Read about how Ontotext Platform utilizes its potential to lower the entry barrier to knowledge graph data in an exploration of the Star Wars universe.
The term “knowledge graph” (KG) has been gaining popularity for quite a while now. Today, as the number of decision-makers recognizing the importance of more dynamic, contextually aware and intelligent information architectures is growing, so is the number of companies with solutions based on knowledge graphs.
Despite this knowledge graphs upsurge, the concept still lives without an agreed-upon description or shared understanding of the methodology used for its designing.
Here, at Ontotext, we work with the following definition of what is a knowledge graph:
The knowledge graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts – where:
To take the conversation forward, we have also decided to outline the main steps of building and maintaining a knowledge graph, based on our extensive experience.
After working with many clients and on many research projects, we can outline 10 steps of creating a knowledge graph. Each of them takes time and needs careful consideration to ensure it meets the goals of the particular business case it has to serve. As a result, a knowledge graph crafted with a view to a specific context and business data needs immensely broadens the opportunities this technology opens for smart data management.
Here is our list of how to build a knowledge graph:
The reason we prefer to have a knowledge graph built with semantic technology is that we like to craft structures that move businesses forward. Because with semantic data we don’t only store data but also have the tools for interpreting it in a way that suits different information needs and helps gain different perspectives.
Functionally, semantic data modeling is about understanding what the data is about and making the knowledge locked in it more explicit. It’s about translating disparate data into information that can be consumed (via queries, via visualization) for different decision-making purposes.
And when it comes to building knowledge graphs done the semantic data modeling way, we have learned from our clients and projects, that this approach offers organizations much more opportunities to transform and interlink data into coherent knowledge. Semantic metadata makes relevant fragments easy to discover and reuse, despite syntactic discrepancies of the schemata of the original sources. Using RDF and other W3C standards to represent your knowledge graph guarantees that your data can be referenced, understood and interpreted in a uniform manner, without dependencies on specific tool vendor’s conventions or undocumented business logic buried into source code.
Technology is about craftsmanship. The very root techne (tekhnē) has implicitly kept this meaning throughout the centuries – it means ‘art, craft’. The process of crafting a knowledge graph has to do with mastery. And mastery here is the ability and the art of gathering datasets, choosing the right way to use them, cleaning and normalizing the data, analyzing the input and preparing it to serve the customized domain model that needs to be built.
The process can never be the same and is no trivial task. It takes dedication, expertise and knowledge of the techniques and approaches that would best serve this challenge. As Amit Sheth wrote in his Why do these Knowledge Graphs need 10,000 pairs of hands?:
Building a KG is a human-intensive process, and humans are primarily involved not only for schema level issues but also instance/fact level issues.
Add to this the fact that businesses have their uniqueness and individuality and a one-fits-all solution cannot be an option and it never has been. Instead, a craft approach is required here.
The steps we have described in this blog post are a solid way to define a project and make the most of building a knowledge graph. They all have their intricacies and there is no singular way to derive value from data.
One thing is for sure, though. Built one way or another, the knowledge graph is to continue helping enterprises navigate the complex world of data and data-based decision-making.