Information Retrieval Lecture Note 2025 PDF by A.I KAYODE

Lecture Note: Introduction to Information Retrieval Mechanisms Lecture Outline Introduction 1. What is Information Retrieval (IR)? 2. Importance of Information Retrieval 3. Basic Concepts in Information Retrieval 4. Components of an Information Retrieval System 5. How Information Retrieval Works: Th...

Lecture Note: Introduction to Information Retrieval Mechanisms Lecture Outline Introduction 1. What is Information Retrieval (IR)? 2. Importance of Information Retrieval 3. Basic Concepts in Information Retrieval 4. Components of an Information Retrieval System 5. How Information Retrieval Works: The Process 6. Types of Information Retrieval Systems 7. What are the characteristics of information retrieval? 8. Challenges in Information Retrieval Introduction In a recent statistics, IBM estimates that every day 2.5 quintillion bytes of data are created – so much that 90% of the data in the world today has been created in the last two years. It is a mind-boggling figure and the irony is that we feel less informed in spite of having more information available today (IBM, 2024). The sheer volume and velocity of data creation make navigating the Internet akin to exploring a dense jungle. Without Information Retrieval (IR), finding specific information would be nearly impossible. Today, we will uncover the concept of information retrieval from the ground up, discussing each and every aspect of this technology. It is important to note from the onset of this lecture that the volume of data grows exponentially in the digital age, effective methods for finding and organizing information have become essential. IR systems, such as search engines, library catalogues, and recommendation platforms, are designed to help users navigate large datasets and extract meaningful insights efficiently. 1  As technology evolves, the challenges and opportunities in IR continue to expand, shaping the way we interact with information in our daily lives. Information Retrieval in Libraries: Libraries were the first to adopt IR systems for information retrieval. In first-generation, it consisted, automation of previous technologies, and the search was based on author name and title. In the second generation, it included searching by subject heading, keywords, etc. In the third generation, it consisted of graphical interfaces, electronic forms, hypertext features, etc. What is Data? Data is factual information — like measurements or statistics — used to support reasoning, discussions, or calculations. Here’s a quick grammar tip: “Data” is plural, while “datum” is singular. For example, a single piece of information is a datum, and multiple pieces are data. Is Data the Same as Information? No, data and information are not the same. Data are raw facts — unprocessed and without context. Information, on the other hand, is what you get when data are processed, organized, and interpreted to make sense in a specific context. In short, Data ≠ Information. Here are some examples of data: The number of steps tracked by your fitness app The temperature readings from a weather station A list of customer names and phone numbers Raw survey responses A collection of photos on your phone The timestamps of messages in a chat log 2 Data on its own doesn’t tell a story. But when you analyze and interpret it, it becomes valuable information. 1. What is Information Retrieval (IR)? Information Retrieval (IR) is the process of finding relevant information from a collection of resources (like documents, websites, books, etc.) based on user input, often in the form of a query. In simple terms, IR helps users find the information they need from large amounts of data. Information Retrieval (IR) is the process of finding relevant information from a large collection of data sources like documents, web pages, books, or research papers. Imagine it as asking a question and having a computer search through millions of documents to give you the best answers. Examples of IR: Using Google Search to find websites on a specific topic. Using Google: When you type a question or keywords, Google finds websites that contain the information you need. Library Catalogs: Searching a library database to find books on a specific subject. Searching for research articles in an academic database. Academic Databases: Students and researchers use tools like JSTOR or Google Scholar to find articles on research topics. 2. Importance of Information Retrieval In today's digital world, we generate and store massive amounts of data every second. Information retrieval is important because it helps us quickly and accurately find the information we need from these large datasets, making research, learning, and decision-making easier and faster. With the rapid growth of data on the internet and in digital databases, the need for efficient and accurate information retrieval has become crucial. Effective IR helps users: 3 Save time by finding relevant information quickly. Improve productivity in research and decision-making. Access diverse sources of knowledge. 3. Basic Concepts in Information Retrieval Document: The items in a collection that contain information (e.g., web pages, books, articles). Query: A set of keywords or questions that describe the information a user is searching for. Relevance: A measure of how well a document meets the user’s needs or query. Index: A data structure that helps the system quickly locate documents relevant to a query. Retrieval Model: The mathematical model that ranks documents based on relevance to a query. 4. Components of an Information Retrieval System An information retrieval system is made up of several key components: 1. User Interface: The point where users interact with the system, entering queries and viewing results. 2. Indexer: This component processes documents and builds an index that maps keywords to documents. 3. Query Processor: This takes a user’s query, processes it, and converts it into a format the system can understand. 4. Search Engine: Finds documents that match the user’s query using the index. 5. Ranking Engine: Sorts the results based on relevance to the user’s query. 4 5. How Information Retrieval Works: The Process 1. Indexing: Tokenization: Breaking down documents into individual words or phrases. Stemming and Lemmatization: Reducing words to their root forms (e.g., "running" becomes "run"). Stop Word Removal: Filtering out common words that don’t add value to the query, such as "the," "is," "on." Building the Index: Creating an index of words and their locations within the document collection. 2. Query Processing: Users enter a query in the form of keywords or a question. The system processes the query to make it comparable to the indexed terms. 3. Matching: The system matches the query against the index to find relevant documents. 4. Ranking: Documents are ranked based on their relevance to the query using algorithms. The system organizes results based on their relevance to the user’s query, usually displaying the most relevant ones at the top. Example: If two documents contain the term "photosynthesis," the one with more information about photosynthesis might appear higher. 5. Results Presentation: The system displays a list of documents or results to the user, ranked by relevance. 6. Types of Information Retrieval Systems Uses logical operators like AND, OR, and NOT to connect keywords in a search. Example: In a library database, you might search for "biology AND genetics" to find books that discuss both topics. On the other hand, Boolean Retrieval: Users create queries using Boolean operators (AND, OR, NOT) to combine keywords (e.g., "Apple AND technology"). 5 Vector Space Model: Documents and queries are represented as vectors in a multi- dimensional space; similarity is calculated using mathematical measures. Probabilistic Model: Estimates the probability that a document is relevant to a query. Web Search Engines: Specialized retrieval systems that search the World Wide Web, like Google or Bing. 7. What are the characteristics of information retrieval? There are 12 characteristics of an Information Retrieval model: Search intermediary Domain knowledge Relevance feedback Natural language interface Graphical query language Conceptual queries Full-text IR Field searching Fuzzy queries Hypertext integration Machine learning Ranked output 8. What is information retrieval used for? When you ask your librarian in your school, they quickly find out the book which you need from hundreds of other books segregated into various sections or types. This is a kind of Information retrieval. Now imagine inputting a similar search query on web search engines, which goes through billions of pages and resources to find out the result of your query. Information Retrieval is believed to be the dominant 6 form of Information access. The IR system assists the users in finding the information they require but it does not explicitly return the answers to the question. Just like your librarian who might recommend a few other books to you in the same genre. It notifies regarding the existence and location of documents that might consist of the required information. An IR system has the ability to represent, store, organize, and access various information items. A set of keywords are required to search. Keywords are what people are searching for in search engines. These keywords summarize the description of the information. 9. Challenges in Information Retrieval Ambiguity: Words may have multiple meanings (e.g., "bank" as a river bank or a financial institution). Relevance: Determining what is most relevant to the user's needs can be complex. Data Overload: Managing and indexing large amounts of data efficiently. User Interface: Ensuring the system is easy to use and understand. Personalization: Adapting results based on the user's history or preferences. Key Takeaways Information retrieval is essential for managing and accessing vast amounts of information. IR systems have a structured process for retrieving information, involving indexing, query processing, and ranking. Different models exist to help improve the accuracy and relevance of results. IR faces challenges such as data overload and ensuring relevance, especially with diverse user needs. 7 Works Cited Courses Taken: Maven Analytics. Data Literacy Foundations. Maven Analytics, https://mavenanalytics.io/course/data-literacy-foundations. 365 Data Science. Data Literacy. 365 Data Science, https://365datascience.com/resources-center/course-notes/data-literacy/. Statistics and Reports: Statista. “Amount of Data Created Worldwide from 2010 to 2025.” Statista, 2024, https://www.statista.com/statistics/871513/worldwide-data-created/. 8