I’ve written a couple of posts about patents at Google on personalized knowledge graphs (a topic worth thinking about seriously).
Personalized Knowledge Graphs are structured information about entities personally related to a particular searcher, the attributes of those entities, the classifications of those entities, and knowledge about relationships between those entities, and between the entities and those attributes.
If Google creates a personalized knowledge graph for you, Google may look at your search history, pages that you have browsed, your emails, your social network posts, and other sources that may contain personalized information about you. Searchers and SEOs do not create personalized knowledge graphs, though Google may create personalized knowledge graphs for specific people.
These are not the knowledge panels that you see about specific entities in search results, but information seen in knowledge panels may be taken from knowledge graphs.
When Google introduced the “Knowledge Graph” in 2012, they told us about just one knowledge graph. But it appears that they didn’t intend the idea of a knowledge graph to be a singular one – there is more than one knowledge graph.
Google later came out with a patent that told us about how each query might return a set of results that a new knowledge graph could be created from to answer the original query. Those mini-knowledge graphs could end up being combined into a larger knowledge graph. I wrote about that patent (filed in 2017) in this post:
Another patent I wrote about was one on User-Specific Knowledge Graphs: User-Specific Knowledge Graphs to Support Queries and Predictions. This patent was filed in November of 2013 and was created back then. These are personalized knowledge graphs based upon information taken from your search history, from pages you have browsed, and from documents such as emails and social networking posts that you have made and received. This patent tells us that these personalized knowledge graphs could be joined together to lead to a universal knowledge graph (combining non-user-specific knowledge graphs, and user-specific knowledge graphs.)
I also wrote about how Google might create personalized entity repositories for people to carry around with them on their mobile devices: A Personalized Entity Repository in the Knowledge Graph. What makes this interesting is that it causes a knowledge base of information to be contained on your computing device, such as a mobile phone or a tablet, which means that your answer doesn’t have to come from a server somewhere, and can come from a knowledge graph built on that personalized knowledge base from an entity repository built from a machine learning approach based on your search history and documents (emails, documents, social network posts) that you may access
A Google whitepaper created for the International Conference on Theory of Information Retrieval (ICTIR) 2019, October 2–5, 2019 – Personal Knowledge Graphs: A Research Agenda by Krisztian Balog and Tom Kenter Captures a lot of the ideas behind the User-Specific Knowledge Graph patent (originally filed in 2013).
The abstract tells us:
Knowledge graphs, organizing structured information about entities, and their attributes and relationships, are ubiquitous today. Entities, in this context, are usually taken to be anyone or anything considered to be globally important. This, however, rules out many entities people interact with daily.
In this position paper, we present the concept of personal knowledge graphs: resources of structured information about entities personally related to its user, including the ones that might not be globally important. We discuss key aspects that separate them for general knowledge graphs, identify the main challenges involved in constructing and using them, and define our search agenda.
The paper tells us about the purposes behind knowledge graphs:
Obvious use cases include enabling rich knowledge panels and direct answers in search result pages, powering smart assistants, supporting data exploration and visualization (tables and graphs), and facilitating media monitoring and reputation management
These are important and essential aspects of how search engines such as Google are working these days. What makes this paper interesting is that it tells us about knowledge graphs that do these things that are personalized to work with individuals. As the authors tell us:
In this position paper, we present the concept of a personal knowledge graph (PKG)—a resource of structured information about entities personally related to its user, their attributes, and the relations between them.
This paper is a good look at the direction that knowledge graphs are evolving towards, and is worth spending time with to see where they might go. This could be very much true when it comes to something such as personal assistants which you may use to with personal errands, such as making a restaurant reservation or booking a flight, or helping with entertainment at homes, such as movies or music or news.
The paper suggests some research that might be done on personalized knowledge graphs in the future and presents several ideas on how to bring these concepts into actual use.
Krisztian Balog was a visiting Scholar at Google for over a year, and a computer science professor when he wrote the above paper. He has an open access book on the Springer Website (at no charge) on Entity Oriented Search, which is highly recommended. It captures well a lot of what I have seen at Google on Entities.