Think of all the times you’ve searched Google. Are you searching for information or for knowledge? If you’re not sure of the difference, meet Yebol, a semantic search engine that can teach you a thing or two about search.
Search v. Discovery
Sometimes we search for information we know we’re looking for and for that we employ machine-generated knowledge systems, like Google. But sometimes we search to discover information we didn’t know about at all, like we do on Wikipedia, a human-generated knowledge system, that is, a system that is populated by user-generated content.
Discovering information is much more complex because it relies on the creation of conceptual relationships which helps to improve the accuracy of a search by understanding searcher intent. With semantic search, if you search for two or more terms, you will find occurrences of a conceptual relationship, not just the terms scattered within the same document, like traditional machine-generated search engines provide.
Searching and discovering are equally valuable, but understanding what you seek to gain from each can help you know where to go to get the information that you need.
A Multi-Dimensional User Interface
Approximately nine months ago, Yebol launched its beta version of its semantic search engine. This month as they plan to move out of beta, Yebol hopes that its advanced application of algorithms paired with human knowledge can provide the “first truly human-like world's knowledge base.”
Yebol’s multi-dimensional user interface features a categorical tree system, displaying a summary of top sites and categories about any given search term, while also visually displaying results matching user intent. The goal is to let users generate richer, more comprehensive search results displayed on a single page.
The Search for Human Knowledge
Semantic search inherently puts information -- unstructured, static and redundant against knowledge -- structured, connected, categorized and ranked with meaning.
Human knowledge is semantic search speak for creating relations between concepts. By utilizing a combination of patented algorithms paired with such conceptual relationships, Yebol has built a web directory for each search term and user.
Yebol uses the Amazon.com clouding computing service, and on it has built a knowledge base for about 10 million concepts over 1 billion web pages. With room to build a knowledge base of 100 million concepts over 10 billion web pages, Yebol is prepared for the future.
Dynamic, Fluid & Scalable in Any Language
Yebol automatically clusters and categorizes search terms, web sites, pages and contents. The beauty of Yebol is that it’s fluid, dynamic and scalable -- users can decide what they want to see more of.
Designed and developed for the English language, Yebol’s founder Dr. Hongfeng Yin, a former Yahoo engineer with more than 20 years of R&D experience on artificial intelligence, pattern recognition, data mining and search, thinks that Yebol can be easily applied to any language.
A Complement, Not a Replacement
Yebol claims to be at the “beginning of the knowledge revolution” and is working to bridge the gap between results provided by machine-based and human-generated knowledge systems so that users can find both information and knowledge that brings context to the content.
No semantic search engine is designed to replace the machine-generated search engine. Rather it exists to provide more options and enhance the accuracy of the search.