriptive • Process-oriented • System-centred • Prescriptive • Object-oriented • Other Jane Reid, AMSc IRIC, QMUL, 30/10/01

  3. User-centred IS models • Description of IS stages / processes • May aid construction of supportive interface • Examples • Simple IS model • Kuhlthau’s ISP model Jane Reid, AMSc IRIC, QMUL, 30/10/01

  4. Simple IS model • Formulate information need • Identify information sources / channels • Search for information • Formulate and submit query • Examine and evaluate results of search • Iteration of search stages if necessary Jane Reid, AMSc IRIC, QMUL, 30/10/01

  5. Limitations of simple IS model • Not appropriate for browsing systems • Assumption of static information need • Overwhelming emphasis on search itself Jane Reid, AMSc IRIC, QMUL, 30/10/01

  6. ISP model [1] • Intended as a search strategy teaching tool for expert intermediaries • Modelling of variations in user uncertainty • Dimensions • Affective (emotional) • Cognitive (thoughts) • Physical (actions) Jane Reid, AMSc IRIC, QMUL, 30/10/01

  7. ISP model [2] • Problem formulation • Initiation • Awareness of lack of knowledge and understanding • Attempt to understand task and relate it to prior knowledge • Selection • Identification and selection of topic to be investigated Jane Reid, AMSc IRIC, QMUL, 30/10/01

  8. ISP model [3] • Exploration • Investigation of information on general topic • Formulation • Formation of focussed perspective on the topic • Development of ability to identify relevant information Jane Reid, AMSc IRIC, QMUL, 30/10/01

  9. ISP model [4] • Problem solving • Collection • Specification of need for relevant, focussed information • Presentation • Completion of search and usage of results Jane Reid, AMSc IRIC, QMUL, 30/10/01

  10. Limitations of ISP model • Not intended for browsing • Model has been partially validated for hypertext environment by another researcher • Highly idealised • Sequential (no iteration) Jane Reid, AMSc IRIC, QMUL, 30/10/01

  11. System-centred IS models • Description of desirable system functions • Aid in construction of intelligent systems • Example • Belkin’s MONSTRAT model Jane Reid, AMSc IRIC, QMUL, 30/10/01

  12. MONSTRAT model [1] • Based on cognitive model of IR interaction • Models • System characteristics • User characteristics • Problem characteristics • Ten functions which correspond to system modules Jane Reid, AMSc IRIC, QMUL, 30/10/01

  13. MONSTRAT model [2] • Dialogue mode • Determine appropriate dialogue type for situation • Problem state • Determine role of user in problem treatment process • Problem mode • Determine appropriate system capability • User model • Generate description of user type, goals, beliefs Jane Reid, AMSc IRIC, QMUL, 30/10/01

  14. MONSTRAT model [3] • Problem description • Generate description of problem type, topic, structure, environment, etc • Retrieval strategy • Choose and apply appropriate retrieval strategies to knowledge resource • Response generator • Determine structure of response appropriate to user and situation Jane Reid, AMSc IRIC, QMUL, 30/10/01

  15. MONSTRAT model [4] • Input analyst • Convert input from user into structures usable by functional experts • Output generator • Convert response to form appropriate to user and situation • Explanation • Describe system operation, capabilities etc to user Jane Reid, AMSc IRIC, QMUL, 30/10/01

  16. Other IS models [1] • Bates’ berry-picking model • Suitable for browsing • Information needs are dynamic • Knowledge is gathered throughout process • Implications for interface design Jane Reid, AMSc IRIC, QMUL, 30/10/01

  17. Other IS models [2] • Reid’s task-oriented model • Model the broader IS context • Work task • Contextual factors • Social factors Jane Reid, AMSc IRIC, QMUL, 30/10/01

  18. Other IS models [3] Jane Reid, AMSc IRIC, QMUL, 30/10/01

  19. Search strategies • General strategies • Overall plan for the search session • Different strategies for different access types • Query-based • Hypermedia • Term selection strategies • Specific strategies • For individual collections, systems, thesauri, etc Jane Reid, AMSc IRIC, QMUL, 30/10/01

  20. Query-based strategies [1] • Starting strategies • Select • Break complex query into topics and deal with each topic separately • Exhaust • Include most elements of the query in the initial query formulation Jane Reid, AMSc IRIC, QMUL, 30/10/01

  21. Query-based strategies [2] • Continuation strategies • Building blocks • Combination of discrete topics • Pearl growing • Small relevant set expanded gradually • Successive fractions • Large relevant set refined gradually Jane Reid, AMSc IRIC, QMUL, 30/10/01

  22. Hypermedia strategies • Strategies often used in combination • Scanning (information structure) • Browsing (casual, undirected exploration) • Selection (choice of individual elements) • Navigation (chain of scan and select operations) Jane Reid, AMSc IRIC, QMUL, 30/10/01

  23. Term selection strategies • Strategies employed by expert searchers depend on: • Vocabulary - free-text vs controlled • Current state of search process • Number of documents retrieved • NLP functionality, e.g. use of proper names • Used as the basis of expert system rules for query reformulation Jane Reid, AMSc IRIC, QMUL, 30/10/01

  24. Search tactics • Individual actions taken at a search stage • Three possible steps • Term tactics • Choose a source of new terms, e.g. thesaurus, WordNet, terms from relevant documents • Search formulation tactics • Design or redesign the search formulation • Idea tactics • Provide ideas to change search direction Jane Reid, AMSc IRIC, QMUL, 30/10/01

  25. Search formulation tactics [1] • Exhaust • Add components to the query • Reduce • Remove components from the query • Union • Specify union of 2 sets representing different query components Jane Reid, AMSc IRIC, QMUL, 30/10/01

  26. Search formulation tactics [2] • Intersect • Specify intersection of 2 sets representing different query components • Parallel • Include synonyms or conceptually similar terms • Vary • Alter / substitute some of the search terms Jane Reid, AMSc IRIC, QMUL, 30/10/01

  27. Search formulation tactics [3] • Block / negate • Reject items containing, or indexed by, certain terms • Neighbour • Add additional “neighbouring” terms from current document Jane Reid, AMSc IRIC, QMUL, 30/10/01

  28. Search formulation tactics [4] • Trace • Examine documents already retrieved for new terms • Fix • Try alternative affixes Jane Reid, AMSc IRIC, QMUL, 30/10/01

  29. Idea tactics [1] • Skip • Shift view of the query laterally • Shift focus from one part of a complex query to another • View the query from a different conceptual angle • Focus • Take a narrower perspective of the query • Choose a limited subset of the query terms • Fix on a limited conceptualisation of the query Jane Reid, AMSc IRIC, QMUL, 30/10/01

  30. Idea tactics [2] • Limit • Limit the search • Specify constraints, e.g. for language, data set, publication year, etc Jane Reid, AMSc IRIC, QMUL, 30/10/01

  31. Summary • Information-seeking models • System-centred • User-centred • Other, e.g. task-oriented • Search strategies • Overall plan for the search session • Search tactics • Individual actions taken at a search stage Jane Reid, AMSc IRIC, QMUL, 30/10/01

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