1. Structure of IR Systems LBSC 796/INFM 718R Session 1, September 10, 2007 Doug Oard

2. Agenda Teaching theater orientation The structure of interactive IR systems Course overview

3. What is IR? Information retrieval is a problem-oriented discipline, concerned with the problem of the effective and efficient transfer of desired information between human generator and human user Anomalous States of Knowledge as a Basis for Information Retrieval. (1980) Nicholas J. Belkin. Canadian Journal of Information Science, 5, 133-143.

4. Information Retrieval Systems Information –What is “information”? Retrieval –What do we mean by “retrieval”? –What are different types information needs? Systems –How do computer systems fit into the human information seeking process?

5. Information Hierarchy Data InformationKnowledge Wisdom More refined and abstract

6. Information Hierarchy Data –The raw material of information Information –Data organized and presented in a particular manner Knowledge –“Justified true belief” –Information that can be acted upon Wisdom –Distilled and integrated knowledge –Demonstrative of high-level “understanding”

7. What do We Mean by “Information?” How is it different from “data”? –Information is data in context Databases contain data and produce information IR systems contain and provide information How is it different from “knowledge”? –Knowledge is a basis for making decisions Many “knowledge bases” contain decision rules

8. A (Facetious) Example Data –98.6 º F, 99.5 º F, 100.3 º F, 101 º F, … Information –Hourly body temperature: 98.6 º F, 99.5 º F, 100.3 º F, 101 º F, … Knowledge –If you have a temperature above 100 º F, you most likely have a fever Wisdom –If you don’t feel well, go see a doctor

9. What types of information? Text Structured documents (e.g., XML) Images Audio (sound effects, songs, etc.) Video Programs Services

10. What Do We Mean by “Retrieval?” Find something that you want –The information need may or may not be explicit Known item search –Find the class home page Answer seeking –Is Lexington or Louisville the capital of Kentucky? Directed exploration –Who makes videoconferencing systems?

11. Relevance Relevance relates a topic and a document –Duplicates are equally relevant, by definition –Constant over time and across users Pertinence relates a task and a document –Accounts for quality, complexity, language, … Utility relates a user and a document –Accounts for prior knowledge

12. Systems: The Memex

13. What Do People Search For? Searchers often don’t clearly understand –The problem they are trying to solve –What information is needed to solve the problem –How to ask for that information The query results from a clarification process Dervin’s “sense making”: Need GapBridge

14. Taylor’s Model of Question Formation Q1 Visceral Need Q2 Conscious Need Q3 Formalized Need Q4 Compromised Need (Query) End-user Search Intermediated Search

15. Types of Information Needs Retrospective (“Retrieval”) –“Searching the past” –Different queries posed against a static collection –Time invariant Prospective (“Filtering”) –“Searching the future” –Static query posed against a dynamic collection –Time dependent

16. Design Strategies Foster human-machine synergy –Exploit complementary strengths –Accommodate shared weaknesses Divide-and-conquer –Divide task into stages with well-defined interfaces –Continue dividing until problems are easily solved Co-design related components –Iterative process of joint optimization

17. Divide and Conquer Strategy: use encapsulation to limit complexity Approach: –Define interfaces (input and output) for each component –Define the functions performed by each component –Study each component in isolation –Repeat the process within components as needed –Make sure that this decomposition makes sense Result: a hierarchical decomposition

18. Process/System Co-Design

19. Human-Machine Synergy Machines are good at: –Doing simple things accurately and quickly –Scaling to larger collections in sublinear time People are better at: –Accurately recognizing what they are looking for –Evaluating intangibles such as “quality” Both are pretty bad at: –Mapping consistently between words and concepts

20. Supporting the Search Process Source Selection Search Query Selection Ranked List Examination Document Delivery Document Query Formulation IR System Query Reformulation and Relevance Feedback Source Reselection NominateChoose Predict

21. Supporting the Search Process Source Selection Search Query Selection Ranked List Examination Document Delivery Document Query Formulation IR System Indexing Index Acquisition Collection

22. Study the IR black box in isolation –Simple behavior: in goes query, out comes documents –Optimize the choice of documents that come out Where to Start? Search Query Ranked List

23. The IR Black Box Documents Query Hits

24. Inside The IR Black Box Documents Query Hits Representation Function Representation Function Query RepresentationDocument Representation Comparison Function Index

25. Search Component Model Comparison Function Representation Function Query Formulation Human Judgment Representation Function Retrieval Status Value Utility Query Information NeedDocument Query RepresentationDocument Representation Query Processing Document Processing

26. What about databases? What are examples of databases? –Banks storing account information –Retailers storing inventories –Universities storing student grades What exactly is a (relational) database? –Think of them as a collection of tables –They model some aspect of “the world”

27. A (Simple) Database Example Student Table Department TableCourse Table Enrollment Table

28. Database Queries What would you want to know from a database? –What classes is John Arrow enrolled in? –Who has the highest grade in LBSC 690? –Who’s in the history department? –Of all the non-CLIS students taking LBSC 690 with a last name shorter than six characters and were born on a Monday, who has the longest email address?

29. Databases vs. IR Other issues Interaction with system Results we get Queries we’re posing What we’re retrieving IRDatabases Issues downplayed.Concurrency, recovery, atomicity are all critical. Interaction is important.One-shot queries. Sometimes relevant, often not. Exact. Always correct in a formal sense. Vague, imprecise information needs (often expressed in natural language). Formally (mathematically) defined queries. Unambiguous. Mostly unstructured. Free text with some metadata. Structured data. Clear semantics based on a formal model.

30. “Bag of Terms” Representation Bag = a “set” that can contain duplicates  “The quick brown fox jumped over the lazy dog’s back”  {back, brown, dog, fox, jump, lazy, over, quick, the, the} Vector = values recorded in any consistent order  {back, brown, dog, fox, jump, lazy, over, quick, the, the}  [1 1 1 1 1 1 1 1 2]

31. Bag of Terms Example The quick brown fox jumped over the lazy dog’s back. Document 1 Document 2 Now is the time for all good men to come to the aid of their party. the quick brown fox over lazy dog back now is time for all good men to come jump aid of their party 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 1 0 1 0 1 1 Term Document 1Document 2 Stopword List

32. Advantages of Ranked Retrieval Closer to the way people think –Some documents are better than others Enriches browsing behavior –Decide how far down the list to go as you read it Allows more flexible queries –Long and short queries can produce useful results

33. Counting Terms Terms tell us about documents –If “rabbit” appears a lot, it may be about rabbits Documents tell us about terms –“the” is in every document -- not discriminating Documents are most likely described well by rare terms that occur in them frequently –Higher “term frequency” is stronger evidence –Low “document frequency” makes it stronger still

34. Document Length Normalization Long documents have an unfair advantage –They use a lot of terms So they get more matches than short documents –And they use the same words repeatedly So they have much higher term frequencies Normalization seeks to remove these effects

35. Problems with “Free Text” Search Homonymy –Terms may have many unrelated meanings –Polysemy (related meanings) is less of a problem Synonymy –Many ways of saying (nearly) the same thing Anaphora –Alternate ways of referring to the same thing

36. Two Ways of Searching Write the document using terms to convey meaning Author Content-Based Query-Document Matching Document Terms Query Terms Construct query from terms that may appear in documents Free-Text Searcher Retrieval Status Value Construct query from available concept descriptors Controlled Vocabulary Searcher Choose appropriate concept descriptors Indexer Metadata-Based Query-Document Matching Query Descriptors Document Descriptors

37. Problems with Controlled Vocabulary New concepts Users and indexers may think differently Using thesauri effectively requires training

38. Behavior Category Minimum Scope

39. Some Examples Read/Ignored, Saved/Deleted, Replied to (Stevens, 1993) Reading time (Morita & Shinoda, 1994; Konstan et al., 1997) Hypertext Link (Brin & Page, 1998)

40. Estimating Authority from Links Authority Hub

41. Problems with Observed Behavior Protecting privacy –What absolute assurances can we provide? –How can we make remaining risks understood? Scalable rating servers –Is a fully distributed architecture practical? Non-cooperative users –How can the effect of spamming be limited?

42. Putting It All Together Free TextBehaviorMetadata Topicality Quality Reliability Cost Flexibility

43. The Big Picture Four Factors, working together –User –Process –System –Collection

44. Course Goals Appreciate IR system capabilities and limitations Understand IR system design & implementation –For a broad range of applications and media Evaluate IR system performance Identify current IR research problems

45. Course Design Text/readings provide background and detail –At least one recommended reading is required Class provides organization and direction –We will not cover every important detail Assignments and project provide experience –The TA can help with the project Final exam helps focus your effort

46. Assumed Background Everyone: –LBSC 690 or INFM 603 or equivalent –Comfortable with learning about technology MIM Students: –Basic systems analysis, scripting languages –Some programming is helpful MLS students: –LBSC 650 and LBSC 670 –LBSC 750 or a subject access course is helpful

47. Grading Assignments (20%) –Mastery of concepts and experience using tools Term project (50%) –Options are described on course Web page Final exam (30%) –In-class exam

48. Handy Things to Know Classes will be videotaped –Available outside my office Office hours: 5 PM Mondays –Or schedule by email, or ask after class Everything is on the Web –At http://www.glue.umd.edu/~oard Doug is most easily reached by email –oard@umd.edu

49. Some Things to Do This Week Assignment 1 –Due at 6 PM next Monday!! At least skim the readings before class –Don’t fall behind! Explore the Web site –Start thinking about the term project