Information Retrieval Systems Quiz
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Questions and Answers

What problem does the Web face regarding stored information?

  • Lack of user interaction options
  • High speed of information retrieval
  • Duplication of information across websites
  • Explosion of stored information with little guidance (correct)

    • Web mining is important only for retrieving textual data.

    False

    What is used to describe the intended documents for retrieval?

    keywords

    A video movie may have associated keywords such as its title, director, actors, and ______.

    <p>genre</p> Signup and view all the answers

    Match the following types of data with their respective keyword associations:

    <p>Text Documents = Keywords related to their content Videos = Title, director, actors, genre Images = Tags describing content Audio Files = Tags related to description</p> Signup and view all the answers

    What is the primary function of document retrieval systems?

    <p>Finding relevant documents based on user input</p> Signup and view all the answers

    Web search engines are a common example of information-retrieval systems.

    <p>True</p> Signup and view all the answers

    What factors influence the ranking of documents in information retrieval systems?

    <p>Term frequency, inverse document frequency, and hyperlinks to documents.</p> Signup and view all the answers

    The formula used to measure the relevance of a document to a term is known as _____.

    <p>TF (Term Frequency)</p> Signup and view all the answers

    Which of the following statements about TF-IDF ranking is correct?

    <p>It combines term frequency with inverse document frequency.</p> Signup and view all the answers

    All terms used as keywords have the same level of importance in document relevance estimation.

    <p>False</p> Signup and view all the answers

    Match the retrieval factors with their descriptions:

    <p>Term Frequency = Frequency of occurrence of query keyword in document Inverse Document Frequency = How many documents the query keyword occurs in Hyperlinks = More links to a document indicate greater importance Ranking = Order documents based on relevance score</p> Signup and view all the answers

    What happens to the relevance score of a document containing multiple occurrences of a term?

    <p>The relevance may not be proportional, as context and length of document matter.</p> Signup and view all the answers

    What is the purpose of inverse document frequency (IDF) in the context of document ranking?

    <p>To reduce the impact of frequent terms</p> Signup and view all the answers

    Stop words are commonly used as keywords in information-retrieval systems.

    <p>False</p> Signup and view all the answers

    What is the TF–IDF approach?

    <p>A measure of relevance that uses term frequency and inverse document frequency.</p> Signup and view all the answers

    The formula for the relevance of a document to a set of terms is denoted as r(d, Q) and can be modified to take into account term __________.

    <p>proximity</p> Signup and view all the answers

    Match the following terms with their definitions:

    <p>Term Frequency (TF) = The number of times a term appears in a document Inverse Document Frequency (IDF) = A measure of how much information a term provides Stop Words = Common words typically ignored in searches Similarity-Based Retrieval = Finding documents similar to a given document</p> Signup and view all the answers

    How can users enhance the relevance of their document queries?

    <p>By specifying weights for the terms</p> Signup and view all the answers

    Documents that contain terms occurring far apart should be ranked higher than those that contain terms close together.

    <p>False</p> Signup and view all the answers

    What happens when a user supplies keywords that include stop words?

    <p>The stop words are discarded.</p> Signup and view all the answers

    What is a major factor in determining the relevance ranking of a web page?

    <p>Hyperlinks pointing to the page</p> Signup and view all the answers

    Popularity ranking involves ranking pages based on their access frequency only.

    <p>False</p> Signup and view all the answers

    What is the basic idea behind popularity ranking in web search?

    <p>To rank popular pages higher than others containing the specified keywords.</p> Signup and view all the answers

    The relevance of a page can be enhanced by combining traditional TF–IDF measures with the page's __________.

    <p>popularity</p> Signup and view all the answers

    Which of the following describes a challenge in determining the access frequency of web pages?

    <p>Most sites do not want to disclose their access frequency.</p> Signup and view all the answers

    Ranking a page based solely on the number of links to it will always yield accurate popularity measurements.

    <p>False</p> Signup and view all the answers

    What term describes the phenomenon where sites may misrepresent their access frequency?

    <p>gaming the system</p> Signup and view all the answers

    What is the vector for document d defined as?

    <p>r(d,t) = TF(d,t) * IDF(t)</p> Signup and view all the answers

    Relevance feedback requires users to add more keywords to their search query.

    <p>False</p> Signup and view all the answers

    What measure is used to determine the similarity between two document vectors?

    <p>cosine of the angle between the vectors</p> Signup and view all the answers

    Relevance feedback can help users find relevant documents from a large set of documents matching the given query keywords, allowing users to identify one or a few of the returned documents as __________.

    <p>relevant</p> Signup and view all the answers

    Match the following concepts with their descriptions:

    <p>Vector space model = Defines an n-dimensional space for documents Cosine similarity = Measure of similarity between document vectors Relevance feedback = User selects relevant documents for further search Clustering = Grouping documents based on similarity</p> Signup and view all the answers

    What is a possible drawback of early Web-search engines that used only TF–IDF based relevance measures?

    <p>They had limitations with very large collections.</p> Signup and view all the answers

    Clustering documents can help display a representative set when the number of documents is very large.

    <p>True</p> Signup and view all the answers

    What is done to avoid returning multiple copies of the same document in search results?

    <p>Detect duplicates and return only one copy</p> Signup and view all the answers

    What is the primary purpose of the PageRank algorithm?

    <p>To measure the popularity of a webpage</p> Signup and view all the answers

    PageRank relies on the number of links pointing to a page in order to determine its ranking.

    <p>True</p> Signup and view all the answers

    What does the variable 'δ' represent in the PageRank algorithm?

    <p>The probability of a step being a random jump</p> Signup and view all the answers

    The PageRank of a page is defined as the probability that a random walker is __________ the page at any given point in time.

    <p>visiting</p> Signup and view all the answers

    Match the components of PageRank with their descriptions:

    <p>PageRank = Measure of webpage popularity T[i, j] = Probability that a walker follows a link from page i to page j Ni = Number of links out of page i P[j] = PageRank of page j</p> Signup and view all the answers

    How is the jump probability matrix T defined for each link?

    <p>T[i, j] = 1/Ni</p> Signup and view all the answers

    In PageRank, each PageRank value is initially set to 1 divided by the total number of pages (1/N).

    <p>True</p> Signup and view all the answers

    What iterative technique is used to solve the equations generated in PageRank?

    <p>An iterative calculation adjusting the P values</p> Signup and view all the answers

    Study Notes

    Web Mining

    • Web mining is a critical tool for researchers to help locate interesting information on the internet.
    • The internet provides easy access to numerous sources, but information overload is a challenge.

    Document Searching

    • Users search for specific documents or types of documents.
    • Keywords, like "database system" or "stock-market scandals", help locate relevant documents.
    • Documents associated with keywords matching the query are retrieved.
    • Keyword-based search works for textual, video, and audio data (if they have descriptive keywords).

    Document Searching (more detail)

    • Keywords like title, director, actors, and genre help search for movies or video clips (video/audio tags work the same way as keywords)
    • Document retrieval uses user keywords or sample documents to find relevant documents.
    • Web search engines are the most common use of this system; they can search even image data with associated keywords.
    • Information retrieval systems allow searches using keywords and logical operators (and, or, not).
    • "And" is understood implicitly even without specifying it.

    Keyword Search (Ranking)

    • Ranking documents by estimated relevance is critical.
    • Factors for ranking include:
      • Term frequency (how often a keyword appears in a document)
      • Inverse document frequency (how many documents contain the keyword)
      • Fewer documents containing the query keyword gives more importance to the keyword

    Relevance Ranking Using Terms (TF-IDF)

    • TF-IDF stands for Term Frequency/Inverse Document Frequency.
    • n(d) is the number of terms in document d
    • n(d, t) is the number of times term t appears in document d
    • Relevance of document d to term t: TF(d,t) = log(1 + n(d,t)/n(d))
    • The log factor avoids excessive preference for frequent terms.
    • Relevance of document to query Q: r(d,Q) = Σ TF (d, t) * IDF(t) (summation over all terms in query Q)

    Motivation for TF-IDF Formula

    • Document length affects the number of keyword occurrences.
    • 10 occurrences of a term in a long document doesn't imply 10 times the relevance as 1 occurrence in a short document.

    Relevance Ranking Using Terms (Cont.)

    • Systems prioritize words present in titles, author lists or headings.
    • Words appearing later in a document are given less preference.

    Relevance Ranking Using Terms (Cont. 2)

    • The formulas for relevance (TF, IDF) can be extended.
    • Term frequency, TF, refers to the importance of a term regardless of the specific formula used.
    • Documents are usually returned in decreasing order of their relevancy score.
    • Typically only few top-ranked documents are shown, not all results.

    Inverse Document Frequency

    • Relevance for a query with multiple keywords is calculated by combining the relevancy of each keyword for a document.
    • A simple way to combine keyword scores is to add them.
    • Not all terms are equal as keywords. More rare terms are valued more highly.

    Inverse Document Frequency (Why rare terms matter more)

    • Imagine a query with "computing" (common) and "quantum" (less common).
    • A document with "quantum" but not "computing" should rank higher.

    Inverse Document Frequency (Calculating IDF)

    • IDF(t)= 1/n(t) , where n(t) is the number of documents containing term t.
    • Relevance measure of document d to terms Q: r(d, Q) = Σ TF (d, t) * IDF(t) (summation over all terms in query Q).

    Inverse Document Frequency (Refinement)

    • Users may specify weights for query terms.
    • Weights are considered by multiplying TF(t) by w(t) in the relevance formula; such adjustment allows users to fine tune the search outcomes.
    • Terms with assigned high weights will get more importance and the resulting document retrieval will be more exact based on the user's query

    Stop Words

    • Common words like "and," "or," "a," etc. have extremely low inverse document frequency, making them irrelevant for queries.
    • Stop words (common words) are not considered when indexing documents.
    • Stop words are discarded if present in user-supplied keywords.

    Proximity

    • Proximity of keywords in a document to each other affects ranking.
    • Documents with closely positioned keywords get more priority.
    • The ranking formula can accommodate proximity.

    Similarity Based Retrieval

    • Some systems use similarity to retrieve documents similar to a given document A.
    • Similarity is often defined based on common terms.

    Similarity Based Retrieval (Finding similar terms)

    • An approach is to identify terms in document A with the highest TF(A,t) * IDF(t) scores.
    • These terms become the query to find similar documents
    • Query terms are weighted by TF(A,t) * IDF(t)

    Similarity Based Retrieval (Vector Space Model)

    • An n-dimensional space (n is the number of words in the document set) is defined.
    • Each document is represented as a vector in this space.
    • The vector's coordinate is TF(d,t) * IDF(t), where d is the document, t is the term.
    • The cosine of the angle between two vectors of documents d and e is used to measure their similarity.

    Relevance Feedback

    • If the set of similar documents to a query is large, the system may present a few most relevant ones to the user.
    • The user can rate the displayed docs, and the system restarts the search considering this feedback.
    • This allows the user to find documents better aligned with their original search intent.
    • Relevance feedback can be used to find documents that are relevant when the original query and results set are too large.
    • Users can tag docs as "relevant" and the system automatically refines the search using this feedback to discover more similar documents.

    Clustering

    • Search systems often cluster similar documents to provide a representative subset when many documents match a query.
    • Cosine similarity based clustering shows documents from different clusters in the results, showing varied answers.

    Mirroring

    • Multiple copies of a document on the web exist (website mirrors).
    • Search systems should identify and return only one copy of the duplicate document.
    • Early search engines used only TF-IDF for ranking.
    • However, text documents often miss crucial information: hyperlinks.
    • Pages with many incoming links often rank higher than those with lesser incoming links (popular pages receive the most hits).

    Popularity Ranks

    • Popularity ranking considers page popularity when ranking documents; pages linked from many other pages (popular pages) are ranked higher.
    • Example: the "google.com" page appears frequently in queries and is often linked to frequently, this page often is highly ranked in searches for "google".

    Combined Measure

    • Traditional ranking methods (TF-IDF) and popularity measures can be combined to provide a more comprehensive measure of a page's relevance to a given query.
    • Results are returned by sorting based on the combined relevance score; higher scores appear earlier.

    Popularity Ranking (Defining Popularity)

    • Determining popularity is difficult.
    • Simply counting page accesses as a measure of popularity has limitations and issues.
      • Difficulty obtaining the access frequency from every website is notable
      • Websites may falsely report access frequency to gain a ranking advantage.

    Popularity Ranking (Crawling)

    • Web crawling processes analyze hyperlinks to estimate popularity.

    Popularity Ranking (Popularity of Sites, not just pages)

    • A popular site's linked pages benefit from its popularity.

    PageRank

    • Google introduced PageRank to improve query results.
    • PageRank is a measure of a page's popularity based on links from other popular pages.
    • This iterative process refines ranking until minimal changes.

    PageRank (Random Walk Model)

    • Use of random web surfing model.
    • Probability of jumping to a different page or following a link.
    • Iterative system to define page rank.
    • Random walker follows random links.
    • PageRank is the odds the walker visits a specific page at any given instant.

    PageRank (Random Walk Model details)

    • Pages frequently pointed to are more likely to hold the attention of the random searcher;
    • Pages linked to by pages with high PageRank often rank high.

    PageRank (Mathematical)

    • PageRank is represented by a set of linear equations that can be solved using matrix manipulation techniques.
    • Each page gets its PageRank from other related documents.

    PageRank (Solving Equations)

    • A set of equations defines PageRank, which are solved using iterative techniques,
      • initial page rank is set to 1/N
      • Repeated calculation until minimal change.

    Example

    • Example values for page 1, 2, 3 and 4; probabilities of linking among the pages.
    • Shows the iterative calculations for PageRank values of each page.

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    Description

    Test your knowledge on information retrieval systems and the factors influencing document rankings. This quiz covers web mining, keyword associations, and document relevance measurements. Perfect for students studying information science or related fields.

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