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With growing data demands, knowledge graphs (KGs) are becoming the first choice for data-intensive organizations. However, manual knowledge graph workflows require a lot of human effort. They are time-intensive and run a risk of human error. 

Automating knowledge graph generation can solve these challenges. However, its automation is difficult to implement because streamlining large-scale data volumes scattered across different data formats is challenging.

In this article, we’ll discuss various components involved in automating a knowledge graph. We’ll also discuss some major challenges and benefits of knowledge graph automation. 

What is Knowledge Graph Automation?

A typical knowledge graph is a representation of real-world entities and their relationships. It is used to store and organize large amounts of structured and unstructured data in a way that is easily accessible and useful for various business applications.

Automation in knowledge graphs refers to the use of various data integration technologies to automatically create, maintain, and enrich a knowledge graph over time.

Automation is an important component of modern knowledge management systems and data-intensive applications. Automated KGs supplement technologies like artificial intelligence, machine learning, and natural language processing to make it easier to build high-quality KGs.

Major Building Blocks of an Automated Knowledge Graph

In knowledge graphs, automation is typically applied to the following processes:

  • Knowledge acquisition & extraction: Knowledge acquisition refers to the process of extracting, organizing, and representing knowledge from various data sources such as business tools, research publications, open-source repositories, and the web. The extracted data is loaded into a knowledge graph in a standardized format.
  • Knowledge fusion: Automation transforms or fuses the extracted data into a format suitable for the knowledge graph, including identifying entities and relationships and mapping them to the appropriate domain-specific concepts and properties in the knowledge graph.
  • Knowledge loading: Automation can be used to load the transformed data into the knowledge graph, including creating new nodes and edges as needed and updating existing ones. Experts use programming frameworks to setup robust KG construction pipelines.
  • Data maintenance: Automation maintains and updates the knowledge graph over time, including identifying and correcting errors and inconsistencies and adding new data as it becomes available using automated data connectors.
Sample pipeline for building knowledge graph

Automating Domain-Specific Knowledge Graphs

A domain-specific knowledge graph is a structured representation of knowledge specific to a particular subject or domain, such as medicine, biology, finance, or technology.

A domain-specific knowledge graph can be created using automated methods. For instance, automated web scraping, data integration, and natural language processing techniques can extract and organize domain-relevant data from disparate data sources.

Domain-specific knowledge graphs are optimized for various purposes, such as 

  • Answering domain-relevant questions
  • Providing curated recommendations and insights
  • Supporting data-driven decision-making based on predictive analytics 

For instance, Wisecube’s biomedical knowledge graph can quickly uncover insights from data that accelerate medical research.

Domain-specific knowledge graphs contain highly-curated data. Hence, they can be used to improve the accuracy and precision of domain-based natural language processing and machine learning systems. 

They can also facilitate knowledge sharing and collaboration within a particular field.

One key advantage of domain-specific knowledge graphs is that, with careful optimization, they can organize and represent complex domain nuances in a structured and easily accessible way. This can be particularly useful in fields where information is constantly changing, or there is a high volume of specialized knowledge.

Benefits of Knowledge Graph Automation

Robust knowledge graphs can navigate deep data hierarchies and identify hidden relationships between items. Automating knowledge graph generation enhances these data workflows. As a result, an organization enjoys many benefits, including

  • Improves cost efficiency: Automated knowledge graph construction results in cost savings through reduced labor costs and improved interoperability.
  • Increases operational efficiency: Automated knowledge graph construction can reduce the time and effort required to build and maintain all stages of KG generation.
  • Supports real-time decision-making: Automated knowledge graph construction supports decision-making for stakeholders by providing a structured representation of information that can be easily accessed, visualized, and queried in real time.
  • Ensures scalability: Automated knowledge graph construction approaches can effectively scale to manage large datasets and handle updates as per business requirements.

Limitation in Automating Knowledge Graph

Automated knowledge graph creation faces many challenges related to the volume, velocity, and variety of data. Some of these major challenges are

  • Ongoing maintenance and update issues: Automated knowledge graphs need to be continuously updated and maintained to ensure that they remain accurate and relevant. It typically requires data connectors that are accurately synced to bring fresh data into the system. For large and complex knowledge graphs, this can be a highly resource-intensive process.
  • Automatic data transformation errors: Automated methods for extracting and organizing information may not always produce accurate or reliable results, especially if the data sources are of low quality or contain many errors. Moreover, data needs to be properly formatted and labeled. Otherwise, this leads to inaccuracies and inconsistencies within the knowledge graph.
  • Failure to capture domain expertise: For building a domain-specific knowledge graph, data needs to be rich in domain-relevant technical nuances that can be automatically presented as entities and relationships. For instance, in a biomedical knowledge graph, failure to capture complex medical entities and relationships may result in an ineffective drug discovery process.

How to Overcome the Limitation of Automating Knowledge Graphs?

There are several factors that contribute to the successful adoption of automated knowledge graph construction in the industry, including

  • Collaboration and partnerships: Collaboration and partnerships with industry experts and organizations can help to ensure that automated knowledge graph construction approaches are aligned with industry standards and practices. These collaborations can result in acquiring more high-quality data sources that can further improve knowledge graph robustness.
  • Regularly update and maintain the knowledge graph: To ensure the relevance and accuracy of the knowledge graph, it is important to regularly review and update the information contained within it. This can be done through a combination of automated and manual processes, which involves adding new information, correcting errors, or removing outdated or irrelevant information using human annotators and reviewers.

Building Biomedical Knowledge Graph Using Wisecube

Wisecube Knowledge Graph Platform

The healthcare industry is expanding at an unprecedented rate. It is challenging for researchers to keep up with the latest trends and discoveries in the healthcare domain. Wisecube’s state-of-the-art knowledge graph engine offers advanced search capabilities to healthcare researchers. They can quickly find out relevant insights from a huge repository of medical data in order to tackle modern healthcare challenges. 

The wisecube knowledge graph platform unifies and synthesizes public and private healthcare data from various data sources. These databases include biochemical, molecular, and adverse effects databases and biological literature.

Check out our latest open-source library Graphster makes it easy to build, analyze, and query knowledge graphs. If you are a biomedical or life sciences researcher (or organization) who wants to learn more about how Wisecube accelerates biomedical research, schedule a demo today.