In recent years, knowledge graphs have emerged as a critical solution for capturing and structuring an organization’s knowledge, and serving as a foundation for next-generation artificial intelligence (AI) applications. This paper provides an introduction to knowledge graphs, the benefits they provide, who should use them, and the various types of knowledge graphs available in the industry.
As implied by the name, a knowledge graph provides a representation of an organization’s knowledge. A knowledge graph organizes and visualizes an organization’s data in an intuitive “graph” structure based on:
A critical step in the KYC process involves constructing a comprehensive data universe, which integrates various sources such as transactions, KYC data, customer surveys, products, segments, and any additional information. Yet data and analytics leaders frequently face challenges in developing a cohesive view of the business, since much of the essential business domain knowledge is entrenched in traditional database schemas that utilize obscure language and exhibit ambiguous connections to business reality. This is the primary reason why modern knowledge graphs, as graph-structured data models that integrate data, have emerged as a game-changing solution.
The entire data universe is streamlined, organizing all data around business-related sets, including entities, bank accounts, transactions, products, etc. Furthermore, it establishes relationships between these sets, allowing both technical and non-technical users to communicate using the same language. Knowledge graphs serve as the foundation for designing, testing, and launching the complete profile monitoring process. (See Figure 1.)
In Figure 1 above is an example of a knowledge graph in the DataWalk system. The icons represent data sets of entities, which may represent anywhere from a few data elements to many billions of objects. The lines represent the connections, or cross-references, between those data sets. The data within a data set, including its attributes, can be viewed in a separate tabular representation.
In this DataWalk example, the data is organized not around data sources, but has been re-organized around relevant, understandable business objects. This reflects that knowledge graphs are particularly well-suited for representing complex, interconnected information in a manner that is easily understandable by business users.
Note that such data is awkward to understand, model, manage and query with traditional technologies such as SQL databases. More importantly, SQL programming is only understood by skilled technology professionals. Knowledge graphs thus provide a breakthrough capability of enabling complex data and their interconnections to be easily understood, more easily managed, and to be queried much faster than alternative approaches.
Knowledge graphs are particularly well-suited for a wide variety of challenges, including:
Knowledge graphs deliver significant benefits in a variety of fields and applications, including cybersecurity, supply chain management, life sciences, fraud detection, AML/KYC, and many others.
Generally speaking, there are two types of knowledge graphs: property graphs, and knowledge graphs based on Semantic Web. In addition, DataWalk includes unique knowledge graph technology as a component of its data analysis platform, The DataWalk knowledge graph generally delivers the key benefits associated with both property graphs and semantic web, without the deficiencies of either.
For additional details about property graphs, semantic web, and DataWalk, see the paper “DataWalk’s No Compromise Knowledge Graph.”
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