Exploring how Google’s knowledge graph works can provide some insights into how is growing and improving and may influence what we see on the web. A newly granted Google patent from the end of last month tells us about one way that Google is using to improve the amount of data that its knowledge graph contains.
The process involved in that patent doesn’t work quite the same way as the patent I wrote about in the post How the Google Knowledge Graph Updates Itself by Answering Questions but taken together, they tell us about how the knowledge graph is growing and improving. But part of the process involves the entity extraction that I wrote about in Google Shows Us How It Uses Entity Extractions for Knowledge Graphs.
This patent tells us that information that may make its way into Google’s knowledge graph isn’t limited to content on the Web, but can also may “originate from another document corpus, such as internal documents not available over the Internet or another private corpus, from a library, from books, from a corpus of scientific data, or from some other large corpus.”
What Knowledge Graph Reconciliation is?
The patent tells us about how a knowledge graph is constructed and processes that it follows to update and improve itself.
The site Wordlift includes some defintions related to Entities and the Semantic Web. The Definition that they provide for reconciling entities means “providing computers with unambiguous identifications of the entities we talk about.” This patent from Google focuses upon a broader use of the word “Reconciliation” and how it applies to knowledge graphs, to make sure that those take advantage of all of the information from web sources that may be entered into those about entities.
This process involves finding missing entities and missing facts about entities from a knowledge graph by using web-based sources to add information to a knowledge graph.
Problems with knowledge graphs