Information Retrieval Indexing Concepts

Subsystems of Information Retrieval (IR) System

• The IR system consists of two subsystems: Indexing and Searching.
• Indexing: Organizes documents offline, using keywords extracted from the collection.
• Searching: An online process that scans the document corpus to match user queries with relevant documents.

Indexing Subsystem

• Indexing is crucial for efficient document searches, as searching relies on prior indexing.
• Documents must be indexed to become searchable; indexing creates a searchable representation of documents.
• Indexing can be achieved using various indexing languages which can include every word in a document.
• Understanding how to search is directly tied to understanding indexing.

Basic Concepts of Indexing

• Indexing arranges terms for rapid searches and minimizes memory space requirements.
• Enhances retrieval efficiency and reduces retrieval time for users.
• Index files contain sorted index terms, generally smaller than the original document files.
• Heaps Law highlights vocabulary size: in 1 GB of text, expected vocabulary size is around 5 MB.
• Linguistic pre-processing can further reduce indexing size.

Current Search Engine Indexing Practices

• Search engines utilize web crawlers to index each web page.
• Post-indexing, the local copy of the page is usually discarded unless cached.
• Automatically indexing search engines: Google, AltaVista, Excite, HotBot, InfoSeek, Lycos.
• Semi-automatically indexing search engines: Yahoo, Magellan, Galaxy, WWW Virtual Library; these are hierarchically organized with partial human input.

Major Steps in Index Construction

• Source File: Each document is described by representative keywords known as index terms.
• Index Terms Selection: Text operations and pre-processing methods are applied for term relevance.
• Different weighting methods for index terms include TF (Term Frequency), IDF (Inverse Document Frequency), and TF*IDF.
• Output: A structured indexing file containing relevant index terms.

Structure of Index Files

• An index file acts as a searchable list, mapping each keyword to the corresponding documents where it occurs.
• Index files are organized for associative look-up, facilitating quick identification of documents for specific terms.
• Various data structures for index files can include sequential files, inverted files, and suffix trees.

Evaluation Metrics for Index Files

• Evaluating performance includes running time for indexing and access, update times, and space used for storage.
• Access types supported efficiently should be considered to enhance the user experience.

Sequential File Indexing

• A sequential file structure arranges records serially in lexicographic order based on a primary key.
• Records are accessed by searching from the beginning, causing a potentially slow retrieval process.

Inverted File Indexing

• An inverted file is a keyword-oriented indexing method, where each keyword points to documents containing it.
• Inverted index files include vocabulary lists and document pointers to quickly retrieve relevant information.
• Each term entry in the vocabulary contains its document occurrence frequency and pointers to occurrences.

Construction of Inverted Files

• The vocabulary collects distinct terms in lexicographic order; each term links to postings of relevant documents.
• Postings consist of pointers to documents containing the specified term.

Searching Efficiently with Inverted Files

• Searching in inverted files leverages a vocabulary list for quicker access and utilizes binary search for efficiency.
• Updating inverted files is complex as it requires adjustments in both vocabulary and postings files.

Example of Inverted File Creation

• Steps to create an inverted file involve text operation steps to identify and sort terms from a document collection, managing occurrences and frequencies of terms efficiently.