Knowledge Graphs

The most effective way to build and store a knowledge graph is to use a graph model and a graph database. Graph databases are purpose-built to store and navigate relationships. Graph databases make it easier to model and manage highly connected data, treat relationships as “first class citizens,” have flexible schemas, and provide higher performance for graph traversal queries. There are different graph technologies that you may consider.

The World Wide Web Consortium (W3C) has created a set of technical specifications for representing graphs, querying them, and for building graph schemas: the specifications for the Resource Description Framework (RDF) graphs, the SPARQL query language, and the Web Ontology Language (OWL) collectively make up what is known as the Semantic Web, a vision of how knowledge could be structured and managed in a distributed fashion; work on the Semantic Web in the early 2000s laid the groundwork for modern knowledge graphs. The seminal article on this topic was published in Scientific American in 2001.

There are also other, de facto industry standards for graphs. Property graphs offer developers a different way to structure their graph model. Neptune supports both RDF as well as property graphs, the latter through the Gremlin query language from the Apache TinkerPop open source project.

While the Semantic Web was intended to be a vast effort to make lots of knowledge accessible on the Web, an enterprise knowledge graph can use the graph technologies in the context of a single organization or company. An enterprise knowledge graph can integrate information from different parts of the company and between proverbial “data silos”, turning the data an organization collects into a valuable and useful asset. This promotes information sharing and reuse. Federated query capabilities built into the Amazon Neptune graph database allow you to build enterprise knowledge graphs that can also access public data on the Web to augment the knowledge contained therein. 

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Amazon Neptune is purpose-built for storing billions of relationships and querying the graph with milliseconds latency. Amazon Neptune is compatible with open graph APIs, and supports popular graph models Property Graph and W3C's RDF, and their respective query languages Apache TinkerPop Gremlin and SPARQL. While graph databases usually require extensive hardware management, provisioning, and manual scaling, Amazon Neptune is a fully managed service, so you no longer have to worry about database management tasks. You can be up and running with an Amazon Neptune graph cluster in a matter of minutes with a few clicks in the AWS management console or with the AWS CLI.

If you searched the web for the longest river in the world, browsed through a list of recommended movies to watch on a Friday, or checked your daily schedule with Alexa, chances are you were interacting with a knowledge graph. Simply put, a knowledge graph is a means of structuring and organizing information for easier access and understanding. It “democratizes” data in an organization by allowing more people to understand and access data.

Graphs are a natural way to model and represent information about the world. This idea is not new, but has now become more viable via the introduction of scalable graph databases. Unlike traditional ways of managing data, such as relational databases, graph modeling is very flexible and allows for the real-world diversity and heterogeneity of data. This lets us model complex and complicated subject matter.

A knowledge graph captures the semantics of a particular domain using a set of definitions of concepts, their properties, relations between them, and logical constraints that are expected to hold. Logic built into such a model allows us to reason about a graph and information contained within, and to make implicit information in the graph explicitly accessible.

Knowledge graphs consolidate and integrate an organization’s information assets and make them more readily available to all members of the organization. There are many applications and use cases that are enabled by knowledge graphs. Information from disparate data sources can be linked and made accessible to answer questions you may not even have thought of yet. Information and entities can be extracted not only from structured sources (e.g., relational databases) but also from semi-structured sources (e.g., media metadata, spreadsheets) and unstructured sources (e.g., text documents, email, news articles).