When Google crawls the Web to collect information about objects or entities, it also collects facts about those entities. These facts are separated into different categories or attributes associated with those entities. For example, a book may have attributes such as an author, a publisher, a year published, a website it can call home, a genre, and more.
Identifying Entities by their Attributes
A search that includes those attributes can be used to identify the entity the attributes might be associated with.
Google was granted a patent recently that describes how those attributes could be searched within an attribute data store to find the entity. The patent shows how the process described within it might be used to answer some complex queries, and some interactive Answerbox type queries. The issue that this patent addresses can be summed up in a single question:
Continue reading “How Knowledge Base Entities can be Used in Searches”
Years ago, I started referring to search results as recommendations, seeing how they’ve been starting to look more and more like that part of a page at Amazon that says “people who viewed this book also looked at these books.”
When someone searches at a search engine, one of the things they look for in the search results they receive are trustworthy pages (or recommendations) that look (and are) legitimate. How does a search engine deliver pages that are trustworthy?
One way to do that might be to try to boost pages in search results that the search engine feels are more trustworthy – and Google developed a version of Trust Rank to do that with. The inventor of Google’s Trust Rank (which differs from the version that Yahoo invented) is Ramanathan Guha.
Continue reading “Move Over TrustRank, Make Room for Trust Buttons”