Organizing Knowledge With Knowledge Graphs: Industry Trends

Knowledge graphs are a giant web of information where elements and ideas are linked to show how they are related in the real world. This is beyond databases that just store information. Knowledge graphs also store the connections between information.

This makes knowledge graphs very useful in various fields. Here are a few examples:

• Search engines: Search engines use knowledge graphs to understand the relationships between search terms and real-world entities. A search for "French food" might not just surface recipes, but also information about French wine regions or famous French chefs, thanks to the connections embodied in the knowledge graph.
• Virtual assistants: Virtual assistants like Siri or Alexa rely on knowledge graphs to understand your requests and provide helpful responses. By knowing that "Eiffel Tower" is a landmark and "Paris" is a city, the assistant can answer your question about the Eiffel Tower's location.
• Machine learning applications: Machine learning algorithms can leverage knowledge graphs to improve their understanding of the world. A recommendation system, for example, can use a knowledge graph to connect movies with actors, directors, and genres. This allows to recommend similar movies based on past preferences.
• Large Language Models (LLMs): LLMs can benefit from knowledge graphs by accessing and processing all the information and connections that they have stored. This helps LLMs to generate more comprehensive and informative responses to our questions.
• Fraud detection: Knowledge graphs can be used to identify fraudulent activity by analyzing connections between entities. For example, a graph might flag a transaction as suspicious if it involves a new account linked to a known fraudulent IP address.

Knowledge Graph Basics

In a library, books may not just be shelved by category, but also cross-referenced. A book on Paris might be near French history books, but also connected to travel guides and works by Parisian authors. This web of connections is the essence of a knowledge graph. The basic building blocks of a knowledge graph contain:

• Nodes: These are the fundamental entities in the graph. They can be anything you can describe: physical objects (like the Eiffel Tower), abstract concepts (like democracy), events (like the French Revolution), or even people (like Marie Curie).
• Edges: These are the connections between nodes. They show how entities relate to each other. Edges are often labeled to specify the nature of the connection. Going back to our Paris example, the edge between "Paris" and "France" might have the label "capital of." Other labels could be "inhabitant of" (between Paris and Marie Curie) or "influenced by" (between French Revolution and democracy).
• Labels: These are crucial for understanding the edges. They provide context and meaning to the connections between nodes.
• Properties: Nodes and edges can have properties, which are additional attributes or metadata associated with them. For example, a person node might have properties such as "name," "age," "gender," etc., while an edge representing the relationship "is married to" might have properties like "start date" and "end date."
• Ontologies: These are blueprints for the knowledge graph. They define the types of entities allowed in the graph, the possible relationships between them, and the labels used for those relationships. In a library, again, there can be a specific classification system for books, defining sections, subsections, and how different categories of books can relate. An ontology sets the rules for how information is organised within the knowledge graph.
• Schema: Based on the ontology, a schema defines the types of entities, relationships, and properties allowed in the graph. It provides structure and consistency to the data, making it easier to query and analyze.

Superpowers of a Knowledge Graph

This web of relationships unlocks a unique power: machines can reason and infer new information based on what they "know" in the graph. Here are two examples below.

Reasoning and Inference: The "Aha Moment" for Machines

Assume a knowledge graph that stores information like "Paris is the capital of France" and "France is in Europe." While the graph might not explicitly state "Paris is in Europe," the connections between these entities allow a machine to reason towards to that conclusion.  This "aha moment" is the essence of reasoning with knowledge graphs. Machines can analyze these connections and infer new information that isn't explicitly stated, expanding their understanding of the world.

Example

A travel recommendation system uses a knowledge graph to connect cities with tourist attractions and nearby landmarks. If a user expresses interest in visiting the Eiffel Tower, the system can reason using the knowledge graph and recommend exploring Paris, even if the user didn't specifically mention the city.

Interoperability: Sharing Knowledge Like a Universal Library

Knowledge graphs aren't isolated islands of information. They can be built using standardized formats, allowing different systems to understand and exchange information stored within their graphs, like a universal filing system for libraries. Each library can curate its own collection (specific knowledge graph), but they can all leverage the information from other libraries because they follow the same organization principles (standardized formats).

Example

A product recommendation engine in an online store uses a knowledge graph. This graph might connect products with their features, brands, and similar items. The store could then share this knowledge graph with a partner company that provides product reviews. The review company, with its own knowledge graph for user sentiment analysis, could then analyze reviews in the context of the product information from the store's knowledge graph. This can lead to more insightful recommendations for customers.

A Sample of Important Use Cases

Knowledge graphs may provide a powerful framework for systematically generating test cases. This can be done by leveraging the structured representation of software components, their interactions, and domain-specific knowledge. By analyzing the graph, testers can identify critical paths, handle complexity, incorporate constraints, and automate the generation process, improving the quality and coverage of the testing effort. Let's explore some important use cases.

Modeling Software Components and Interactions

Knowledge graphs can represent components of a software system, such as modules, classes, functions, or APIs, as nodes in the graph. Edges between these nodes may represent the interactions or dependencies between the components. By analyzing these interactions, testers can identify potential test scenarios and paths through the system.

Incorporating Domain Knowledge

Knowledge graphs can integrate domain-specific knowledge, such as industry standards, best practices, or regulatory requirements, into the test case generation process. By incorporating domain-specific nodes and edges into the graph, testers can ensure that test cases align with domain-specific considerations and constraints.

Versioning and Change Management

Knowledge graphs can also support versioning and change management by tracking the history of requirements and test cases over time. Testers can view the evolution of requirements and their associated test cases, including when changes were made and by whom. This historical context is valuable for understanding the rationale behind changes and ensuring traceability across different iterations of the software.

Cross-Referencing Dependencies

Requirements often have dependencies on each other, and test cases may also have dependencies on multiple requirements. Knowledge graphs can capture these dependencies as edges between nodes, enabling testers to visualize and understand the interconnectedness of requirements and test cases. This can help in identifying potential conflicts or gaps in the testing coverage.

Identifying Patterns and Trends

Knowledge graphs may enable testers to identify patterns and trends in defect occurrences, such as recurring issues, common failure scenarios, or correlations between specific code changes and defects. By analyzing the graph, testers can gain insights into the root causes of defects and prioritize their investigation efforts accordingly.

OpenSource Knowledge Graphs

Some open-source knowledge graphs offer a glimpse into how these systems are structured and function. Examples include:

• Wikidata:  A collaborative, editable knowledge base operated by the Wikimedia Foundation
• DBpedia:  A knowledge graph extracted from Wikipedia
• YAGO:  A knowledge graph from Wikipedia for web search 
• KBpedia: KBpedia is an open-source knowledge graph that integrates seven leading public knowledge bases, including Wikipedia, Wikidata, schema.org, DBpedia, GeoNames, OpenCyc, and standard UNSPSC products and services. It provides a comprehensive structure for promoting data interoperability and knowledge-based artificial intelligence (KBAI). KBpedia’s upper ontology (KKO) includes more than 58,000 reference concepts, mapped linkages to about 40 million entities (mostly from Wikidata), and 5,000 relations and properties. It’s a flexible and computable knowledge graph suitable for various machine learning tasks.
• Logseq: A knowledge graph tool that combines note-taking, outlining, and wiki functionality; It allows users to create interconnected notes and organize information in a graph-like structure.
• Athens: A knowledge graph tool that integrates with other note-taking apps like Roam Research; It allows users to create linked notes and build a network of ideas.
• GraphGPT: While not a standalone knowledge graph, GraphGPT is a language model fine-tuned for generating graph-based responses. It can be used to create educational content related to knowledge graphs.
• GitJournal: A knowledge graph tool that integrates with Git repositories; It allows users to create and manage notes using Git version control.
• RecBole: A recommendation library that leverages knowledge graphs for personalized recommendations; It can be useful for educational scenarios related to recommendation systems.
• DeepKE: A toolkit for knowledge embedding that can be used to embed entities and relations from knowledge graphs into vector representations; It’s helpful for educational purposes related to graph-based machine learning.

These resources provide a valuable learning ground for understanding the fundamentals of knowledge graphs and their potential applications.

Knowledge Graphs in the Industry

There are multiple cases in the industry where companies benefit from knowledge graphs. The tech giant Google utilizes knowledge graphs extensively. Their knowledge graph powers search results by understanding the relationships between entities, providing more relevant information to users. 

Amazon leverages knowledge graphs to enhance its recommendation systems. By analyzing user behavior and product attributes, they create personalized recommendations for customers. 

Walmart uses knowledge graphs to optimize supply chain management. By modeling relationships between products, suppliers, and logistics, they improve inventory management and distribution. 

The ride-sharing company Lyft, employs knowledge graphs to enhance route optimization and improve driver-passenger matching. By understanding geographical relationships, they optimize travel times and reduce wait times. 

Airbnb’s knowledge graph helps match hosts and guests based on preferences, location, and availability. It enhances the user experience by suggesting relevant listings. 

Let's dive into the details of two specific cases: Allianz and eBay.

Allianz: Streamlining Regression Testing with Knowledge Graphs

German insurance giant Allianz implemented a knowledge graph system to streamline regression testing for their core insurance platform.  Here's how it worked:

Knowledge Graph Construction

Allianz built a knowledge graph that captured information about the insurance platform's functionalities, user roles, data entities (policies, claims, customers), and the relationships between them.

Test Case Automation

The knowledge graph was leveraged to automate the generation of basic regression test cases. The rich network of information within the graph allowed the system to identify different testing scenarios and create corresponding test cases. This significantly reduced the manual effort required for regression testing.

Improved Test Maintenance

The knowledge graph's ability to represent changes in the system proved valuable. When updates were made to the insurance platform, the knowledge graph was easily updated to reflect these changes. This ensured that the automatically generated regression tests remained relevant and continued to cover the latest functionalities.

The results for Allianz were positive.  They reported a significant reduction in regression testing time and a corresponding increase in test coverage.  The knowledge graph also simplified test maintenance, allowing testers to focus on more complex scenarios.

eBay: Enhancing Test Case Design With Knowledge Graphs

E-commerce giant eBay experimented with knowledge graphs to improve the design and management of test cases for their marketplace platform. Here's a breakdown of their approach:

Mapping User Journeys

eBay used a knowledge graph to model user journeys on the platform. This included entities like buyers, sellers, products, search functionalities, and checkout processes. Relationships between these entities were carefully mapped, providing a holistic view of user interactions.

Identifying Test Coverage Gaps

By visualizing user journeys within the knowledge graph, eBay could easily identify areas where existing test cases were lacking. For example, the graph might reveal that there were no tests for a specific type of user interaction or a particular edge case scenario.

Optimizing Test Suite Design

With these gaps identified, eBay could then design new test cases to ensure comprehensive coverage of user journeys. The knowledge graph facilitated a more systematic approach to test case design, ensuring functionalities were thoroughly tested.

While specific details about the outcomes are limited, eBay's experiment demonstrates the potential of knowledge graphs to improve the efficiency and effectiveness of test case design for complex software systems.

Technological Challenges

There are open issues in building and maintaining these powerful tools.  From gathering and cleaning vast amounts of data to ensuring the knowledge graph stays up-to-date, there are significant challenges to overcome.  Let's explore a sample of challenges in detail.

1. Data Acquisition and Cleaning

Knowledge Gathering

Building a comprehensive knowledge graph requires gathering information from diverse sources. This can be a time-consuming and resource-intensive task, especially for complex domains.

Data Quality

The accuracy and consistency of information feeding into the knowledge graph are crucial. Cleaning and filtering data to eliminate errors, inconsistencies, and duplicates can be a significant challenge.

2. Knowledge Graph Construction and Maintenance

Schema Design

Defining the structure of the knowledge graph, including the types of entities, relationships, and properties, requires careful planning. This schema should be flexible enough to accommodate new information while maintaining consistency.

Knowledge Graph Population

Populating the graph with accurate and up-to-date information can be an ongoing process. As the world changes, the knowledge graph needs to be updated to reflect these changes.

3. Integration and Interoperability

Data Integration

Knowledge graphs often need to integrate information from various sources, which can have different formats and structures. Reconciling these differences and ensuring seamless data flow can be challenging.

Interoperability

For knowledge graphs to truly unlock their potential, they need to be able to communicate and exchange information with other knowledge graphs. Standardized formats and protocols are needed to facilitate this interoperability.

4. Reasoning and Inference

Reasoning Capabilities

While knowledge graphs have the potential to reason and infer new information based on existing connections, developing robust reasoning algorithms is an ongoing area of research.

Explainability

When a knowledge graph makes an inference, it's crucial to understand the reasoning behind it. Ensuring transparency and explainability in the reasoning process is important for building trust in the system.

5. Scalability and Performance

Large Knowledge Graphs

As knowledge graphs grow in size and complexity, managing their storage, processing, and querying can become challenging. Scalable solutions are needed to handle massive amounts of information efficiently.

Query Performance

Ensuring fast and efficient retrieval of information from the knowledge graph is essential for real-world applications. Optimizing query processing techniques is an ongoing challenge.

Wrapping Up

Knowledge graphs represent a paradigm shift in software engineering and testing. By moving beyond traditional test case management approaches, knowledge graphs offer a more holistic and interconnected view of the software system. This structured representation of information unlocks possibilities for automation, optimization, and more robust and efficient software development lifecycles. As the technology matures and the challenges are addressed, knowledge graphs are a promising candidate to become a cornerstone of modern software engineering practices.