Yoogli is the next generation of search technology that matches complex queries with more exacting results. Search terms are converted into a patented semantic model; a model that represents not the words, but their actual meaning. The semantic model of search terms is compared against the semantic models of the resources available in the global search space. The engine is able to correctly understand and analyze complete pages of text, documents and URLs and deliver more targeted and related results than keyword search. It is able to drill down deeper into a specific result thus continuously refining the desired result for the user. An even more significant result is the ability to represent people as semantic vectors, as well as specific interest groups that people form. This connects a user with other people with the information that is most valuable and useful to them. The technology also advances social networking to the next level. The tool allows people to find and access libraries of information that are of interest to them posted by other people. Collaborative filtering technology is able to identify people who have established themselves as experts in a particular field who may then be connected with others of like interests.
Yoogli is a semantic search engine that may be used with Google in order to achieve more relevant and deeper search results. Yoogli uses patented semantic search technology. The technology developed by Yoogli is protected under patents 7013300, 7219073, 7881981 and 8027876. The patents embody a locating, filtering, matching macro-context from indexed database for searching context where micro-context relevant to textual input by user. The inventions relate to a data extraction tool and, more particularly, to novel systems and methods for organizing information from a database for ready access by a user. The technology covers semantic search, e-commerce, advertising placement, certified coupon delivery and click fraud protection.
Yoogli Semantic Search:
Semantic search improves search accuracy by understanding searcher intent and the contextual meaning of terms as they appear in the searchable dataspace, whether on the Web or within a closed system, to generate more relevant results. Semantic search systems consider various points including context of search, location, intent, variation of words, synonyms, generalized and specialized queries, concept matching and natural language queries to provide relevant search results. Using semantic search the user provides the search engine with a phrase, which is intended to denote an object about which the user is trying to gather information. There is no particular document, which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents, which together will provide the desired information. Semantic search is closely related with exploratory search. Rather than using ranking algorithms such as Google’s PageRank to predict relevancy, semantic search uses semantics, or the science of meaning in language, to produce highly relevant search results. In most cases, the goal is to deliver the information queried by a user rather than have a user sort through a list of loosely related keyword results.
PageRank is an algorithm used by Google Search to rank websites in their search engine results. PageRank was named after Larry Page, one of the founders of Google. PageRank is a way of measuring the importance of website pages. According to Google: PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites. A PageRank results from a mathematical algorithm based on the webgraph, created by all World Wide Web pages as nodes and hyperlinks as edges, taking into consideration authority hubs such as cnn.com or usa.gov. The rank value indicates an importance of a particular page. A hyperlink to a page counts as a vote of support. The PageRank of a page is defined recursively and depends on the number and PageRank metric of all pages.
The Taylor Rank algorithm is an automated computer program that continuously re-creates, re-masters and improves semantic search results for users. The algorithm is based on an analysis of each user’s historical search footprint on a specific subject combined with all other searches on the same subject, rather than an analysis of the quality and number of links to each page searched for as is the case with Google’s Page Rank algorithm. The Taylor Rank algorithm learns from itself, continuously updates itself, and provides more perfected and more pinpointed search results to the user. In other words, each Yoogli user may have a unique custom semantic search algorithm that understands and responds to each user’s specific search requests, based on both their own search footprint as well as the cumulative search footprints of thousands of other search users seeking the same information from their individual search queries. The Taylor Rank technology takes the patented semantic search technology developed by Yoogli to the next level in providing more pinpointed search results for a user. It does this by adding and examining the behavioral paths that humans take using the Yoogli technology in harnessing prior semantic search results to achieve even more optimal search results against a specific subject of interest.The beginning of the search path that all users take begins when they choose “Similar Page Links” from the first Yoogli keyword search result from Wikipedia as the initial search result delivered to a user. This is the embodiment of “semantic network intelligence” which is a precursor of “semantic artificial intelligence”.