SLIDE 1IS 240 – Spring 2007 Prof. Ray Larson University of California, Berkeley School of Information Tuesday and Thursday 10:30 am - 12:00 pm Spring Principles of Information Retrieval Lecture 2: Basic Concepts

SLIDE 2IS 240 – Spring 2007 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

SLIDE 3IS 240 – Spring 2007 Development of IR Theory and Practice Phase I: circa –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

SLIDE 4IS 240 – Spring 2007 Historical Milestones in IR Research 1958 Statistical Language Properties (Luhn) 1960 Probabilistic Indexing (Maron & Kuhns) 1961 Term association and clustering (Doyle) 1965 Vector Space Model (Salton) 1968 Query expansion (Roccio, Salton) 1972 Statistical Weighting (Sparck-Jones) Poisson Model (Harter, Bookstein, Swanson) 1976 Relevance Weighting (Robertson, Sparck- Jones) 1980 Fuzzy sets (Bookstein) 1981 Probability without training (Croft)

SLIDE 5IS 240 – Spring 2007 Historical Milestones in IR Research (cont.) 1983 Linear Regression (Fox) 1983 Probabilistic Dependence (Salton, Yu) 1985 Generalized Vector Space Model (Wong, Rhagavan) 1987 Fuzzy logic and RUBRIC/TOPIC (Tong, et al.) 1990 Latent Semantic Indexing (Dumais, Deerwester) 1991 Polynomial & Logistic Regression (Cooper, Gey, Fuhr) 1992 TREC (Harman) 1992 Inference networks (Turtle, Croft) 1994 Neural networks (Kwok) 1998 Language Models (Ponte, Croft)

SLIDE 6IS 240 – Spring 2007 Development of Bibliographic Databases Chemical Abstracts Service first produced “Chemical Titles” by computer in Index Medicus from the National Library of Medicine soon followed with the creation of the MEDLARS database in By 1970 Most secondary publications (indexes, abstract journals, etc) were produced by machine

SLIDE 7IS 240 – Spring 2007 Boolean IR Systems Synthex at SDC, 1960 Project MAC at MIT, 1963 (interactive) BOLD at SDC, 1964 (Harold Borko) 1964 New York World’s Fair – Becker and Hayes produced system to answer questions (based on airline reservation equipment) SDC began production for a commercial service in 1967 – ORBIT NASA-RECON (1966) becomes DIALOG 1972 Data Central/Mead introduced LEXIS – Full text of legal information Online catalogs – late 1970’s and 1980’s

SLIDE 8IS 240 – Spring 2007 Experimental IR systems Probabilistic indexing – Maron and Kuhns, 1960 SMART – Gerard Salton at Cornell – Vector space model, 1970’s SIRE at Syracuse I3R – Croft Cheshire I (1990) TREC – 1992 Inquery Cheshire II (1994) MG (1995?) Lemur (2000?)

SLIDE 9IS 240 – Spring 2007 The Internet and the WWW Gopher, Archie, Veronica, WAIS Tim Berners-Lee, 1991 creates WWW at CERN – originally hypertext only Web-crawler Lycos Alta Vista Inktomi Google (and many others)

SLIDE 10IS 240 – Spring 2007 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

SLIDE 11IS 240 – Spring 2007 Research Sources in Information Retrieval ACM Transactions on Information Systems Am. Society for Information Science Journal Document Analysis and IR Proceedings (Las Vegas) Information Processing and Management (Pergammon) Journal of Documentation SIGIR Conference Proceedings TREC Conference Proceedings Much of this literature is now available online

SLIDE 12IS 240 – Spring 2007 Research Systems Software INQUERY (Croft) OKAPI (Robertson) PRISE (Harman) – SMART (Buckley) MG (Witten, Moffat) CHESHIRE (Larson) – LEMUR toolkit (Callan) Lucene (Pederson) Others

SLIDE 13IS 240 – Spring 2007 Background Concepts for IR User Information Needs Controlled Vocabularies (Pre and Post- coordination) Indexing Languages IR definitions and concepts –Documents –Queries –Collections –Evaluation –Relevance (we’ll spend the most time on this today)

SLIDE 14IS 240 – Spring 2007 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?

SLIDE 15IS 240 – Spring 2007 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

SLIDE 16IS 240 – Spring 2007 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.

SLIDE 17IS 240 – Spring 2007 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

SLIDE 18IS 240 – Spring 2007 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

SLIDE 19IS 240 – Spring 2007 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

SLIDE 20IS 240 – Spring 2007 Thesauri A Thesaurus is a collection of selected vocabulary (preferred terms or descriptors) with links among Synonymous, Equivalent, Broader, Narrower and other Related Terms

SLIDE 21IS 240 – Spring 2007 Thesauri (cont.) National and International Standards for Thesauri –ANSI/NISO z American National Standard Guidelines for the Construction, Format and Management of Monolingual Thesauri –ANSI/NISO Draft Standard Z x -- American National Standard Guidelines for Indexes in Information Retrieval –ISO Documentation -- Guidelines for the establishment and development of monolingual thesauri –ISO Documentation -- Guidelines for the establishment and development of multilingual thesauri

SLIDE 22IS 240 – Spring 2007 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.

SLIDE 23IS 240 – Spring 2007 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

SLIDE 24IS 240 – Spring 2007 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.

SLIDE 25IS 240 – Spring 2007 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

SLIDE 26IS 240 – Spring 2007 Central Concepts in IR Documents Queries Collections Evaluation Relevance

SLIDE 27IS 240 – Spring 2007 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.

SLIDE 28IS 240 – Spring 2007 Collection A collection is some physical or logical aggregation of documents –A database –A Library –A index? –Others?

SLIDE 29IS 240 – Spring 2007 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 (and probably aren’t) accurate expressions of the information need –Differences between conversation with a person and formal query expression

SLIDE 30IS 240 – Spring 2007 Evaluation Why Evaluate? What to Evaluate? How to Evaluate?

SLIDE 31IS 240 – Spring 2007 Why Evaluate? Determine if the system is desirable Make comparative assessments Others?

SLIDE 32IS 240 – Spring 2007 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.

SLIDE 33IS 240 – Spring 2007 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

SLIDE 34IS 240 – Spring 2007 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...

SLIDE 35IS 240 – Spring 2007 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

SLIDE 36IS 240 – Spring 2007 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?

SLIDE 37IS 240 – Spring 2007 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))

SLIDE 38IS 240 – Spring 2007 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

SLIDE 39IS 240 – Spring 2007 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

SLIDE 40IS 240 – Spring 2007 A. Gages Measure Degree Extent Judgement Estimate Appraisal Relation

SLIDE 41IS 240 – Spring 2007 B. Aspect Utility Matching Informativeness Satisfaction Appropriateness Usefulness Correspondence

SLIDE 42IS 240 – Spring 2007 C. Object judged Document Document representation Reference Textual form Information provided Fact Article

SLIDE 43IS 240 – Spring 2007 D. Frame of reference Question Question representation Research stage Information need Information used Point of view request

SLIDE 44IS 240 – Spring 2007 E. Assessor Requester Intermediary Expert User Person Judge Information specialist

SLIDE 45IS 240 – Spring 2007 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

SLIDE 46IS 240 – Spring 2007 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.”

SLIDE 47IS 240 – Spring 2007 Froelich Centrality and inadequacy of Topicality as the basis for relevance Suggestions for a synthesis of views

SLIDE 48IS 240 – Spring 2007 Janes’ View Topicality Pertinence Relevance Utility Satisfaction

SLIDE 49IS 240 – Spring 2007 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

SLIDE 50IS 240 – Spring 2007 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