Information retrieval 1 introduction to ir
- 1. Information Retrieval : 1 Introduction to IR Prof Neeraj Bhargava Vaibhav Khanna Department of Computer Science School of Engineering and Systems Sciences Maharshi Dayanand Saraswati University Ajmer
- 2. Learning objectives of IR Series • Introduction: Motivation, Basic concepts, past, present, and future, the retrieval process. • • Modeling: Introduction, A taxonomy of information retrieval models, retrieval: ad hoc and filtering, a formal characterization of IR models, classic information retrieval, alternative set theoretic models, alternative algebraic models, alternative probabilistic models, structured text retrieval models, models for browsing. • • Retrieval Evaluation: Introduction, retrieval performance evaluation, reference collections. query • Languages: Introduction, keyword-based querying, Pattern matching, Structural queries, Query protocols. • • Query Operations: Introduction, user relevance feedback, automatic local analysis, automatic global analysis. • • Text and multimedia languages and Properties: Introduction, metadata, text, markup languages, • Indexing and searching: Introduction; inverted files; other indices for text; Boolean queries; sequential searching; pattern matching; structural queries; compression. • • Searching the Web: Introduction, challenges, characterizing the web, search engines, browsing, meta searchers, finding the needle in the haystack, searching using hyperlinks.
- 3. Architecture of the IR System
- 4. Information Retrieval (IR) • IR deals with the representation, storage, organization of, and access to information items • Types of information items: documents, Web pages, online catalogs, structured records, multimedia objects • Early goals of the IR area: indexing text and searching for useful documents in a collection • Nowadays, research in IR includes: – Modeling, Web search, text classification, systems architecture, user interfaces, data visualization, filtering and languages.
- 5. Early Developments • For more than 5,000 years, man has organized information for later retrieval and searching • This has been done by compiling, storing, organizing, and indexing papyrus, hieroglyphics, and books • For holding the various items, special purpose buildings called libraries, or bibliothekes, are used • The oldest known library was created in Elba, in the Fertile Crescent, between 3,000 and 2,500 BC • Since the volume of information in libraries is always growing, it is necessary to build specialized data structures for fast search — the indexes
- 6. Libraries and Digital Libraries • For centuries indexes have been created manually as sets of categories, with labels associated with each category • The advent of modern computers has allowed the construction of large indexes automatically • Libraries were among the first institutions to adopt IR systems for retrieving information • Initially, such systems consisted of an automation of existing processes such as card catalogs searching • Increased search functionality was then added • Ex: subject headings, keywords, query operators • Nowadays, the focus has been on improved graphical interfaces, electronic forms, hypertext features
- 7. IR at the Center of the Stage • Until recently, IR was an area of interest restricted mainly to librarians and information experts • A single fact changed these perceptions—the introduction of the Web, which has become the largest repository of knowledge in human history • Due to its enormous size, finding useful information on the Web usually requires running a search • And searching on the Web is all about IR and its technologies • Thus, almost overnight, IR has gained a place with other technologies at the center of the stage
- 8. The IR Problem • The IR Problem • The key goal of an IR system is to retrieve all the items that are relevant to a user query, while retrieving as few non relevant items as possible • That is, the IR system must rank the information items according to a degree of relevance to the user query
- 9. The User’s Task • Consider a user who seeks information on a topic of their interest • This user first translates their information need into a query, which requires specifying the words that compose the query • In this case, we say that the user is searching or querying for information of their interest • Consider now a user who has an interest that is either poorly defined or inherently broad • For instance, the user has an interest in car racing and wants to browse documents on Formula 1 In this case, we say that the user is browsing or navigating the documents of the collection
- 10. The User’s Task
- 11. Information × Data Retrieval • Data retrieval: the task of determining which documents of a collection contain the keywords in the user query Data retrieval system Ex: relational databases • Deals with data that has a well defined structure and semantics • A single erroneous object among a thousand retrieved objects means total failure • Data retrieval does not solve the problem of retrieving information about a subject or topic