1. 2010.01.25 - SLIDE 1IS 240 – Spring 2010 Prof. Ray Larson University of California, Berkeley School of Information Principles of Information Retrieval Lecture 2: Concepts and Elements

2. 2010.01.25 - SLIDE 2IS 240 – Spring 2010 Review –IR History, Readings Central Concepts in IR –Documents –Queries –Collections –Evaluation –Relevance Elements of IR System

3. 2010.01.25 - SLIDE 3IS 240 – Spring 2010 Review – IR History Journal Indexes “Information Explosion” following WWII –Cranfield Studies of indexing languages and information retrieval –Paper by Joyce and Needham on Thesauri for IR. –Development of bibliographic databases Chemical Abstracts Index Medicus -- production and Medlars searching

4. 2010.01.25 - SLIDE 4IS 240 – Spring 2010 Development of IR Theory and Practice Phase I: circa 1955-1975 –Foundational research –Fundamental IR concepts advanced in research environment Phase II: 1975 to present –Slow adoption of IR research into operational systems –Accelerated in mid-1990’s due to WWW search engines

5. 2010.01.25 - SLIDE 5IS 240 – Spring 2010 Information Retrieval – Historical View Boolean model, statistics of language (1950’s) Vector space model, probablistic indexing, relevance feedback (1960’s) Probabilistic querying (1970’s) Fuzzy set/logic, evidential reasoning (1980’s) Regression, neural nets, inference networks, latent semantic indexing, TREC (1990’s) DIALOG, Lexus-Nexus, STAIRS (Boolean based) Information industry (O($B)) Verity TOPIC (fuzzy logic) Internet search engines (O($100B?)) (vector space, probabilistic) ResearchIndustry

6. 2010.01.25 - SLIDE 6IS 240 – Spring 2010 Readings and Discussion Joyce and Needham –Assigned index terms or Automatic? –Lattice theory (extension of Boolean algebra to partially ordered sets) –Notice the Vector suggestion? Luhn –Document/Document similarity calculations based on term frequency –KWIC indexes Doyle –Term associations

7. 2010.01.25 - SLIDE 7IS 240 – Spring 2010 Readings (Next time) Saracevic –Relevance Maron and Kuhns –Probabilistic Indexing and matching Cleverdon –Evaluation Salton and Lesk –The SMART system Hutchins –Aboutness and indexing

8. 2010.01.25 - SLIDE 8IS 240 – Spring 2010 Documents What do we mean by a document? –Full document? –Document surrogates? –Pages? Buckland (JASIS, Sept. 1997) “What is a Document” Bates (JASIST, June 2006) “Fundamental Forms of Information” Are IR systems better called Document Retrieval systems? A document is a representation of some aggregation of information, treated as a unit.

9. 2010.01.25 - SLIDE 9IS 240 – Spring 2010 Collection A collection is some physical or logical aggregation of documents –A database –A Library –A index? –Others?

10. 2010.01.25 - SLIDE 10IS 240 – Spring 2010 Queries A query is some expression of a user’s information needs Can take many forms –Natural language description of need –Formal query in a query language Queries may not be accurate expressions of the information need –Differences between conversation with a person and formal query expression

11. 2010.01.25 - SLIDE 11IS 240 – Spring 2010 User Information Need Why build IR systems at all? People have different and highly varied needs for information People often do not know what they want, or may not be able to express it in a usable form –Filling the gaps in Boulding’s “Image” How to satisfy these user needs for information?

12. 2010.01.25 - SLIDE 12IS 240 – Spring 2010 Controlled Vocabularies Vocabulary control is the attempt to provide a standardized and consistent set of terms (such as subject headings, names, classifications, or the thesauri discussed by Joyce and Needham) with the intent of aiding the searcher in finding information. Controlled vocabularies are a kind of metadata: –Data about data –Information about information

13. 2010.01.25 - SLIDE 13IS 240 – Spring 2010 Pre- and Postcoordination Precoordination relies on the indexer (librarian, etc.) to construct some adequate representation of the meaning of a document. Postcoordination relies on the user or searcher to combine more atomic concepts in the attempt to describe the documents that would be considered relevant.

14. 2010.01.25 - SLIDE 14IS 240 – Spring 2010 Structure of an IR System Search Line Interest profiles & Queries Documents & data Rules of the game = Rules for subject indexing + Thesaurus (which consists of Lead-In Vocabulary and Indexing Language Storage Line Potentially Relevant Documents Comparison/ Matching Store1: Profiles/ Search requests Store2: Document representations Indexing (Descriptive and Subject) Formulating query in terms of descriptors Storage of profiles Storage of Documents Information Storage and Retrieval System Adapted from Soergel, p. 19

15. 2010.01.25 - SLIDE 15IS 240 – Spring 2010 Uses of Controlled Vocabularies Library Subject Headings, Classification and Authority Files. Commercial Journal Indexing Services and databases Yahoo, and other Web classification schemes Online and Manual Systems within organizations –SunSolve –MacArthur

16. 2010.01.25 - SLIDE 16IS 240 – Spring 2010 Types of Indexing Languages Uncontrolled Keyword Indexing Folksonomies –Uncontrolled but somewhat structured) Indexing Languages –Controlled, but not structured Thesauri –Controlled and Structured Classification Systems –Controlled, Structured, and Coded Faceted Classification Systems and Thesauri

17. 2010.01.25 - SLIDE 17IS 240 – Spring 2010 Thesauri A Thesaurus is a collection of selected vocabulary (preferred terms or descriptors) with links among Synonymous, Equivalent, Broader, Narrower and other Related Terms

18. 2010.01.25 - SLIDE 18IS 240 – Spring 2010 Development of a Thesaurus Term Selection. Merging and Development of Concept Classes. Definition of Broad Subject Fields and Subfields. Development of Classificatory structure Review, Testing, Application, Revision.

19. 2010.01.25 - SLIDE 19IS 240 – Spring 2010 Categorization Summary Processes of categorization underlie many of the issues having to do with information organization Categorization is messier than our computer systems would like Human categories have graded membership, consisting of family resemblances. Family resemblance is expressed in part by which subset of features are shared It is also determined by underlying understandings of the world that do not get represented in most systems

20. 2010.01.25 - SLIDE 20IS 240 – Spring 2010 Classification Systems A classification system is an indexing language often based on a broad ordering of topical areas. Thesauri and classification systems both use this broad ordering and maintain a structure of broader, narrower, and related topics. Classification schemes commonly use a coded notation for representing a topic and it’s place in relation to other terms.

21. 2010.01.25 - SLIDE 21IS 240 – Spring 2010 Classification Systems (cont.) Examples: –The Library of Congress Classification System –The Dewey Decimal Classification System –The ACM Computing Reviews Categories –The American Mathematical Society Classification System

22. 2010.01.25 - SLIDE 22IS 240 – Spring 2010 Evaluation Why Evaluate? What to Evaluate? How to Evaluate?

23. 2010.01.25 - SLIDE 23IS 240 – Spring 2010 Why Evaluate? Determine if the system is desirable Make comparative assessments Others?

24. 2010.01.25 - SLIDE 24IS 240 – Spring 2010 What to Evaluate? How much of the information need is satisfied. How much was learned about a topic. Incidental learning: –How much was learned about the collection. –How much was learned about other topics. How inviting the system is.

25. 2010.01.25 - SLIDE 25IS 240 – Spring 2010 What to Evaluate? What can be measured that reflects users’ ability to use system? (Cleverdon 66) –Coverage of Information –Form of Presentation –Effort required/Ease of Use –Time and Space Efficiency –Recall proportion of relevant material actually retrieved –Precision proportion of retrieved material actually relevant effectiveness

26. 2010.01.25 - SLIDE 26IS 240 – Spring 2010 Relevance In what ways can a document be relevant to a query? –Answer precise question precisely. –Partially answer question. –Suggest a source for more information. –Give background information. –Remind the user of other knowledge. –Others...

27. 2010.01.25 - SLIDE 27IS 240 – Spring 2010 Relevance “Intuitively, we understand quite well what relevance means. It is a primitive “y’ know” concept, as is information for which we hardly need a definition. … if and when any productive contact [in communication] is desired, consciously or not, we involve and use this intuitive notion or relevance.” »Saracevic, 1975 p. 324

28. 2010.01.25 - SLIDE 28IS 240 – Spring 2010 Relevance How relevant is the document –for this user, for this information need. Subjective, but Measurable to some extent –How often do people agree a document is relevant to a query? How well does it answer the question? –Complete answer? Partial? –Background Information? –Hints for further exploration?

29. 2010.01.25 - SLIDE 29IS 240 – Spring 2010 Relevance Research and Thought Review to 1975 by Saracevic Reconsideration of user-centered relevance by Schamber, Eisenberg and Nilan, 1990 Special Issue of JASIS on relevance (April 1994, 45(3))

30. 2010.01.25 - SLIDE 30IS 240 – Spring 2010 Saracevic Relevance is considered as a measure of effectiveness of the contact between a source and a destination in a communications process –Systems view –Destinations view –Subject Literature view –Subject Knowledge view –Pertinence –Pragmatic view

31. 2010.01.25 - SLIDE 31IS 240 – Spring 2010 Define your own relevance Relevance is the (A) gage of relevance of an (B) aspect of relevance existing between an (C) object judged and a (D) frame of reference as judged by an (E) assessor Where… From Saracevic, 1975 and Schamber 1990

32. 2010.01.25 - SLIDE 32IS 240 – Spring 2010 A. Gages Measure Degree Extent Judgement Estimate Appraisal Relation

33. 2010.01.25 - SLIDE 33IS 240 – Spring 2010 B. Aspect Utility Matching Informativeness Satisfaction Appropriateness Usefulness Correspondence

34. 2010.01.25 - SLIDE 34IS 240 – Spring 2010 C. Object judged Document Document representation Reference Textual form Information provided Fact Article

35. 2010.01.25 - SLIDE 35IS 240 – Spring 2010 D. Frame of reference Question Question representation Research stage Information need Information used Point of view request

36. 2010.01.25 - SLIDE 36IS 240 – Spring 2010 E. Assessor Requester Intermediary Expert User Person Judge Information specialist

37. 2010.01.25 - SLIDE 37IS 240 – Spring 2010 Schamber, Eisenberg and Nilan “Relevance is the measure of retrieval performance in all information systems, including full-text, multimedia, question- answering, database management and knowledge-based systems.” Systems-oriented relevance: Topicality User-Oriented relevance Relevance as a multi-dimensional concept

38. 2010.01.25 - SLIDE 38IS 240 – Spring 2010 Schamber, et al. Conclusions “Relevance is a multidimensional concept whose meaning is largely dependent on users’ perceptions of information and their own information need situations Relevance is a dynamic concept that depends on users’ judgements of the quality of the relationship between information and information need at a certain point in time. Relevance is a complex but systematic and measureable concept if approached conceptually and operationally from the user’s perspective.”

39. 2010.01.25 - SLIDE 39IS 240 – Spring 2010 Froelich Centrality and inadequacy of Topicality as the basis for relevance Suggestions for a synthesis of views

40. 2010.01.25 - SLIDE 40IS 240 – Spring 2010 Janes’ View Topicality Pertinence Relevance Utility Satisfaction

41. 2010.01.25 - SLIDE 41IS 240 – Spring 2010 Operational Definition of Relevance From the point of view of IR evaluation (as typified in TREC and other IR evaluation efforts) –Relevance is a term used for the relationship between a users information need and the contents of a document where the user determines whether or not the contents are responsive to his or her information need

42. 2010.01.25 - SLIDE 42IS 240 – Spring 2010 IR Systems Elements of IR Systems Overview – we will examine each of these in further detail later in the course

43. 2010.01.25 - SLIDE 43IS 240 – Spring 2010 What is Needed? What software components are needed to construct an IR system? One way to approach this question is to look at the information and data, and see what needs to be done to allow us to do IR

44. 2010.01.25 - SLIDE 44IS 240 – Spring 2010 What, again, is the goal? Goal of IR is to retrieve all and only the “relevant” documents in a collection for a particular user with a particular need for information –Relevance is a central concept in IR theory OR The goal is to search large document collections (millions of documents) to retrieve small subsets relevant to the user’s information need

45. 2010.01.25 - SLIDE 45IS 240 – Spring 2010 Collections of Documents… Documents –A document is a representation of some aggregation of information, treated as a unit. Collection –A collection is some physical or logical aggregation of documents Let’s take the simplest case, and say we are dealing with a computer file of plain ASCII text, where each line represents the “UNIT” or document.

46. 2010.01.25 - SLIDE 46IS 240 – Spring 2010 How to search that collection? Manually? –Cat, more Scan for strings? –Grep Extract individual words to search??? –“tokenize” (a unix pipeline) tr -sc ’A-Za-z’ ’\012’ < TEXTFILE | sort | uniq –c –See “Unix for Poets” by Ken Church Put it in a DBMS and use pattern matching there… –assuming the lines are smaller than the text size limits for the DBMS

47. 2010.01.25 - SLIDE 47IS 240 – Spring 2010 What about VERY big files? Scanning becomes a problem The nature of the problem starts to change as the scale of the collection increases A variant of Parkinson’s Law that applies to databases is: –Data expands to fill the space available to store it

48. 2010.01.25 - SLIDE 48IS 240 – Spring 2010 The IR Approach Extract the words (or tokens) along with references to the record they come from –I.e. build an inverted file of words or tokens Is this enough?

49. 2010.01.25 - SLIDE 49 Document Processing Steps

50. 2010.01.25 - SLIDE 50IS 240 – Spring 2010 What about … The structure information, POS info, etc.? Where and how to store this information? –DBMS? –XML structured documents? –Special file structures DBMS File types (ISAM, VSAM, B-Tree, etc.) PAT trees Hashed files (Minimal, Perfect and Both) Inverted files How to get it back out of the storage –And how to map to the original document location?

51. 2010.01.25 - SLIDE 51IS 240 – Spring 2010 Structure of an IR System Search Line Interest profiles & Queries Documents & data Rules of the game = Rules for subject indexing + Thesaurus (which consists of Lead-In Vocabulary and Indexing Language Storage Line Potentially Relevant Documents Comparison/ Matching Store1: Profiles/ Search requests Store2: Document representations Indexing (Descriptive and Subject) Formulating query in terms of descriptors Storage of profiles Storage of Documents Information Storage and Retrieval System Adapted from Soergel, p. 19

52. 2010.01.25 - SLIDE 52IS 240 – Spring 2010 What next? User queries –How do we handle them? –What sort of interface do we need? –What processing steps once a query is submitted? Matching –How (and what) do we match?

53. 2010.01.25 - SLIDE 53IS 240 – Spring 2010 From Baeza-Yates: Modern IR… User Interface Text operations indexing DB Man. Text Db index Query operations Searching Ranking

54. 2010.01.25 - SLIDE 54IS 240 – Spring 2010 Query Processing In order to correctly match queries and documents they must go through the same text processing steps as the documents did when they were stored In effect, the query is treated like it was a document Exceptions (of course) include things like structured query languages that must be parsed to extract the search terms and requested operations from the query –The search terms must still go through the same text process steps as the document…

55. 2010.01.25 - SLIDE 55IS 240 – Spring 2010 Query Processing Once the text is in a form to match to the indexes then the fun begins –What approach to use? Boolean? Extended Boolean? Ranked –Fuzzy sets? –Vector? –Probabilistic? –Language Models? –Neural nets? Most of the next few weeks will be looking at these different approaches

56. 2010.01.25 - SLIDE 56IS 240 – Spring 2010 Display and formatting Have to present the the results to the user Lots of different options here, mostly governed by –How the actual document is stored –And whether the full document or just the metadata about it is presented