Information Retrieval Basics

Information Retrieval

• Information Retrieval (IR) is the process of obtaining information from a collection of data, documents, or databases
• Applications of IR include search engines, digital libraries, and recommender systems

Components of IR System

• User Interface: allows users to input queries
• Query Processor: analyzes the query and generates a query plan
• Indexer: builds and maintains the index of the document collection
• Retrieval: retrieves documents that match the query
• Ranking: ranks the retrieved documents based on relevance

Challenges of IR

• Information overload
• Relevance ranking
• Query ambiguity
• Scalability

Inverted Index Creation

• Inverted index is a data structure used to store the indexing information of documents
• Each term in the index points to a list of documents that contain the term

Index Compression Techniques

• Run-length encoding (RLE)
• Variable-byte coding (VBC)
• Huffman coding
• Dictionary-based compression

Term Frequency, Document Frequency, and Weights

• Term frequency (TF): the frequency of a term in a document
• Document frequency (DF): the number of documents that contain a term
• Term frequency weight (TFW): a weighting scheme that takes into account the importance of a term in a document
• Document frequency weight (DFW): a weighting scheme that takes into account the importance of a term across documents

Boolean Retrieval Model and Operators

• Boolean retrieval model: a retrieval model based on Boolean logic
• Term-document incidence matrix: a matrix that represents the presence or absence of terms in documents
• Inverted index: a data structure used to store the indexing information of documents
• Boolean operators: AND, OR, NOT

Query Processing

• Query parsing: breaking down a query into individual terms and operators
• Query optimization: optimizing the query plan to improve efficiency
• Query execution: executing the query plan to retrieve relevant documents

Cosine Similarity

• Cosine similarity: a measure of similarity between two vectors
• Used in information retrieval to measure the similarity between a query and a document

Probabilistic Model

• Probabilistic model: a retrieval model based on probability theory
• Rank documents based on the probability of relevance

Spelling Correction

• Spelling correction: the process of correcting spelling errors in queries and documents
• Challenges of spelling errors: ambiguity, out-of-vocabulary words, and typo propagation
• Edit distance algorithm: a measure of the minimum number of operations needed to transform one string into another

K-gram Indexing

• K-gram indexing: a technique used to index n-grams of terms
• Used in spelling correction and query processing

Soundex and Phonetic Retrieval

• Soundex: a phonetic algorithm used to index words based on their sound
• Phonetic retrieval: a retrieval model that takes into account the phonetic similarity between words

Wildcard Query and Permuterm Index

• Wildcard query: a query that contains a wildcard character
• Permuterm index: a data structure used to index terms with wildcards

N-gram Overlapping

• N-gram overlapping: a technique used to index overlapping n-grams of terms
• Used in query processing and spelling correction

Performance Evaluation Metrics

• F-measure: a measure of the balance between precision and recall
• Augmented precision: a measure of the precision of a retrieval system

Test Collection and Relevance Judgment Evaluation

• Test collection: a standard dataset used to evaluate the performance of a retrieval system
• Relevance judgment: a human judgment of the relevance of a document to a query

Hadoop and MapReducing

• Hadoop: a distributed computing framework
• MapReducing: a programming model used in Hadoop

Web Graph and Link Analysis

• Web graph: a graph that represents the hyperlink structure of the web
• Link analysis: the analysis of the hyperlink structure of the web
• PageRank algorithm: a link analysis algorithm used in Google's search engine
• HITS algorithm: a link analysis algorithm that computes hub and authority scores

Learning to Rank

• Learning to rank: a machine learning approach to ranking documents
• Point-wise ranking: a ranking approach that trains a model to predict the relevance of a single document
• Pair-wise ranking: a ranking approach that trains a model to predict the relevance of a pair of documents
• List-wise ranking: a ranking approach that trains a model to predict the relevance of a list of documents

Web Search Engine

• Web search engine: a system that retrieves and ranks web pages based on a user's query
• Types of search engines: crawler-based, directory-based, and hybrid
• Web structure: a model of the web's hyperlink structure
• Bow-tie structure: a model of the web's structure that consists of a core, in-links, and out-links

Web Crawling

• Web crawling: the process of fetching and indexing web pages
• Features of web crawling: politeness, focus, and incremental crawling

Indexing of Web Pages

• Indexing of web pages: the process of building and maintaining an index of web pages
• Used in search engines and information retrieval systems

Clustering in IR

• Clustering in IR: the process of grouping similar documents together
• K-means clustering algorithm: a clustering algorithm that partitions documents into K clusters
• Hierarchical clustering algorithm: a clustering algorithm that builds a hierarchy of clusters
• Agglomerative clustering algorithm: a clustering algorithm that starts with individual documents and merges them into clusters

Query Expansion and Result Grouping

• Query expansion: the process of expanding a query to improve retrieval performance
• Result grouping: the process of grouping retrieved documents into clusters