Introduction to Knowledge Graphs:

A Transformative Approach to Working with Data


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.


What is a knowledge graph?

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:

  • Entities - These are data elements that are understandable and relevant to the business. For example, for a bank, key entities might include accounts, customers, and transactions.
  • Attributes - These are the key data associated with an entity. For example, for a person the attributes might be gender, birthdate, and height.
  • Relationships - These are relationships or connections between entities, and are key elements of the knowledge graph. For example, a person might be connected to a home, a vehicle, and a phone number, which is then connected with calls to other phones.


The Knowledge Graph – A Single View of Bank Data

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 Graph Applications

Knowledge graphs are particularly well-suited for a wide variety of challenges, including:

  • Data Modeling and Integration: Knowledge graphs provide a flexible framework for data modeling, which simplifies data integration and is a significant advantage over traditional relational databases. 
  • Entity Resolution: Structuring data around entities and relationships simplifies the process of identifying, organizing, and managing matching records.
  • 360-Degree Views: Being oriented around entities as well as their connections, knowledge graphs inherently enable holistic 360-degree views of entities. 
  • Querying and Data Discovery: Knowledge graphs ensure precise retrieval from complex structures, express intricate queries, and promote the exploration of data patterns. They further enrich information extraction by supporting reasoning and queries of inferred knowledge. 
  • Artificial Intelligence: Using advanced AI techniques such as machine learning, predictive scoring, embeddings, and sophisticated analytic algorithms with a knowledge graph can uncover patterns and anomalies that would otherwise go undetected. 
  • Large language models (LLMs): Recent developments in LLM tools (e.g. ChatGPT) bring language interpretation, sentiment analysis, and comparison capabilities. Combining LLMs with knowledge graphs’ domain-specific, deterministic and analytic strengths opens new opportunities to solve ever more complex problems and overcome LLMs’ greatest weaknesses of hallucinations and lack of domain specific knowledge. 

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.


Types Of Knowledge Graphs: Property Graph and Semantic Web

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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