1. 10/21/98Organization of Information in Collections Subject Access to Collections: Introduction University of California, Berkeley School of Information Management and Systems SIMS 245: Organization of Information In Collections

2. 10/21/98Organization of Information in Collections Review Review of Description 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

3. 10/21/98Organization of Information in Collections Indexing Languages and Thesauri Origins and Uses of Controlled Vocabularies for Information Retrieval Types of Indexing Languages, Thesauri and Classification Systems

4. 10/21/98Organization of Information in Collections Controlled Vocabularies Vocabulary control is the attempt to provide a standardized and consistent set of terms (such as subject headings, names, classifications, etc.) with the intent of aiding the searcher in finding information.

5. 10/21/98Organization of Information in Collections What is a “Controlled Vocabulary” “The greatest problem of today is how to teach people to ignore the irrelevant, how to refuse to know things, before they are suffocated. For too many facts are as bad as none at all.” (W.H. Auden) Similarly, there are too many ways of expressing or explaining the topic of a document. Controlled vocabularies are sets of Rules for topic identification and indexing, and a THESAURUS, which consists of “lead-in vocabulary” and an limited and selective “Indexing Language” sometimes with special coding or structures.

6. 10/21/98Organization of Information in Collections Uses of Controlled Vocabularies Library Subject Headings, Classification and Name Authority Files. Commercial Journal Indexing Services and databases Yahoo, and other Web classification schemes Online and Manual Systems within organizations –SunSolve –MacArthur

7. 10/21/98Organization of Information in Collections Name Authority Files ID:NAFL8057230 ST:p EL:n STH:a MS:c UIP:a TD:19910821174242 KRC:a NMU:a CRC:c UPN:a SBU:a SBC:a DID:n DF:05-14-80 RFE:a CSC: SRU:b SRT:n SRN:n TSS: TGA:? ROM:? MOD: VST:d 08-21-91 Other Versions: earlier 040 DLC$cDLC$dDLC$dOCoLC 053 PR6005.R517 100 10 Creasey, John 400 10 Cooke, M. E. 400 10 Cooke, Margaret,$d1908-1973 400 10 Cooper, Henry St. John,$d1908-1973 400 00 Credo,$d1908-1973 400 10 Fecamps, Elise 400 10 Gill, Patrick,$d1908-1973 400 10 Hope, Brian,$d1908-1973 400 10 Hughes, Colin,$d1908-1973 400 10 Marsden, James 400 10 Matheson, Rodney 400 10 Ranger, Ken 400 20 St. John, Henry,$d1908-1973 400 10 Wilde, Jimmy 500 10 $wnnnc$aAshe, Gordon,$d1908-1973 Different names for the same person

8. 10/21/98Organization of Information in Collections Name Authority Files ID:NAFO9114111 ST:p EL:n STH:a MS:n UIP:a TD:19910817053048 KRC:a NMU:a CRC:c UPN:a SBU:a SBC:a DID:n DF:06-03-91 RFE:a CSC:c SRU:b SRT:n SRN:n TSS: TGA:? ROM:? MOD: VST:d 08-19-91 040 OCoLC$cOCoLC 100 10 Marric, J. J.,$d1908-1973 500 10 $wnnnc$aCreasey, John 663 Works by this author are entered under the name used in the item. For a listing of other names used by this author, search also under$bCrease y, John 670 OCLC 13441825: His Gideon's day, 1955$b(hdg.: Creasey, John; usage: J.J. Marric) 670 LC data base, 6/10/91$b(hdg.: Creasey, John; usage: J.J. Marric) 670 Pseuds. and nicknames dict., c1987$b(Creasey, John, 1908-1973; Britis h author; pseud.: Marric, J. J.)

9. 10/21/98Organization of Information in Collections Name authority files ID:NAFL8166762 ST:p EL:n STH:a MS:c UIP:a TD:19910604053124 KRC:a NMU:a CRC:c UPN:a SBU:a SBC:a DID:n DF:08-20-81 RFE:a CSC: SRU:b SRT:n SRN:n TSS: TGA:? ROM:? MOD: VST:d 06-06-91 Other Versions: earlier 040 DLC$cDLC$dDLC$dOCoLC 100 10 Butler, William Vivian,$d1927- 400 10 Butler, W. V.$q(William Vivian),$d1927- 400 10 Marric, J. J.,$d1927- 670 His The durable desperadoes, 1973. 670 His The young detective's handbook, c1981:$bt.p. (W.V. Butler) 670 His Gideon's way, 1986:$bCIP t.p. (William Vivian Butler writing as J.J. Marric) Different people writing with the same name

10. 10/21/98Organization of Information in Collections Indexing Languages An index is a systematic guide designed to indicate topics or features of documents in order to facilitate retrieval of documents or parts of documents. An Indexing language is the set of terms used in an index to represent topics or features of documents, and the rules for combining or using those terms.

11. 10/21/98Organization of Information in Collections Types of Indexing Languages Uncontrolled Keyword Indexing Indexing Languages –Controlled, but not structured Thesauri –Controlled and Structured Classification Systems –Controlled, Structured, and Coded Faceted Classification Systems

12. 10/21/98Organization of Information in Collections Indexing Languages Library of Congress Subject Headings Yellow Pages Topics Wilson Indexes (“Reader’s Guide”)

13. 10/21/98Organization of Information in Collections Controlled Vocabulary Start with the text of the document Attempt to “control” or regularize: –The concepts expressed within mutually exclusive exhaustive –The language used to express those concepts limit the normal linguistic variations regulate word order and structure of phrases reduce the number of synonyms or near-synonyms Also, provide cross-references between concepts and their expression. See Bates, 1988

14. 10/21/98Organization of Information in Collections Subject Headings vs. Descriptors Describe the contents of an entire document Designed to be looked up in an alphabetical index –Look up document under its heading Few (1-5) headings per document Describe one concept within a document Designed to be used in Boolean searching –Combine to describe the desired document Many (5-25) descriptors per document

15. 10/21/98Organization of Information in Collections Subject Heading vs. Descriptor Example WILSONLINE –Athletes –Athletes--Heath&Hygiene –Athletes--Nutrition –Athletes--Physical Exams –… –Athletics –Athletics -- Administration –Athletics -- Equipment -- Catalogs –… –Sports -- Accidents and injuries –Sports -- Accidents and injuries -- prevention ERIC –Athletes –Athletic Coaches –Athletic Equipment –Athletic Fields –Athletics –… –Sports psychology –Sportsmanship

16. 10/21/98Organization of Information in Collections Assigning Headings vs. Descriptors Subject headings -- assign one (or a few) complex heading(s) to the document Descriptors -- mix and match –How would we describe recipes using each technique?

17. 10/21/98Organization of Information in Collections 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. 10/21/98Organization of Information in Collections Thesauri (cont.) National and International Standards for Thesauri –ANSI/NISO z39.19--1994 -- American National Standard Guidelines for the Construction, Format and Management of Monolingual Thesauri –ANSI/NISO Draft Standard Z39.4-199x -- American National Standard Guidelines for Indexes in Information Retrieval –ISO 2788 -- Documentation -- Guidelines for the establishment and development of monolingual thesauri –ISO 5964-- Documentation -- Guidelines for the establishment and development of multilingual thesauri

19. 10/21/98Organization of Information in Collections Thesauri (cont.) Examples: –The ERIC Thesaurus of Descriptors –The Art and Architecture Thesaurus –The Medical Subject Headings (MESH) of the National Library of Medicine

20. 10/21/98Organization of Information in Collections 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.

21. 10/21/98Organization of Information in Collections 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

22. 10/21/98Organization of Information in Collections 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.

23. 10/21/98Organization of Information in Collections 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

24. 10/21/98Organization of Information in Collections Classification Schemes Classify possible concepts. Goals: –Completely distinct conceptual categories (mutually exclusive) –Complete coverage of conceptual categories (exhaustive)

25. 10/21/98Organization of Information in Collections Hierarchical Classification Traditional “family-tree” –Each category is successively broken down into smaller and smaller subdivisions –Each level divided out by a “character of division”. Also known as a feature. Example: distinguish Literature based on: –Language –Genre –Time Period

26. 10/21/98Organization of Information in Collections Hierarchical Classification Literature SpanishFrenchEnglish DramaPoetryProse 18th17th16th DramaPoetryProse 19th18th17th16th19th...

27. 10/21/98Organization of Information in Collections Labeled Categories for Hierarchical Classification LITERATURE –100 English Literature 110 English Prose –English Prose 16th Century –English Prose 17th Century –English Prose 18th Century –... 111 English Poetry –121 English Poetry 16th Century –122 English Poetry 17th Century –... 112 English Drama –130 English Drama 16th Century –… –200 French Literature

28. 10/21/98Organization of Information in Collections Faceted Classification Create a separate, free-standing list for each characteristic of division (feature). Combine features to create a classification.

29. 10/21/98Organization of Information in Collections Faceted Classification and Labeled Catgories A Language –a English –b French –c Spanish B Genre –a Prose –b Poetry –c Drama C Period –a 16th Century –b 17th Century –c 18th Century –d 19th Century Aa English Literature AaBa English Prose AaBaCa English Prose 16th Century AbBbCd French Poetry 19th Century BbCd Drama 19th Century

30. 10/21/98Organization of Information in Collections How to use such classification structures? How to look through them? How to use them in search?

31. 10/21/98Organization of Information in Collections Automatic Indexing and Classification Automatic indexing is typically the simple deriving of keywords from a document and providing access to all of those words. More complex Automatic Indexing Systems attempt to select controlled vocabulary terms based on terms in the document. Automatic classification attempts to automatically group similar documents using either: –A fully automatic clustering method. –An established classification scheme and set of documents already indexed by that scheme.

32. 10/21/98Organization of Information in Collections Agglomerative Clustering ABCDEFGHIABCDEFGHI

33. 10/21/98Organization of Information in Collections Agglomerative Clustering ABCDEFGHIABCDEFGHI

34. 10/21/98Organization of Information in Collections Agglomerative Clustering ABCDEFGHIABCDEFGHI

35. 10/21/98Organization of Information in Collections Hierarchical Methods 2.4 3.4.2 4.3.3.3 5.1.4.4.1 1 2 3 4 Single Link Dissimilarity Matrix Hierarchical methods: Polythetic, Usually Exclusive, Ordered Clusters are order-independent

36. 10/21/98Organization of Information in Collections Threshold =.1 Single Link Dissimilarity Matrix 2.4 3.4.2 4.3.3.3 5.1.4.4.1 1 2 3 4 2 0 3 0 0 4 0 0 0 5 1 0 0 1 1 2 3 4 2 1 3 5 4

37. 10/21/98Organization of Information in Collections Threshold =.2 2.4 3.4.2 4.3.3.3 5.1.4.4.1 1 2 3 4 2 0 3 0 1 4 0 0 0 5 1 0 0 1 1 2 3 4 2 1 3 5 4

38. 10/21/98Organization of Information in Collections Threshold =.3 2.4 3.4.2 4.3.3.3 5.1.4.4.1 1 2 3 4 2 0 3 0 1 4 1 1 1 5 1 0 0 1 1 2 3 4 2 1 3 5 4

39. 10/21/98Organization of Information in Collections Clustering Agglomerative methods: Polythetic, Exclusive or Overlapping, Unordered clusters are order-dependent. Doc 1. Select initial centers (I.e. seed the space) 2. Assign docs to highest matching centers and compute centroids 3. Reassign all documents to centroid(s) Rocchio’s method

40. 10/21/98Organization of Information in Collections Automatic Class Assignment Doc Search Engine 1. Create pseudo-documents representing intellectually derived classes. 2. Search using document contents 3. Obtain ranked list 4. Assign document to N categories ranked over threshold. OR assign to top-ranked category Automatic Class Assignment: Polythetic, Exclusive or Overlapping, usually ordered clusters are order-independent, usually based on an intellectually derived scheme

41. 10/21/98Organization of Information in Collections K-Means Clustering 1 Create a pair-wise similarity measure 2 Find K centers using agglomerative clustering –take a small sample –group bottom up until K groups found 3 Assign each document to nearest center, forming new clusters 4 Repeat 3 as necessary

42. 10/21/98Organization of Information in Collections Scatter/Gather Cutting, Pedersen, Tukey & Karger 92, 93 Hearst & Pedersen 95 Cluster sets of documents into general “themes”, like a table of contents Display the contents of the clusters by showing topical terms and typical titles User chooses subsets of the clusters and re-clusters the documents within Resulting new groups have different “themes”

43. 10/21/98Organization of Information in Collections S/G Example: query on “star” Encyclopedia text 14 sports 8 symbols47 film, tv 68 film, tv (p) 7 music 97 astrophysics 67 astronomy(p)12 steller phenomena 10 flora/fauna 49 galaxies, stars 29 constellations 7 miscelleneous Clustering and re-clustering is entirely automated