Abstract: Architecture that uses content from off-page data sources such as feeds (e.g., yellow pages, coupons, social networks, commerce, etc.) to present additional, relevant information in association with search results. The additional and relevant information is directly relevant to the implicit task the user is trying to accomplish. The architecture includes online and offline mechanisms that identify an entity represented on a web page and look-up information related to that entity in disparate data sources. Relevance heuristics are employed to determine which of the available entity data to show in the caption given the user query, the web page, and the underlying user task (other known information about the user such as geographic location).

Abstract: Architecture that inserts one or more label items in search result entries. In addition to the typical search result caption (title, snippet, and link), the architecture includes the label component of one or more of the label items in the result entry. The number and type of label annotations are based on the query. When a particular label item is selected (e.g., hover, mouse click), a presentation component (e.g., expansion object, pop-up window) launches proximate to a label item in response to interaction with the label item and presents additional information from the target webpage. The additional information can include an action and data related to the search result entry and the target webpage. The data can be obtained from a data source other than the target webpage.

Abstract: Architecture that computes a dominant image from one or more images on a webpage. A dominant image classifier scans webpages in an offline-created index to identify the prominent images in the webpages. In a more specific implementation the image selected is the image associated with a name query. Face detection technology can be utilized to identify which of the images on a given webpage contain faces. A query classifier identifies queries that contain people names. In the context of search engines and search result pages, the web results for name queries can further include prominent people face images as thumbnail images. Additional facts (structured data) can further be included that together with the results elements of caption title, snippet and attribute (uniform resource locator (URL)) provide an improved summary of the person on the page.

Abstract: Architecture that uses content from off-page data sources such as feeds (e.g., yellow pages, coupons, social networks, commerce, etc.) to present additional, relevant information in association with search results. The additional and relevant information is directly relevant to the implicit task the user is trying to accomplish. The architecture includes online and offline mechanisms that identify an entity represented on a web page and look-up information related to that entity in disparate data sources. Relevance heuristics are employed to determine which of the available entity data to show in the caption given the user query, the web page, and the underlying user task (other known information about the user such as geographic location).

Abstract: Architecture that inserts one or more label items in search result entries. In addition to the typical search result caption (title, snippet, and link), the architecture includes the label component of one or more of the label items in the result entry. The number and type of label annotations are based on the query. When a particular label item is selected (e.g., hover, mouse click), a presentation component (e.g., expansion object, pop-up window) launches proximate to a label item in response to interaction with the label item and presents additional information from the target webpage. The additional information can include an action and data related to the search result entry and the target webpage. The data can be obtained from a data source other than the target webpage.

Abstract: Architecture that generates and presents a separator interface element in association with subsnippets (of search results) to indicate to the user the closeness relationship between subsnippets. The closeness can be presented in terms of bytes, words, paragraphs, semantic distance, rhetorical relationship, and so on, which assist the user in determining how closely related the parts of the document are in which the search terms appear. The element can be a box, for example, that is sized in length proportional to an inter-subsnippet closeness relationship.