Information Retrieval

Overview

Information retrieval (IR) is the activity of finding and returning information resources that are relevant to a user's query from within a larger collection of documents or data. As a field of computer science, it studies how to represent, index, store, and rank information so that the most pertinent items can be located quickly and accurately. Information retrieval techniques underpin web search engines, digital libraries, question-answering systems, recommendation services, and enterprise document search, and they increasingly combine traditional indexing with machine-learning methods to interpret intent and improve ranking. Effective retrieval depends on modeling both the content of stored resources and the meaning of a query, then matching the two through measures of relevance. As collections grow in size and complexity, knowledge management and artificial intelligence play a larger role in organizing information and surfacing useful results. Applied Robotics and Artificial Intelligence publishes peer-reviewed, open-access research across applied artificial intelligence and intelligent systems, including work on design support that steers complex problem-solving through knowledge management and AI. This page gathers material relevant to information retrieval within the journal's wider focus on how intelligent systems acquire, organize, and apply information to support human tasks.

Research published in this journal

1 peer-reviewed article, ranked by relevance. Each links to its DOI.

This page summarises published research for orientation; it is not medical or professional advice.

Related topics