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Automatic hyperlinking

Software feature

Wikipedia contributorsAdapted for Fathomly · View authors
2 min read
Source updated May 26, 2026

An autolink is a hyperlink added automatically to a hypermedia document, after it has been authored or published. Automatic hyperlinking describes the process or the software feature that produces autolinks. Segments of the hypermedia are identified through a process of pattern matching. For example, in hypertext, the software could recognise textual patterns for street addresses, phone numbers, ISBNs, or URLs.

In a distributed hypermedia system, such as the World Wide Web, autolinking can be carried out by client or server software. For example, a web server could add links to a web page as it sends it to a web browser. A browser can also add links to a page after it has received it from the server.

01Examples

Google Toolbar

AutoLink was a feature of the Google Toolbar. Users could convert street addresses or ISBNs in a web page in their browser to links by clicking a button on Google Toolbar. The links directed the users to Google Maps for street addresses and Amazon.com for book information.

Since it was introduced in February 2005, there has been a lot of controversy about this feature. Some webmasters expressed their concerns over the AutoLink feature claiming that Google had no rights to modify their webpages or to direct the users to a commercial website such as Amazon.

This caused Barnes & Noble, a competitor of Amazon, to link all ISBNs on their site back to themselves to prevent people clicking them to take them to their competitor.

Google sceptics compare it with Microsoft's unpopular SmartTags, which ultimately proved too controversial and were dropped.

Trac Wiki Engine

Trac is a web application for issue tracking in software development. It has a wiki engine which is used for all text and documentation in the system. This includes not only wiki pages but also tickets and check-in log messages. These pieces of text have AutoLinks created, for example the text ticket:1 links to ticket 1. This is an example of server-side AutoLinking.

AutoLinker

AutoLinker is a plugin application which has been developed for vBulletin forum software. It allows keywords set by the board administrator to be turned into hyperlinks or popups automatically any time they are used in a forum post or message.

Tomboy Notes

Tomboy Notes is a personal wiki and text editor for Linux that creates automatic hyperlinks for web and email addresses while the text is being edited.

Moodle

The moodle online course platform uses autolinking for automatic linking of terms within a moodle site to a glossary or database entries, or to resources within the same course.

Saga

Saga is a note-taking application that creates automatic links between notes. The application recognizes page titles and headings and automatically creates links to them when they are mentioned inside notes. By using autolinks users can quickly create connected knowledge bases.

InLinks

InLinks is an online software that automates internal linking between web pages by utilizing a named entity detection algorithm to establish connections between pages containing similar entities.

Watch videos about Automatic hyperlinkingExplainers and documentaries on YouTube (opens in a new tab)

Sources and credits

This article is adapted from the Wikipedia article “Automatic hyperlinking”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.

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Related topics

Entity linking

In natural language processing, entity linking, also referred to as named-entity disambiguation, named-entity recognition and disambiguation (NERD), named-entity normalization (NEN), or concept recognition, is the task of assigning a unique identity to entities (such as famous individuals, locations, or companies) mentioned in text. For example, given the sentence "Paris is the capital of France", the main idea is to first identify "Paris" and "France" as named entities, and then to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred to as "Paris" and "France" to the French country.

Named-entity recognition

Named-entity recognition (also known as (named) entity identification, entity chunking, and entity extraction) is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names (PER), organizations (ORG), locations (LOC), geopolitical entities (GPE), vehicles (VEH), medical codes, time expressions, quantities, monetary values, percentages, etc. Most research on NER/NEE systems has been structured as taking an unannotated block of text, such as transducing: Jim bought 300 shares of Acme Corp.

Tf-idf

In information retrieval, tf-idf is a measure of importance of a word to a document in a collection or corpus, adjusted for the fact that some words appear more frequently in general. Like the bag-of-words model, it models a document as a multiset of words, without word order.