OpenContent License, unless specifically labeled as such. REQUIRED TEXTS. Robert R. Korfhage. Information Storage and Retrieval, first edition (John Wiley. SIS/DIST Information Retrieval course is designed to provide you with unique The book written by the late SIS Professor Korfhage provides an appropriate. Robert R. Korfhage is the author of Information Storage and Retrieval ( avg rating, 15 ratings, 1 review, published ), Discrete Computational Str.
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Documents Flashcards Grammar checker. Meadow, published by Academic Press, Inc. The book is available at http: The knowledge, experience and background in information systems are preferred.
The information retrieval and storage focuses on the latter, the second level. The aim of this course is to prepare students as information retrieval system analysts and designers. To outline basic terminology and components in information storage and retrieval systems To compare and contrast information retrieval models and internal mechanisms such as Boolean, Probability, and Vector Space Models To outline the structure of queries and documents To articulate fundamental functions used in information retrieval such as automatic indexing, abstracting, and clustering To critically evaluate information retrieval system effectiveness and improvement techniques To understand the unique features of Internet-based information retrieval To describe current trends in information retrieval such as information visualization.
A significant portion of your grade is determined by your individual assignments. It is extremely important for you to understand the grading policies and obtain high points on your assignments. Attendance is mandatory and class participation is expected. You will be graded on your participation and contributions to class discussions. Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM, 35 Week 2 Data control and data presentation Content Query, differences between documents and queries, type of documents, types of data structure, document surrogates, vocabulary control, structure of a thesaurus, structural representation, fine data structure, bit and byte, MARC structure.
Chapters 3 and 4.
Robert R. Korfhage (Author of Information Storage and Retrieval)
Some simple effective approximations to the 2—poisson model for probabilistic weighted retrieval. Van Rijsbergen, and Iain Campbell. Is this document relevant?. A survey of probabilistic models in information retrieval.
ACM Computing Surveys 30 yb Probabilistic models in information retrieval. Computer Journal 35 3: Week 6 Automatic indexing and abstracting Content Indexing, automatic indexing, purpose of indexing, why use automatic indexing, stop list approach, raw term frequency approach, normalized term frequency approach, informatjon term frequency approach, and other considerations. A new term significance weighting approach. Journal of Intelligent Information Systems, 24 1 Understanding inverse document frequency: On theoretical arguments for IDF.
Journal of Documentation, 60 5pp. Automatic text decomposition and structuring. Information Processing and Management, 32 2pp. The significance of the Cranfield tests on index languages. Week 7 Similarity measure algorithms Content Data fusion, term association, general similarity measures, similarity measures in the vector retrieval model, comparisons of the two kinds of similarity approaches, extended user profile, current awareness systems, retrospective search systems, reference point, modifying the query by the user profile.
Chapters storaage and 5. Developing a new similarity measure from two different perspectives. Zhang J, and Rasmussen E Using interdocument similarity in document retrieval systems. Journal of the American Korrfhage for Information Science, Cottrell, and Richard K.
INFSCI 2140 – Information Storage and Retrieval
Optimizing similarity using multi-query relevance feedback. Moffat, Alistair, and Justin Zobel. Exploring the similarity space. Week 8 Automatic clustering approaches Content Definition of automatic clustering, criteria of clustering, differences between clustering and classification, significance of a clustering approach in IR, categorization of clustering algorithms, non- hierarchical clustering algorithm, the K-means clustering algorithm, K-means in SPSS, hierarchical clustering algorithm, hierarchy cluster in SPSS.
Evaluation of hierarchical clustering algorithms for document databases. In Proceedings of the eleventh international conference on Information and knowledge management, pp. Hamerly, Greg, and Charles Elkan. Learning the k in k-means. Narasimha Murty, and Patrick Flynn. ACM Computing Surveys 31 3: A survey of recent advances in hierarchical clustering rterieval.
Computer Journal 26 4: Information Visualization Content Visualization, visualization for information retrieval, analysis of traditional information retrieval systems, navigation problems on WWW, why use visualization for information retrieval, core of visualization for information retrieval, functionality of visualization, Boolean-based information retrieval system, non-Boolean-based information retrieval informatioon, visualization of web-based information,consideration from cognitive engineering, history of visualization, technical environment for the visualization, potential research topics.
Readings in information visualization: Morgan Kaufmann, pp, User interfaces and visualization. Ribeiro-Neto, editors, Modern Information Retrieval, chapter 10, pp. Visual exploration of large data sets. Communications of the ACM, 44, 8, pp. Visualising semantic spaces and author co-citation networks in digital libraries.
Information Processing and Management, 35 3 The role of visualization in document analysis. Zhang J, and Korfhage RR Distance and Angle Retrieval Environment: A Tale of the Two Measures. Journal of the American Society for Information Science, 50 9 Week 11 Internet Information Retrieval Content Challenge in the Web, language distribution, centralized architecture, crawlers, jargons, crawling the Web, breadth first approach, depth first approach, crawling approach, web page ranking, meta-search, considerations for meta-search engines, trends Reading: The anatomy of a large-scale hypertextual web search engine.
A taxonomy of web search. Estimating linguistic diversity on retrjeval internet: A taxonomy to avoid pitfalls and paradoxes. Journal of Computer-Mediated Communication 12 4.
INFORMATION STORAGE AND RETRIEVAL SYLLABUS
Pennock, and Gary W. Using web structure for classifying and describing web pages. Week 12 Image retrieval Content Content-based image retrieval, image feature description, color, color histogram, color order system, texture, Shape, characteristics of image queries, image system applications, image retrieval systems Reading: Lecture notes Week 13 Evaluation issues Content Seven criteria for evaluation for information retrieval, Average recall and average precision, Harmonic mean, evaluation of a search engine, relevance issue, Kappa measure, quality versus quantity, Possible factors which influence outcome of a search, Grandfield experimental study Reading: A review of the literature and a framework for thinking on the notion in information science.
Behavior and effects of relevance.
Variations in relevance assessments and the measurement of retrieval effectiveness. Week 14 Student presentation Note: Papers will characterize current issues associated with korfhgae topic, discuss the state of the art of the topic, evaluate sample systems, and outline future directions for the area. Papers must integrate a minimum of 15 relevant sources.
Music information retrieval .
INFORMATION STORAGE AND RETRIEVAL SYLLABUS
Image information organization and retrieval . Automatic indexing theory and practice . Evaluation of search engines . On Boolean-based information retrieval system .
Comparison between Boolean-based and Vector-based information systems . Other information retrieval models . Evaluation of an information visualization system. Important properties of memory rerieval.
The Future of Information Literacy. Cmpe Introduction to Information Retrieval.