Contextual search is a form of optimizing web-based search results based on context provided by the user and the computer being used to enter the query. [1] Contextual search services differ from current search engines based on traditional information retrieval that return lists of documents based on their relevance to the query. Rather, contextual search attempts to increase the precision of results based on how valuable they are to individual users. [2]
The basic form of contextual search is the process of scanning the full-text of a query in order to understand what the user needs. Web search engines scan HTML pages for content and return an index rating based on how relevant the content is to the entered query. HTML pages that have a higher occurrence of query keywords within their content are not rated higher. Users have limited control over the context of their query based on the words they use to search with. [3] For example, users looking for the menu portion of a website can add “menu” to the end of their query to provide the search engine with context of what they need. The next step in contextualizing search is for the search service itself to request information that narrows down the results, such as Google asking for a time range to search within.
Certain search services, including many Meta search engines, request individual contextual information from users to increase the precision of returned documents. Inquirus 2 is a Meta search engine that acts as a mediator between the user query and other search engines. When searching on Inquirus 2, users enter a query and specify constraints such as the information need category, maximum number of hits, and display formats. [4] For example, a user looking for research papers can specify documents with “references” or “abstracts” to be rated higher. If another user is searching for general information on the topic rather than research papers, they can specify the GenScore attribute to have a heavier weight. [5]
Explicitly supplied context effectively increases the precision of results, however, these search services tend to suffer from poor user-experience. Learning the interface of programs like Inquirus can prove challenging for general users without knowledge of search metrics. Aspects of supplied context do appear on major search engines with better user-interaction such as Google and Bing. Google allows users to filter by type: Images, Maps, Shopping, News, Videos, Books, Flights, and Apps. [6] Google has an extensive list of search operators that allow users to explicitly limit results to fit their needs such as restricting certain file types or removing certain words. [7] Bing also uses a similar set of search operators to assist users in explicitly narrowing down the context of their queries. Bing allows users to search within a time range, by file type, by location, language, and more. [8]
There are other systems being developed that are working on automatically inferring the context of user queries based on the content of other documents they view or edit. IBM's Watson Project aims to create a cognitive technology that dynamically learns as it processes user queries. When presented with a query Watson creates a hypothesis that is evaluated against its present bank of knowledge based on previous questions. As related terms and relevant documents are matched against the query, Watson's hypothesis is modified to reflect the new information provided through unstructured data based on information it has obtained in previous situations. [9] Watson's ability to build off previous knowledge allows queries to be automatically filtered for similar contexts in order to supply precise results.
Major search services such as Google, Bing, and Yahoo also have a system of automatically inferring the context of particular user queries. Google tracks user's previous queries and selected results to further personalize results for those individuals. For example, if a user consistently searches for articles related to animals, wild animals, or animal care a search for "jaguar" would rank an article on jaguar cats higher than links to Jaguar Cars. [10] Similar to Watson, search services strive to learn from users based on previous experiences to automatically provide context on current queries. Bing also provides automatic context for particular queries based on content of the query itself. A search of "pizza" returns an interactive list of restaurants and their ratings based on the approximate location of the user's computer. The Bing server automatically infers that when a user searches for a food item they are interested in documents within the context of purchasing that food item or finding restaurants that sell that particular item.
The drive to develop better contextualized search coincides with the increasing popularity of using mobile phones to complete searches. BIA/Kelsey research marketing firm projected that by 2015 mobile local search would "exceed local search by more than 27 billion queries". [11] Mobile phones provide the opportunity to provide search services with a broader supply of contextual information, particularly for location services but also personalized searches based on the wealth of information stored locally on the phone including contacts information, geometric analysis such as speed and elevation, and installed apps. [12]
Google Search is a search engine provided and operated by Google. Handling more than 3.5 billion searches per day, it has a 92% share of the global search engine market. It is the most-visited website in the world. Additionally, it is the most searched and used search engine in the entire world.
Meta elements are tags used in HTML and XHTML documents to provide structured metadata about a Web page. They are part of a web page's head section. Multiple Meta elements with different attributes can be used on the same page. Meta elements can be used to specify page description, keywords and any other metadata not provided through the other head elements and attributes.
A search engine is an information retrieval system designed to help find information stored on a computer system. The search results are usually presented in a list and are commonly called hits. Search engines help to minimize the time required to find information and the amount of information which must be consulted, akin to other techniques for managing information overload.
robots.txt is a standard used by websites to indicate to visiting web crawlers and other web robots which portions of the website they are allowed to visit.
Search engine optimization (SEO) is the process of improving the quality and quantity of website traffic to a website or a web page from search engines. SEO targets unpaid traffic rather than direct traffic or paid traffic. Unpaid traffic may originate from different kinds of searches, including image search, video search, academic search, news search, and industry-specific vertical search engines.
A query language, also known as data query language or database query language (DQL), is a computer language used to make queries in databases and information systems. A well known example is the Structured Query Language (SQL).
Yahoo! Search is a Yahoo! internet search provider that uses Microsoft's Bing search engine to power results, since 2009, apart from four years with Google until 2019.
The Text REtrieval Conference (TREC) is an ongoing series of workshops focusing on a list of different information retrieval (IR) research areas, or tracks. It is co-sponsored by the National Institute of Standards and Technology (NIST) and the Intelligence Advanced Research Projects Activity, and began in 1992 as part of the TIPSTER Text program. Its purpose is to support and encourage research within the information retrieval community by providing the infrastructure necessary for large-scale evaluation of text retrieval methodologies and to increase the speed of lab-to-product transfer of technology.
Federated search retrieves information from a variety of sources via a search application built on top of one or more search engines. A user makes a single query request which is distributed to the search engines, databases or other query engines participating in the federation. The federated search then aggregates the results that are received from the search engines for presentation to the user. Federated search can be used to integrate disparate information resources within a single large organization ("enterprise") or for the entire web.
Contextual advertising is a form of targeted advertising for advertisements appearing on websites or other media, such as content displayed in mobile browsers. In context targeting, advertising media are controlled on the basis of the content of a website using linguistic elements. The advertisements themselves are selected and served by automated systems based on the context of what a user is looking at.
Microsoft Bing is a web search engine owned and operated by Microsoft. The service has its origins in Microsoft's previous search engines: MSN Search, Windows Live Search and later Live Search. Bing provides a variety of search services, including web, video, image and map search products. It is developed using ASP.NET.
Search Engine Results Pages (SERP) are the pages displayed by search engines in response to a query by a user. The main component of the SERP is the listing of results that are returned by the search engine in response to a keyword query. The page that a search engine returns after a user submits a search query. In addition to organic search results, search engine results pages (SERPs) usually include paid search and pay-per-click (PPC) ads.
Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc.
Search engine indexing is the collecting, parsing, and storing of data to facilitate fast and accurate information retrieval. Index design incorporates interdisciplinary concepts from linguistics, cognitive psychology, mathematics, informatics, and computer science. An alternate name for the process, in the context of search engines designed to find web pages on the Internet, is web indexing.
Image meta search is a type of search engine specialised on finding pictures, images, animations etc. Like the text search, image search is an information retrieval system designed to help to find information on the Internet and it allows the user to look for images etc. using keywords or search phrases and to receive a set of thumbnail images, sorted by relevancy.
Discoverability is the degree to which something, especially a piece of content or information, can be found in a search of a file, database, or other information system. Discoverability is a concern in library and information science, many aspects of digital media, software and web development, and in marketing, since products and services cannot be used if people cannot find it or do not understand what it can be used for.
Personalized search refers to web search experiences that are tailored specifically to an individual's interests by incorporating information about the individual beyond the specific query provided. There are two general approaches to personalizing search results, involving modifying the user's query and re-ranking search results.
Multimodal search is a type of search that uses different methods to get relevant results. They can use any kind of search, search by keyword, search by concept, search by example, etc.
Facebook Graph Search was a semantic search engine that was introduced by Facebook in March 2013. It was designed to give answers to user natural language queries rather than a list of links. The name refers to the social graph nature of Facebook, which maps the relationships among users. The Graph Search feature combined the big data acquired from its over one billion users and external data into a search engine providing user-specific search results. In a presentation headed by Facebook CEO Mark Zuckerberg, it was announced that the Graph Search algorithm finds information from within a user's network of friends. Additional results were provided by Microsoft's Bing search engine. In July it was made available to all users using the U.S. English version of Facebook. After being made less publicly visible starting December 2014, the original Graph Search was almost entirely deprecated in June 2019.
Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
