Abstract: An apparatus and method are disclosed for producing a semantic representation of information in a semantic space. The information is first represented in a table that stores values which indicate a relationship with predetermined categories. The categories correspond to dimensions in the semantic space. The significance of the information with respect to the predetermined categories is then determined. A trainable semantic vector (TSV) is constructed to provide a semantic representation of the information. The TSV has dimensions equal to the number of predetermined categories and represents the significance of the information relative to each of the predetermined categories. Various types of manipulation and analysis, such as searching, classification, and clustering, can subsequently be performed on a semantic level.

Abstract: Techniques are provided for delivering search results pages to the users of a search engine, where one or more search result listings on the search results pages include items that are not included by default. The non-default items may take many forms, including but not limited to images, banners, controls, animations, and even Java applets. The non-default items may be designed to entice the search engine users to select the search result listing with which they are associated over possibly higher-ranked search result listings that contain only default items.

Abstract: A method of determining a relationship between a search item provided by a user and a plurality of objective items, wherein each objective item in the plurality includes a plurality of objective item properties and the search item includes one or more search item properties, the method comprising: grouping a subset of objective items based on an objective relationship between one or more of the plurality of objective item properties and the one or more search item properties; modifying the subset based on a subjective relationship, wherein the subjective relationship indicates an association between objective items in the subset and the search item, thereby calculating an affinity value between each objective item in the subset and the search item; and ranking the objective items in the modified subset based on the affinity value calculated.