How big enterprises are using Knowledge Graphs. Very good to understand this if you are planning to use knowledge graphs of your own. Instructive. in ACMQueue, CACM Here a short starting excerpt, full text at the link:
Industry-scale Knowledge Graphs: Lessons and Challenges
Five diverse technology companies show how it's done
Natasha Noy, Google; Yuqing Gao, Microsoft; Anshu Jain, IBM Watson; Anant Narayanan, Facebook; Alan Patterson, eBay; Jamie Taylor, Google
Knowledge graphs are critical to many enterprises today: They provide the structured data and factual knowledge that drive many products and make them more intelligent and "magical."
In general, a knowledge graph describes objects of interest and connections between them. For example, a knowledge graph may have nodes for a movie, the actors in this movie, the director, and so on. Each node may have properties such as an actor's name and age. There may be nodes for multiple movies involving a particular actor. The user can then traverse the knowledge graph to collect information on all the movies in which the actor appeared or, if applicable, directed.
Many practical implementations impose constraints on the links in knowledge graphs by defining a schema or ontology. For example, a link from a movie to its director must connect an object of type Movie to an object of type Person. In some cases the links themselves might have their own properties: a link connecting an actor and a movie might have the name of the specific role the actor played. Similarly, a link connecting a politician with a specific role in government might have the time period during which the politician held that role.
Knowledge graphs and similar structures usually provide a shared substrate of knowledge within an organization, allowing different products and applications to use similar vocabulary and to reuse definitions and descriptions that others create. Furthermore, they usually provide a compact formal representation that developers can use to infer new facts and build up the knowledge—for example, using the graph connecting movies and actors to find out which actors frequently appear in movies together.
This article looks at the knowledge graphs of five diverse tech companies, comparing the similarities and differences in their respective experiences of building and using the graphs, and discussing the challenges that all knowledge-driven enterprises face today. The collection of knowledge graphs discussed here covers the breadth of applications, from search, to product descriptions, to social networks:
• Both Microsoft's Bing knowledge graph and the Google Knowledge Graph support search and answering questions in search and during conversations. Starting with the descriptions and connections of people, places, things, and organizations, these graphs include general knowledge about the world. .... "
• Facebook has the world's largest social graph, which also includes information about music, movies, celebrities, and places that Facebook users care about.
• The Product Knowledge Graph at eBay, currently under development, will encode semantic knowledge about products, entities, and the relationships between them and the external world.
• The Knowledge Graph Framework for IBM's Watson Discovery offerings addresses two requirements: one focusing on the use case of discovering nonobvious information, the other on offering a "Build your own knowledge graph" framework.
The goal here is not to describe these knowledge graphs exhaustively, but rather to use the authors' practical experiences in building knowledge graphs in some of the largest technology companies today as a scaffolding to highlight the challenges that any enterprise-scale knowledge graph will face and where some innovative research is needed. ....'
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