Web Search Engine
A system that crawls the web, indexes what it finds, and ranks pages in answer to typed queries, the main way people have found things online since the late 1990s and one of the largest advertising businesses ever built.
A web search engine finds pages in answer to a typed query. It does three jobs in sequence: it gathers pages, it builds an index of the words they contain, and at query time it ranks the matching pages and shows them as a list of results.
Gathering is done by programs that fetch a page, extract its links, and queue those links for fetching in turn; see Web Crawler. Indexing turns the collected text into a structure that maps each word to the documents containing it, so a query can be answered without scanning everything. Ranking is where engines compete. Early systems scored pages mainly by how often and where query words appeared, which made them easy to game by stuffing pages with repeated terms.
The first web era leaned on hand-built catalogs as much as on search. Full-text engines such as WebCrawler, Lycos, and AltaVista arrived in 1994 and 1995 and indexed millions of pages, but results were noisy. Google, started by Larry Page and Sergey Brin at Stanford and incorporated in 1998, ranked pages partly by the links pointing at them, treating each link as a vote weighted by the importance of the page casting it. Modern engines combine hundreds of signals, including freshness, location, and how searchers respond to results.
Search became a business through advertising sold against queries. Advertisers bid on keywords and pay when someone clicks, a model pioneered by GoTo.com in 1998 and refined by Google's AdWords from 2000. Because a query states intent directly, these ads are unusually valuable, and the results page gradually filled with ads, maps, and boxes answering questions without a click. A two-sided business with users on one side and advertisers on the other tends toward concentration. In 2024, a US federal court ruled that Google had illegally maintained a monopoly in general search.
Every ranking system invites manipulation. A whole industry tries to move pages up the results; see Search Engine Optimization. Link farms, content farms, and spam pages exist mainly to be found. Personalized ranking raised the worry that people see only results confirming what they already believe; see Filter Bubble.
Since 2023, engines have added generated answers above the links, summarizing retrieved pages with a language model; see Retrieval-Augmented Generation. This keeps more visitors on the results page and reduces the traffic that sites receive, unsettling the arrangement in which sites let themselves be crawled in exchange for being found. Search quality is often framed as the trade-off between finding all relevant pages and showing only relevant ones; see Precision and Recall.
See also3
Autocomplete
Suggesting completions while someone types, which needs prefix-friendly indexes, ranking by likely intent, and careful handling of latency, typos, and accessibility.
Data & Content14 connections
Structured Data
Machine-readable markup describing what a page is about, in a shared vocabulary.
Data & Content14 connections
Canonical URL
The one preferred address for a page that's reachable at many, declared so search engines and other tools treat the copies as a single page, a hint introduced by the major search engines together in 2009.
Data & Content10 connections
Linked from6
- Canonical URLData & Content
The one preferred address for a page that's reachable at many, declared so search engines and other tools treat the copies as a single page, a hint introduced by the major search engines together in 2009.
- Filter BubbleMeaning & Society
A personalization system narrows what a person sees to what they already agree with, without their choosing it.
- Precision and RecallAgents & Language Models
The two measures of how well a classifier or search finds what it should, how often its hits are right and how many of the real cases it catches, and why improving one usually costs the other.
- Retrieval-Augmented GenerationAgents & Language Models
Fetching relevant documents at query time and feeding them into the prompt, so a model answers from retrieved text rather than memory.
- Stemming and LemmatizationData & Content
Reducing words to a common base form so a search for "running" also finds "runs" and "ran," with stemming chopping suffixes by rule and lemmatization looking up the dictionary form.
- Structured DataData & Content
Machine-readable markup describing what a page is about, in a shared vocabulary.