Receiving a collection of text (web pages and query logs).

It could start finding semantic classes by: This patent describes ways to extract semantic classes and corresponding instances from a collection of text. An instance is either a one time example of an entity from one of those classes such as “George Washington,” or the “Inverted Jenny” stamp or “Princeton”, or a subclass of class, so lions are a subclass of animals. A class is a type of property value for objects, such as “presidents” or “stamps” or “cities”. The patent uses the language “classes” and “instances”, and it’s important knowing what it means by those words. When the patent says text, they mean (in this instance) of collections of web pages and query logs. So entities do appear in queries, and Google receives a lot of queries a day (as does Microsoft and Yahoo).Ī patent from Google granted last year from the same authors as the patent on extracting entities and data about them in queries, Marius Pasca and Benjamin Van Durme, describes how Google might be mining text for semantic classes and instances of objects (entities) from the text. In my last post, I wrote about how search queries might be used, along with linguistic patterns, to extract attributes about facts from those search queries, as described in a patent titled Inferring attributes from search queries.Ī Microsoft paper from 2009, Named Entity Recognition in Query, tells of a manual analysis they performed of 1,000 queries, and told us that 70% of those queries contained named entities. In creating a knowledge base, there seem to be several approaches that can be used to supply entities and facts from sources like web pages and query logs.

Crunchbase tagger media