Assignment #2 Group F 1

Part A:

1. User guide

This database is meant primarily for library science students researching topics

relating to the storage and retrieval of information. The database contains a variety of

articles that deal with the different aspects of information retrieval, including

organization of information, information systems, and many others from various points of

view. Searches can be conducted using a variety fields that have identifying information

about the articles, including title, author, and citation.

Analytical searches of the articles can be accomplished through natural language

searches in the abstract and title fields, and controlled vocabulary searches in the post-co

and pre-co fields. For natural language searches, the user may use terms that they believe

would be found in relevant articles. For searches within the controlled vocabulary of the

pre-co and post-co fields, the user may view the query choices available within each field

by clicking on the field being searched and then viewing the browser by hitting the F3

key, thus opening the inverted file. By viewing the browser choices, the user can be sure

that the terms used will have results. If the user does not confine their searches of pre-co

and post-co fields to the valid terms, the query will most likely not retrieve any

documents. In creating the controlled vocabulary for the post-co fields, the user must

remember that the database is designed for articles in the realm of Information Science.

Therefore, post-co terms such as “history” and “navigation” will yield documents within

the context of Information Science, for instance, history of information science or

navigation of information systems.

Assignment #2 Group F 2

When searching by title or abstract, the user is free to conduct a natural language

search. However, this can lead to false drops as some titles and abstracts include words

that are not necessarily indicative of the aboutness of an article. When searching in the

pre-co field, the user is urged to use the inverted file as the main access points to

documents. This way, they can see how the documents are divided and search by the

terms that they believe are most indicative of the information that they are seeking. The

terms shown in the choices are all the terms that were assigned to the articles as

indicators of what the article is mainly about. For example if a user is interested in data

storage relating to information retrieval they would pick the term =information retrieval

data storage from the browser choices.

The terms in the title and abstract fields are taken directly from the articles and,

therefore, terms are both plural and singular. It is suggested that the user can truncate the

word if it is unclear as to how the word might have been used. For instance, when

seeking documents about relevance within information retrieval, some documents might

have the term “relevant” while others use “relevance”. In order to increase the likelihood

of retrieving relevant documents, the user should search for the term “relevan*” which

will bring up those documents using either term. In the post-co field, many of the terms

are in the plural or with the –ing ending. Words that are plural will not be retrieved if

they are searched in the singular unless they include truncation. If the term being

searched is “procedure” the query will result in no records retrieved because the term is

in the plural form in the database. In order to retrieve the record the term used would also

have to be in the plural or singular with the truncation, procedure*. [The use of F3

guarantees the selection of a valid term.]

Assignment #2 Group F 3

For analytical searches, it is recommended that the user use the post-co field.

Searching the titles of the articles does not aggregate as well and may not retrieve all of

the relevant records, as many titles are superficially unrelated to the aboutness of a

document. The abstract may give the user better aggregation, but may also not retrieve

documents that may be relevant if the term being searched was not a part of the abstract.

The post-co terms are indicative of the main subjects discussed in the article and so may

provide the user with better results. The post-co field is especially good for browsing the

articles via the inverted file. [The abstract, however, may provide access to more

specific topics not covered by the controlled vocabulary.]

If a query results in a large number of records retrieved, then it would be best to

do a search using multiple terms. For example, when conducting a search on information

retrieval, many documents are retrieved. In order to narrow the search, another term can

be included to indicate which aspect of information retrieval is being sought. When

adding another term it is important that the user remember to use the & symbol rather

than simply list the terms. For example a search for information retrieval and artificial

intelligence would be done using =information retrieval & =artificial intelligence. [You

could also talk about what to do when few or no records are retrieved, perhaps

mentioning the use of OR (“/”).]

Generally a good job with the User Guide. Providing more examples would improve it.

2. Textbase Structure

Textbase: C:\MLIS202\articles\assn2Created: 4/2/2005 9:18:01 AMModified: 4/8/2005 11:35:05 PM

Field Summary: 1. DOC_NO: Automatic Number(next avail=21, increm=1), Term 2. AUTHOR: Text, Term & Word

Assignment #2 Group F 4

3. TITLE: Text, Word

4. CITE: Text, Term & Word 5. ABSTRACT: Text, Word 6. POSTCO: Text, Term Validation: valid-list 7. PRECO: Text, Term Validation: valid-list

Log file enabled, showing 'DOC_NO'Leading articles: a an the Stop words: a an and by for from in of the to

Textbase Defaults: Default indexing mode: SHARED IMMEDIATE Default sort order: <none>Textbase passwords: Master password = '' 0 Access passwords: No Silent password

3. a. Validation list for Preco

Term index for field 'PRECO', textbase 'assn2', 4/18/2005 6:27:24 AM:

1 Abstracting Techniques1 Classification Cognitive Aspects1 Indexing Techniques2 Indexing Techniques Testing1 information extraction multiple input output devices1 information retrieval artificial intelligence1 information retrieval artificial intelligence introduction [1]1 information retrieval boolean notation1 information retrieval browsing online searching1 information retrieval data storage1 information retrieval data storage obsolescence1 information retrieval debugging errors1 information retrieval development of interfaces [2]1 information retrieval expert systems1 information retrieval imental [oops] model bibliographic record1 information retrieval individual differences1 information retrieval individual differences humanities1 information retrieval natural language understanding2 information retrieval qualitative simulation online catalog5 Information Retrieval Relevance1 Information Retrieval Skills Instruction Heuristic1 Information Retrieval Systems Design Interface [2]2 Information Retrieval Systems Design Theory

1 Information Retrieval Systems End Users Searching2 Information Retrieval Systems User Queries1 information retrieval techniques online searching [3]1 Information Retrieval Theory1 Information Science1 information seeking authors [?]

Assignment #2 Group F 5

6 Information Seeking Behavior1 Information Seeking Behavior Browsing Effectiveness1 Information Seeking Behavior Decision Making Think Aloud1 Information Seeking Behavior Trial and Error1 information seeking end user interfaces [2]1 information seeking history1 information seeking multicultural needs2 information seeking multimedia source1 information seeking new model research1 Information Seeking Online Searching Strategies [3]1 intelligent access knowledge representing2 interfaces human computer interaction [2]1 mental model online catalog [4]1 mental model online catalog law1 online searching berrypicking1 online searching interface design [2]1 Query Keywords Natural Language1 Reference Services Interview Techniques1 Reference Services User Queries1 searching approach logical operators1 searching online interfaces types of browsing [2]1 searching user interfaces world wide web [2]

Total number of keys: 51

b. Validation list for Postco

Term index for field 'POSTCO', textbase 'assn2', 4/18/2005 6:26:49 AM:

1 abstracting1 algorithm1 analysis2 articulation1 artificial intelligence1 authors1 berrypicking1 bibliographic records

2 boolean logic based information1 browsing1 cyberspace1 data mapping2 data storage1 decision making1 document similarity1 document value2 effectiveness1 electronic storage2 end users1 experiment1 expert systems1 exploratory process1 fallout1 heuristic1 history

Assignment #2 Group F 6

1 human computer interaction [1]1 humanities3 indexing3 information access1 information extraction1 information organization1 information processing11 information retrieval3 information retrieval systems1 information science4 information seeking1 instruction1 interaction [6]1 interface [1]1 interfaces [1]1 keywords2 knowledge representation1 law models [I’m not sure what this means.]2 memory2 mental model1 meta fields1 multicultural needs1 multimedia information1 multimedia storage1 natural language1 natural language understanding [2]1 navigation1 obsolescence2 online searching [4]1 organization1 postulates2 precision1 procedures1 qualitative simulation3 query1 ranking1 reading comprehension2 recall1 reference services5 relevance1 research1 results1 search interfaces [3]1 search strategies1 search tools [5]3 searching1 seeking behavior [7]1 sex related differences1 skills1 techniques2 tests1 think aloud1 trial and error3 user behavior [7]2 user interfaces [1]1 user modelling

Assignment #2 Group F 7

1 World Wide Web

Total number of keys: 82

Again, except for a couple terms, you have only a modest amount of aggregation here. There are several terms that you should consider merging.

These comments are keyed to the numbers above:

1. Definitely merge interface, interfaces, and user interfaces; consider merging human computer interaction with these as well.

2. I would use just natural language. If you feel that the concept of understanding is important, then include that as a separate term.

3. This should be brought out using two existing terms: searching and interfaces.4. Instead of online searching, use your existing terms searching and information

retrieval systems.5. Since the concept of “tools” could apply in other areas, I would consider splitting this

term up into your existing term searching, and tools. The same might be said for search strategies. Someone searching on the term searching would miss documents indexed with search tools and search strategies.

6. Is this distinct from human computer interaction?7. User behavior sounds very similar to Seeking behavior.

4. Records

DOC_NO 6AUTHOR Gauch, SusanTITLE Intelligent Information Retrieval: An IntroductionCITE Journal of the American Society for Information Science, v43 n2, p175-182, 1992ABSTRACT Researchers are exploring the application of artificial intelligence techniques to information retrieval with the goal of providing intelligent access to online information. This article surveys several such systems to show what is possible in the lab today, and what may be possible in the library or office of tomorrow. Systems incorporating user modelling, natural language understanding, and expert systems technology are presented.POSTCO artificial intelligence

information retrieval user modelling natural language understanding expert systems interaction knowledge representationPRECO information retrieval-artificial intelligence information retrieval-expert systems

Assignment #2 Group F 8

intelligent access-knowledge representing information retrieval-natural language understanding information retrieval-artificial intelligence- introductionDOC_NO 7AUTHOR Bates, Marcia J.TITLE The design of browsing and berrypicking techniques for the online search interfaceCITE Graduate school of library and Information Science, University of California at Los AngelesABSTRACT First, a new model of searching in online and other information systems, called "berrypicking", is discussed. This model is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems. Third, based on the new model and research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browing that can be facilitated.POSTCO online searching berrypicking information retrieval search interfaces seeking behavior browsingPRECO online searching-berrypicking Information Seeking Behavior searching-online interfaces-types of browsing information retrieval-browsing-online searching online searching-interface design information seeking-new model-researchDOC_NO 8AUTHOR Marchionini, GaryTITLE Interfaces for End-User Information SeekingCITE College of Library and Information Services, University of MarylandABSTRACT Essential features of interfaces to support end-user information seeking are discussed and illustrated. Examples of interfaces to support the following basic information- seeking functions are presented: problem definition, source selection, problem articulation, examination of results, and information extraction. It is argued that presented interfaces focus on problem articulation and examination of results

The term “berrypicking” is probably unique to this article. I would use a more general term such as browsing or information seeking behavior.

Assignment #2 Group F 9

functions, and research and development are needed to support the problem definition and information extraction functions.POSTCO user interfaces information seeking articulation information extraction multimedia information information retrieval end users multicultural needsPRECO information seeking-end user-interfaces information seeking-multimedia source information retrieval-development of interfaces information retrieval-individual differences information extraction-multiple input output devices information seeking-multicultural needs interfaces-human computer interactionDOC_NO 9AUTHOR Borgman, L., Hristine, C. [OCR error? – it’s Christine Borgman]TITLE The user's mental model of an information retrieval system: an experiment on a prototype online catalogCITE International Journal of Man-Machine Studies, v24, p47-64, 1986ABSTRACT An empirical study was performed to train naive subjects in the use of a prototype Boolean logic-based information retrieval system on a database of bibliographic records. The research was based on the mental models theory which proposes that people can be trained to develop a "mental model" or a qualitative simulation of a system which will aid in generating methods for interacting with the system, debugging errors, and keeping track of one's place in the system. It follows that conceptual training based on a system model will be superior to procedural training based on the mechanics of the system.POSTCO boolean logic_based information information retrieval mental model sex_related differences articulation bibliographic records humanities qualitative simulation human_computer interaction experimentPRECO information retrieval-imental model- bibliographic record information retrieval-qualitative simulation- online catalog information retrieval-individual differences- humanities

Boolean?

Assignment #2 Group F 10

mental model-online catalog information retrieval-debugging errorsDOC_NO 10AUTHOR Hearst, Marti A.TITLE Interfaces for Searching the WebCITE Scientific American Article : March 1997ABSTRACT The rapid growth of the World Wide Web is outpacing current attempts to search and organize it. New user interfaces may offer a better approach.POSTCO user interfaces World Wide Web searching organization cyberspace information access exploratory processPRECO searching-user interfaces-world wide web searching-approach-logical operators information retrieval-boolean notationDOC_NO 11AUTHOR Farrow, John F.TITLE A Cognitive Process Model of Document IndexingCITE Journal of Documentation, v. 47, n. 2, p. 149- 166, June 1991ABSTRACT Classification, indexing and abstracting can all be regarded as summarisations of the content of a document. A model of text comprehension by indexers (including classifiers and abstractors) is presented. The allocation of mental resources to text processing is discussed, and a cognitive process model of abstracting, indexing and classification is described.POSTCO abstracting indexing memory reading comprehensionPRECO Abstracting-Techniques Classification-Cognitive Aspects Indexing-TechniquesDOC_NO 12AUTHOR Dervin, Brenda; Dewdney, PatriciaTITLE Neutral Questioning: A New Approach to the Reference InterviewCITE Annual Review of Information Science & Technology, v. 21, p. 3-33, 1986ABSTRACT Neutral questioning is a strategy for conducting the reference interview in a way that allows the librarian to understand the query from the user's viewpoint. Neutral questions are open in form, avoid premature diagnosis of the problem, and structure the interview along dimensions important to the user. Neutral questioning may become a useful component of in-service training for librarians.

Assignment #2 Group F 11

POSTCO query user behavior

information seeking reference servicesPRECO Reference Services-Interview Techniques Reference Services-User Queries Information Seeking BehaviorDOC_NO 13AUTHOR Huston, Mary M.TITLE Windows into the Search Process: An Inquiry into Dimensions of Online Information RetrievalCITE Online Review, v.15, n. 314, p. 227-243, June/Aug 1991ABSTRACT From diverse users' points of view, contextual frameworks are elaborated for the nature of the information technology, the information universe, and the information search. Future directions for research on users' search processes are discussed in terms of the role for online retrieval in the future information environment.POSTCO information seeking information retrieval systems user behavior interface information access query navigation online searching search strategies search tools interfacesPRECO Information Seeking Behavior Information Retrieval Systems-Design-Interface Information Retrieval Systems-User Queries Information Seeking-Online Searching-StrategiesDOC_NO 14AUTHOR Najarian, Suzanne E.TITLE Organizational Factors in Human Memory: Implications for Library Organization and Access SystemsCITE The Library Quarterly, v. 51, n. 3, p. 269- 291, 1981ABSTRACT Presents evidence for a model which represents the organization of knowledge in memory in terms of a hierarchical type of structure. The experimental findings suggest several considerations for the design of library systems of organization and access that would take into account characteristics of the conceptual organization of knowledge.POSTCO information access information retrieval systems information processing memory user behavior

Assignment #2 Group F 12

information organizationPRECO Information Retrieval Systems-User Queries Information Retrieval Systems-Design-Theory Information Seeking BehaviorDOC_NO 15AUTHOR Bates, Marcia J.TITLE The Invisible Substrate of Information ScienceCITE Journal of the American Society for Information Science, v. 50, n. 12, p. 1043- 1050, 1999ABSTRACT The purpose of this article is to elucidate key elements of the below-the-water-line portion of the information science paradigm. Particular emphasis is given to information science's role as a meta-science.POSTCO information science meta-fields knowledge representation information retrieval systems information retrievalPRECO Information Science Information Retrieval-Theory Information Retrieval Systems-Design-TheoryDOC_NO 16AUTHOR Harman, DonnaTITLE User-Friendly Systems Instead of User- Friendly Front-EndsCITE Journal of the American Society for Information Science, 43, no. 2, 164-174, March 1992.ABSTRACT This article presents four prototype implementations of statistical retrieval systems that demonstrate their potential as powerful and easily used retrieval systems able to service all users.POSTCO end users boolean logic_based information information retrieval ranking document similarity query relevance natural language keywordsPRECO Information Retrieval- Relevance Information Retrieval Systems-End Users- Searching Query- Keywords- Natural LanguageDOC_NO 17AUTHOR Wang, Peiling and Dagobert Soergel [use last name first for consistency]TITLE Beyond Topical relevance: Document Selection Behavior of Real Users of IR SystemsCITE ASIS '93: Proceedings of the 56th ASIS Annual Meeting: Integrating Technologies; Converging Professions, v.30, Learned Information, 87- 92, Oct. 1993.ABSTRACT This paper reports on part of a study of real

Assignment #2 Group F 13

users' behavior in selecting documents from a list of citations resulting from a search of an information retrieval (IR) system. Document selection involves value judgements and decision making. Understanding how users evaluate documents and make decisions provides a basis for designing intelligent IR systems that can do a better job of predicting usefulness.POSTCO relevance decision making think-aloud document valuePRECO Information Retrieval- Relevance Information Seeking Behavior-Decision Making- Think AloudDOC_NO 18AUTHOR Ury, Connie Jo; Johnson Carolyn V.; Meldrem, Joyce ATITLE Teaching a Heuristic Approach to information retrievalCITE Research Strategies, v.15, no.1, 39-47, Winter 1997.ABSTRACT This artical describes how the Library Use Instruction Program at Northwest Missouri State University incorporates a heuristic

model in which students continually refine their information seeking practices while progressing through all levels of courses in diverse disciplines. Collegial partnerships with departmental faculty and ongoing instructional assessment are essential to the success of the program.POSTCO heuristic instruction information retrieval skillsPRECO Information Retrieval Skills-Instruction- HeuristicDOC_NO 19

AUTHOR Swanson, Don R.TITLE Historical Note: Information Retrieval and the Future of an Illusion.CITE Journal of the American society for information sceince, 39, no. 2 92-98, March 1988.ABSTRACT This article offers a personal perspective on automatic indexing and information retrieval, focusing not necessarily on mainstream research but on those events and ideas that have led to the view that information retrieval involved conceptual problems of greater subtlety than is generally recognized. The development and growth of online services seems not to have been

Assignment #2 Group F 14

accompanied by much interest in these conceptual problems, the limits they appear to impose or the potential for transcending such limits through more creative use of the new services.POSTCO information retrieval postulates results searching relevance recall

precision indexing testsPRECO Information Retrieval- Relevance Indexing-Techniques-TestingDOC_NO 20AUTHOR Sawnson, Don R.TITLE Information Retrieval as a Trial-and-Error ProcessCITE Library Quarterly, 47, no. 2, 128-148, 1977ABSTRACT This paper examines three important and well- known information retrieval experiments, with a focus on certain internal inconsistencies and on the high variability of research results. In these experiments, retrieval systems are evaluated in terms of their ability to select relevant documents and reject those that are irrelevant. It is suggested that this criterion is inadequate, because of ambiguities inherent in the concept of relevance and that closer attention to trial-and-error processes may be helpful in developing better criteria. Specific examples of how one might improve document retrieval, library use, and citation indexing are offered.POSTCO information retrieval searching relevance recall indexing trial-and-error precision fallout effectiveness testsPRECO Information Retrieval- Relevance Information Seeking Behavior-Trial and Error Indexing-Techniques-Testing Information Seeking Behavior-Browsing- EffectivenessDOC_NO 1AUTHOR Smith, Elizabeth S.TITLE On the Shoulders of Giants:From Boole to Shannon To Taube: The Origins and Development

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of Computerized Information from the Mid-19th Century to the PresentCITE Information Technology and Libraries,12, no.2, (June 1993), 217-226ABSTRACT This article describes the evolvement of computerized information storage and retrieval, from its beginnings in hte theortical works on logic by George Boole in the mid-nineteenth century, to the application of Boole's logic to switching circuits by Claude Shannon in the 1930s, and the development of coordinate indexing by Mortimer Taube in the late 1940's and early 1950s.POSTCO history data storage information retrieval electronic storagePRECO information retrieval-data storage information seeking-historyDOC_NO 2AUTHOR Rothenberg, JeffTITLE Ensuring the Longevity of digital documentsCITE Scientific American; Jan95, Vol. 272 issue 1, p42ABSTRACT Discusses the problem of obsolescence in digital data storage. Comparison with tradition all information recording; Unrecoverability of data stored in an unknown or obsolete format; process involved in digital data storage.POSTCO multimedia storage data storage obsolescencePRECO information retrieval-data storage-obsolescence information seeking-multimedia sourceDOC_NO 3AUTHOR McCain, Katherine W.TITLE Mapping Authors in Intellectual Space: a technical overviewCITE Journal of the American Society for Information Science, 41 no.6 (September 1990), 433-443ABSTRACT An overview of current data gathering and analytical techniques for author cocitation analysis (ACA). The results of clustering, mapping, and factor analyzing cocited authors form the subdiscipline of macroeconomics.POSTCO authors data mapping analysis techniques proceduresPRECO information seeking-authors information retrieval-techniques-online searchingDOC_NO 4

Assignment #2 Group F 16

AUTHOR Sutton, Stuart ATITLE The role of attorney mental models of law in case relevance determinations: An exploratory analysisCITE Journal of the American Society for Information Science v.45, no.3, 186-200 1994ABSTRACT This article examines the information seeking and evaluative behavior of attorneys as they search the corpus of law for primary authority in order to solve context sensitive legal issues. First, dynamic mental nodels attorneys construct of the law as expressed in its published artifacts is explored. The relevence judgement of cases is then explicated in terms of these models. The conclusion reached is that relevance judgements shift along a knowledge continuum depending on the attorney's mental model, and that factors underlying these judgements are complex, multidimensional, and knowable.POSTCO information seeking law models mental model relevancePRECO Information Seeking Behavior mental model-online catalog-law information retrieval-qualitative simulation- online catalogDOC_NO 5AUTHOR Spink, AmandaTITLE Term relevance feeback and mediated database searching: implications for information retrieval practice and systems designCITE Information Processing and Management, 31, no.2 (1995), 161-171ABSTRACT Research into both the algorithmic and human approaches to information retrieval is required to improve information retrieval system design and database searching effectiveness. The study uses the human apprach to examine the sources and effectiveness of search terms selected during mediated interactive information retrieval.POSTCO research algorithm information retrieval effectivenessPRECO Information Retrieval- Relevance Information Seeking Behavior interfaces-human computer interaction

I would use attorneys and mental model, not law model.

Assignment #2 Group F 17

Part B – Database Evaluation - Measurement

Query #1.

Information need : I would like to know how to design a more user-friendly interface.

Relevant documents : 7,6,10,16

Field Query Doc. Retrieved Recall Precision Effectiveness

Title Interface* 7,8,10 0.5 0.67 0.57

Abstract Design & Interface* 7 0.25 1 0.47

Post-co (Interface*/Interac*)&Information* 6,13 0.25 0.5 0.36

Pre-co Searching--User interface* 10 0.25 1 0.47

You shouldn’t need to use truncation with post-co terms, and Boolean OR should be rare.

Query #2.

Information need: What are the problems with relevance as the basis of IR evaluation?Relevant documents: 4, 17, 19, 20

Field Query Docs. Retrieved

Recall Precision Eff.

Title Relevance 4, 5, 17 0.5 0.67 0.58

Abstract Relevance 4, 20 0.5 1.00 0.65

Post-co Relevance & Information Retrieval

16, 19, 20 0.5 0.67 0.58

Pre-co Information Retrieval-Relevance

5, 16, 17, 19, 20

0.75 0.6 0.67

Query #3.

Information need: How can online retrieval systems better support browsing?

Relevant documents : 5, 6, 7, 16, 17

Field Query Doc. Retrieved Recall Precision Effectiveness

Title Information retrieval 5,6,7, 9,13 60.00% 60.00% 60.00%

Abstract Information retrieval and use* 5,6,9,13,18 40.00% 40.00% 40.00%

Post-co Information retriev* & search* 7,10 20.00% 50.00% 33.29%

Assignment #2 Group F 18

Pre-co Information seeking and use 6,8 20.00% 50.00% 33.29%

Query #4.

Information need: What are the behavior patterns of people seeking information?

Relevant documents : 4,5,7,13,14,17

Field Query Doc. Retrieved Recall Precision Effectiveness

Title behav* 17 16.7% 100% 40.6%

Abstract (user/behav*) & seek* 4,7,8 33.3% 66.7% 47.3%

Post-co Information seeking & (seeking behavior/user behavior) 12,13 16.7% 50% 31.2%

Pre-co Information seeking behavior 12,13,14 33.3% 66.7% 47.3%

For consistency, you should give your measurements as either decimal numbers or percentages, but not mix them.

I would consider merging the terms seeking behavior and user behavior; then you wouldn’t need to OR them.

Good presentation of your testing. Your calculations appear correct.