How might Google improve on information from sources such as knowledge bases to help them answer search queries?
That information may be learned from or inferred from sources outside of those knowledge bases when Google may:
- Analyze and annotate images
- Consider other data sources
A recent Google patent on this topic defines knowledge bases for us, why those are important, and it points out examples of how Google looks at entities while it may annotate images:
A knowledge base is an important repository of structured and unstructured data. The data stored in a knowledge base may include information such as entities, facts about entities, and relationships between entities. This information can be used to assist with or satisfy user search queries processed by a search engine.
Examples of knowledge bases include Google Knowledge Graph and Knowledge Vault, Microsoft Satori Knowledge Base, DBpedia, Yahoo! Knowledge Base, and Wolfram Knowledgebase.
Continue reading “How Google May Annotate Images to Improve Search Results”
Searching for Quotes has shifted at Google with an Updated Continuation Patent
In August of 2017, I wrote the post Google Searching Quotes of Entities. I wrote about the patent Systems and methods for searching quotes of entities using a database.
This patent was updated last year (February 2019) with a continuation patent. I like comparing older patents with claims from newer continuation patents. They are a way of saying, “We used to do something one way, but we changed how we do it. To protect our intellectual property, we have updated the claims with a newer version of it.”
Reviewing the Patents on quote searching
Continue reading “Google Has Updated Quote Searching to Focus on Videos”
A recently granted patent from Google covers supporting querying and predictions. It does this by focusing on user-specific knowledge graphs.
Those User Specific Knowledge Graphs can be specific to particular users.
Google can use those graphs to provide results in response to one or more queries submitted by the user, and/or to show data that is relevant to the user.
I thought of another patent I wrote about when I saw this patent, in the post Answering Questions Using Knowledge Graphs. In that one, Google may search on a question someone asks by building a knowledge graph from search results, to use to find the answer to their question.
So Google doesn’t just have one knowledge graph but may use many knowledge graphs.
New ones for questions that may be asked, or for different people asking those questions.
Continue reading “User-Specific Knowledge Graphs to Support Queries and Predictions”
What are Augmented Search Queries?
Last year, I wrote a post called Quality Scores for Queries: Structured Data, Synthetic Queries and Augmentation Queries. It told us Google may look at query logs and structured data (table data and schema data) that are related to a site to create augmentation queries, and evaluate information about searches for those queries by comparing them to original queries for pages from that site. If search results from the augmentation queries do well in evaluations compared to search results from original query results, searchers may see search results that are a combination of results from the original queries and the augmentation queries.
Around the time that patent was granted to Google another patent that talks about augmented search queries was also granted to Google, and is worth talking about at the same time with the patent I wrote about last year. It takes the concept of adding results from augmented search queries together with original search results, but it has a different way of coming up with augmented search queries, This newer patent that I am writing about starts off by telling us what the patent is about:
This disclosure relates generally to providing search results in response to a search query containing an entity reference. Search engines receive search queries containing a reference to a person, such as a person’s name. Results to these queries are oftentimes not sufficiently organized, not comprehensive enough, or otherwise not presented in a useful way.
Continue reading “Augmented Search Queries Using Knowledge Graph Information”
Exploring how the Google Knowledge Graph works provides insights into how it is growing and improving and may influence what we see on the web. A newly granted Google patent from last month is about how Google improves the amount of data the Google Knowledge Graph contains.
The process in that patent differs from the patent I wrote about in How the Google Knowledge Graph Updates Itself by Answering Questions. Taken together, they tell us about how the knowledge graph is growing and improving. Part of the process involves entity extraction which I covered in Entity Extractions for Knowledge Graphs at Google.
This new patent tells us that information making its way into the knowledge graph is not limited to content from the Web, but also can “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 Google Knowledge Graph Reconciliation is?
Continue reading “Google Knowledge Graph Reconciliation”