Primer on Knowledge Graphs 

Knowledge graphs

Graphs have been relevant to computer science for a long time for visualizing complex data as a network of human-understandable information. As the internet grew popular and industries began to digitize, data grew exponentially. As data grew, data sources began to decentralize to handle its complexities. Consequently, we saw a surge in the requirement for systems and technologies that store and manage complex large-scale data. 

Fortunately, the need for integrating, storing, and contextualizing large volumes of decentralized information has finally been manifested as a single intelligent system: Knowledge Graphs.

This article will discuss the background of knowledge graphs and introduce you to the basic concepts behind them. Continue reading to learn the basics of knowledge graphs.

What is a Knowledge Graph?

A knowledge graph is a graphical illustration of real-world knowledge. The information in a knowledge graph is represented as nodes and edges linked together in a network. The two key elements of a knowledge graph include:

  • Data Entities: Data entities in a knowledge graph refer to real-world objects or entities. These entities are represented as a node in a knowledge graph. Examples of entities include people, places, events, etc.
  • Relationships: Relationships in a knowledge graph refer to the association between data entities. Relationships are represented as edges in a knowledge graph. An edge can be unidirectional or bidirectional, depending on the relationship between two nodes. Examples of relationships include ‘brother of,’ ‘works at,’ ‘born in,’ etc.

All this information regarding entities and their relationships is stored in a graph database. The graph database serves as a knowledge base for a knowledge graph. 

A knowledge graph gives context and meaning to structured and unstructured data in a format understandable by humans and machines alike. It is characterized by its ability to gather and store real-world data, integrates with various sources, and map relationships across any data store.

Knowledge graphs are mainly used in industries to retrieve meaningful facts from organizational data. The ability of knowledge graphs to manage the fluctuating nature of real-world data makes them excellent for modern data analysis and discovering new facts. 

As an example, consider the Biopharma industry. Knowledge graphs prove revolutionary for the biopharma industry for discovering new drugs from historical medical data. For further information: Knowledge Graphs in Drug Discovery.

Knowledge Graph-Background 

Brief History of Knowledge Graphs

Graphs were already popular among data scientists for data modeling way before the advent of the first computer. Although Google officially coined the term ‘Knowledge Graph’ in 2012, its concepts date back to the 1950s. By the 1970s, data scientists realized the potential of combining and representing data and knowledge. 

Data began to grow dramatically during the digitization boom in the early 2000s and paved the way for new technologies and concepts like the ‘Semantic Web‘.

Knowledge graphs are a blend of great ideas from computer science history. It is a materialization of an essential requirement of machines to understand the contextual representations of knowledge. The idea of representing data and knowledge at a web scale, combined with integrating data from disparate sources, set the stage for the knowledge graphs we use today. 

How is Knowledge Different From Data?

Data refers to the raw, unprocessed facts obtained and observed as mere symbols and characters.

On the other hand, knowledge refers to the contextual information that gives meaning to data. When you attach context to data, it becomes information. Upon further processing, this information is used to obtain knowledge about data facts and their correlations. 

An important distinction between data and knowledge is that knowledge can be inferred from existing information. Moreover, knowledge can produce actionable information that can be used to make critical decisions. In contrast, data exists as it is, meaning that raw data cannot be used for insights.

How is a Knowledge Graph Different From a Normal Graph?

While all kinds of graphs represent data entities and their connections, knowledge graphs are unique for their support of heterogeneous data entities and relations. For example, a ‘person’ entity can be related to a ‘date’ entity through different relations like ‘born on’, ‘died on’, etc. Hence, knowledge graphs can be used to model real-world information in a way closest to what a human brain perceives. Moreover, knowledge graphs can infer connections between entities based on logical reasoning. For example, A is the mother of B, and B is the mother of C; hence A is the grandmother of C.

What are the Main Components of a Knowledge Graph?

A knowledge graph is designed around the following key components:

  • Taxonomy: Taxonomy is the classification and categorization of data entities to structure them into data models. From a machine’s perspective, taxonomy is the vocabulary associated with a particular dataset that makes the machine understand data the same way as humans. For example, the classification of people’s job roles in a company.
  • Ontology: Ontology is the formal model of knowledge that maps properties and relationships to data entities. It uses the data categorization from taxonomy to define and associate connections between the entities. For example, a person (entity) with a name (property) is an employee of (relation) an organization (entity).
  • Data Sources/Content: All the data stores that supply information to knowledge graphs act as data sources. These data sources are essential for knowledge graphs as they house information that serves as the content used for building a knowledge graph. A knowledge graph can link different types of data scattered across multiple sources. Examples of data sources include relational databases, images, text documents, or even other knowledge graphs.
  • Graph database: A graph database is a centralized repository that stores all the data and their meanings, represented by a knowledge graph. It stores data entities and references to their properties and relationships with other entities. A graph database serves as the foundation on which a knowledge graph is built.

What are the Steps Involved in Building a Knowledge Graph?

There are many tools available today for building knowledge graphs. While their strategies and technologies may differ, the basic steps for creating knowledge graphs are the same. Following is an overview of the main steps all these tools go through to build a knowledge graph:

  • Identify Use Case: The first step is to identify the domain of your interest. Your use case defines the data you need for your knowledge graph.
  • Find Relevant Data: Once you know your domain, you can narrow your search to specific data sources for gathering data.
  • Data Organization: Next comes the organization of unstructured data using business taxonomies that classify and categorize the data.
  • Knowledge Extraction: Once you have a model for structured data, you need to build a knowledge model using ontologies. An ontology model maps the relationships between the data entities. 
  • Graph Construction: Next, you combine the data and knowledge models inside a graph database. A graph database stores this knowledge with structure and context that you can use to illustrate a knowledge graph.

What is the Purpose of a Knowledge Graph?

From healthcare to finance to the web, the applications of knowledge graphs are widespread. At the heart of it, all these industries are looking for knowledge graphs to serve the following purposes:

  • Representation of Knowledge: Knowledge graphs are meant to be a single source of truth for representing heterogeneous information.
  • Real-world Data Integration: Knowledge graphs centralize information from a variety of sources in a single graph database. They are designed to accept and adapt to real-world information.
  • For machines to interpret knowledge: Knowledge graphs standardize knowledge for systems to interpret contextual information.
  • Faster access to data: As a single source of truth, knowledge graphs are helpful for quickly traversing relation paths between entities for faster retrieval of information.
  • Discovering hidden patterns: Every industry wants meaningful insights from its data. Knowledge graphs are purpose-built for organizations to gain insights from existing information and discover new knowledge.

Wisecube’s Knowledge Graph Platform

Organizations like Roche and Providence Healthcare today are adopting a data-driven culture to keep up with the rapidly evolving digital world. Knowledge graphs are becoming increasingly popular among these organizations for enabling their data-driven solutions. Knowledge graphs have proven revolutionary for data analysis with their speed and reasoning capabilities.

Look no further if you are also looking to discover hidden gems in your organizational data. Wisecube’s Knowledge Graph Platform is the solution for you to unify and synthesize hidden patterns in your data. Moreover, the platform learns from the discovered patterns to make intelligent predictions based on your data. 

Schedule a call with us to learn more about Knowledge Graphs.

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