There is a great deal of talk these days about using knowledge graphs to help manage large amounts of data. In this blog post, I’d like to introduce you to knowledge graphs.
Let’s start with a basic definition. What, exactly, is a graph?
Graphs and Graph Databases
According to the Wikipedia article on Graph Theory, “[Graphs] are mathematical structures used to model pairwise relations between objects.”
A graph consists of multiple nodes (aka points or vertices) connected by edges (aka links, or relationships). In a directed graph, these edges have direction from one node to another.
A graph database is a data storage structure that organizes information using graphs instead of tables. Like their mathematical predecessors, graph databases consist of nodes and relationships. Nodes and relationships have labels, and nodes also have attributes (or properties).
Graph Databases Provide Semantic Richness
Another way to view these nodes, relationships, and attributes is through the lens of grammar. A node is a noun. A relationship is a verb. Attributes for nouns are like adjectives and attributes for relationships are like adverbs.
The value of storing information as graphs comes down to semantic richness. Compared to traditional table-based relational databases, graph databases are a more accurate representation of real world information structures. A graph database makes the data and any insights or patterns within the data more accessible to both humans traversing the graph and computers performing queries on the graph.
A graph database that represents real-world information is called a knowledge graph.
To demonstrate the semantic value of graphs, here is information about Content Rules and me, in both sentence and graph form.
Content Rules, Inc. is a business that was founded in 1994 and is based in the state of California. Content Rules employs a person named Max as its Office Manager. Max is writing a blog post on the topic of graphs.
As humans, we can read and comprehend the meaning of these sentences and how they relate to each other.
Organized into a graph, the meaning of the sentences can be represented graphically and structurally:
Content Professionals, Knowledge Graphs, and Personalization
Tech giants use artificial intelligence to build and maintain enormous knowledge graphs. Among other things, these knowledge graphs can help companies deliver personalized experiences at scale. A content delivery platform can use a knowledge graph to produce uniquely personalized recommendations for each individual, based on the complex network of individual decisions that the user makes on the platform.
Personalization is possible in part by the advent of Big Data. Companies collect, store, and manage tremendous amounts of data based on our online behavior. They use this information to improve their ability to deliver:
- The right content
- To the right person
- At the right time
- On the right device
- In the language of their choosing
More and more businesses are beginning to embrace knowledge graphs – semantically rich data storage structures – to enable personalization at scale.