What is a search engine?

A search engine is a software program or a system designed to help users find information stored on the internet or within a specific database. Search engines work by indexing and cataloging content from various sources and then providing users with a list of relevant results based on their search queries.

Search engines are useful tools for users looking to find specific information quickly and efficiently. They vary in their scope, functionality, and the content types they index. This versatility caters to specific user needs across different contexts. Search engines can be enormous internet search engines designed to catalog everything on the web, or enterprise search engines designed to be used within organizations to make internal information discovery easier. They even include desktop search engines that index files on your local network.

The concept of a search engine predates the internet by several decades. In Vannevar Bush's famous 1945 essay in the Atlantic¹, the Director of the Office of Scientific Research and Development proposed that the United States should undertake a "memex" project. This proposed automatic personal filing system would be "mechanized so that it may be consulted with exceeding speed and flexibility."

In the 1950s, the first automated information retrieval systems were introduced. In the late 1950s, researchers like Gerard Salton began laying the groundwork for automated information retrieval systems for text. Salton developed the SMART (System for the Mechanical Analysis and Retrieval of Text) information retrieval system in the early 1960s. This milestone used mathematical and statistical methods to analyze and retrieve relevant textual information. It also introduced the concept of relevance ranking.

Throughout the 1960s and 1970s, various information retrieval systems continued to evolve, incorporating new techniques such as Boolean searching and the Vector Space Model (a mathematical model that represents objects, such as text, as vectors). The 1980s marked the integration of natural language processing techniques into information retrieval systems, which allowed for more sophisticated analysis of user queries and document content. A big turning point came in 1996 with the debut of WebCrawler, the first automated web search engine. Google's entry in 1998 revolutionized internet search with its PageRank algorithm, significantly enhancing search relevance.

The late 1990s and early 2000s witnessed the diversification of search engines. Enterprise search engines came on the scene as a response to the increasing digitalization of business data. The 2010s saw the rise of open-source enterprise options that offered businesses flexibility and scalability for building their own search functionality. Other new search engines include vertical search engines (which focus on a specific topic) and social media search. Today, with the help of AI, search engines will continue to improve and diversify.

Search engines work by following a multi-step process designed to find the most relevant results to a user’s query. From enterprise search engines to internet search, most follow a similar process.

For internet-based search engines, the search engine deploys automated bots, known as web crawlers or spiders, to look for information. These bots start by visiting a set of known web pages. From those, they extract and follow links to other pages, creating a map of interconnected web pages. In enterprise search engines, different methods are used to explore internal databases, documents, and other repositories within an organization, often APIs and purpose-built connectors.

After crawling, indexing begins. The crawlers analyze the content of each web page, internal document, or other piece of data to extract relevant information, keywords, and metadata. The collected information is then organized into an index. An index is a structured database that allows for quick and efficient retrieval of information. It usually includes details about the content and the location of the web page or document.

The next steps are query submission and analysis. When a user enters a search query, the search engine processes the query to understand the user's intent. It breaks down the query by identifying keywords, phrases, and user context. Today, many search engines use natural language processing (NLP) techniques to better understand the meaning behind the words.

The search engine then matches the user's query against the indexed content. It identifies documents or web pages that contain relevant keywords or phrases. Relevant results are retrieved from the index. The search engine generates a list of potential matches based on its algorithms.

Algorithmic ranking organizes results based on various factors like relevance or the authority of the source. Some measure by content relevance, some take into account metrics like bounce rate and historic engagement, while others measure click-through rates and even provide automated suggestions based on user engagement data. Often, search engines use proprietary algorithms and complex ranking algorithms to discern the order to list results. In enterprise search, the hierarchical results list can be tuned and even curated to prioritize certain results.

Finally, the search engine presents the ranked results to the user on a search engine results page (SERP). Each result typically includes a title, description, and URL or other information location. Users can click on the search results to access the full content of a web page or document.

Search engines are constantly crawling, indexing, and refining their algorithms to provide users with the most relevant and up-to-date information. They are also continuously monitoring user behavior, including click-through rates and dwell time on pages. This data helps refine ranking algorithms and improve result relevance over time.

Search engines are important because they serve as gateways to the huge amount of information available on the internet and various digital platforms. In a world inundated with data, search engines act as navigational tools, helping users quickly find whatever information they are looking for.

Whether they are being used for academic research, enterprise business queries, or simply shopping online, search engines streamline the search process by indexing and organizing online information and internal data to make it easily accessible.

Search engines offer different possibilities in different industries. In the world of ecommerce, for example, they can act as a bridge between content creators and consumers, fostering a digital ecosystem. For researchers and students across every level of academia, search engines are a vital tool for education and discovery. Search engines can support legal discovery by providing access to legal information and databases. Similarly, they can support healthcare professionals by indexing files and aggregating information from medical journals. In most industries, search engines support and streamline the research and discovery process, making them vital to our daily lives.

Search engines come in a variety of forms, each designed to cater to specific needs and contexts. Here are some different types of search engines:

Internet search engines: By indexing and retrieving information from the internet, internet search engines can take user queries and generate relevant web pages, images, videos, and more. Note that an internet search engine is not the same thing as a web browser, which is a software application that displays web pages. A web browser is used to get to the search engine.

Enterprise search engines: Designed for internal organizational use, enterprise search engines index and retrieve information within a company's databases, documents, and intranet.

Desktop search engines: By indexing files, applications, and documents on a user's personal computer or local network, desktop search engines provide the user with quick local search capabilities.

Academic search engines: Focusing on scholarly content, including research papers, articles, and academic publications, academic search engines cater to the needs of researchers and students.

Social media search engines: Social media search engines can index content on social media platforms, which allows users to search for topics connected to posts and discussions as well as find other user profiles.

Meta search engines: These aggregate results from multiple search engines to provide users with a broader perspective and are often used to compare results.

Vertical search engines and specialized search engines: These terms are sometimes used interchangeably. However, there are important distinctions between them. The main difference lies in the scope of coverage. Specialized search engines usually have a narrower focus, honing in on unique content types, while vertical search engines cover a broader spectrum within a specific industry or topic.

Search engine optimization (SEO) is primarily associated with internet search engines. SEO strategies are designed to enhance a website's visibility and ranking in the search engine results pages (SERPs) for specific keywords and queries.

The principles of SEO can also be applied to other types of search engines, depending on the context. For example, organizations may employ SEO techniques in their enterprise search engines to optimize their internal content. Developers of specialized search engines can modify SEO techniques and apply them to images, videos, or other types of content. Even with these modifications, the core principles of SEO remain consistent.

Different SEO subtypes are used within the context of internet search engines:

• Technical SEO optimizes the technical aspects of a website to enhance its search engine visibility and user experience. Website speed, mobile friendliness, site architecture, and HTTPS security are some of the many considerations in technical SEO.
• On-page SEO optimizes individual pages on a website to rank higher and attract relevant traffic. Keyword optimization, meta tags, header tags, URL structure, and page content are all factors in on-page SEO.
• Off-page SEO involves activities performed outside the website to enhance its rankings. This can involve acquiring relevant backlinks from other reputable websites, leveraging social media platforms, and other external promotions such as influencer outreach.

Several new trends are likely to shape the future of search engines. Here are some to think about:

• Zero-click searches (where a user gets the information they need from the search results page instead of clicking on a link) will increase in prevalence. This will be enhanced by generative AI, which gathers information from various sources to answer a user’s query.
• Search engines will increasingly leverage artificial intelligence for personalization as well. The AI will factor in a user’s preferences and their behavior (as well as any other relevant context, such as location) in its results.
• Voice-activated devices and improvements in natural language processing are already leading to an increased reliance on voice search. Search engines will evolve to better understand and respond to conversational queries.
• Privacy concerns will drive the development of privacy-focused search engines. Users may gravitate towards search engines that prioritize data protection and provide anonymous search options.
• Visual search will continue to advance. Users will be able to create complex queries using text, images, and video (called multimodal search) in addition to what is currently available (such as adding text prompts to a visual search).
• Question answering has become commonplace to provide featured snippets of quick, concise answers to user queries at the top of search results. Optimizing content for question-answering is a growing SEO strategy on internet search engines.

Elastic is the perfect choice for building personalized, scalable, AI search experiences for your business. With Elastic, you will get best-in-class textual, vector, hybrid, and semantic search, generative AI integration, and support for NLP transformer models and third-party model management. Learn more about why Elastic is the industry standard for building AI search applications today.

• Elasticsearch: The heart of the open source Elastic Stack
• Video: Getting started with Elasticsearch
• Dive deeper into the new Elasticsearch Relevance Engine

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Vannevar Bush, "As We May Think," July 1945, The Atlantic, https://www.theatlantic.com/magazine/archive/1945/07/as-we-may-think/303881/