Here are 5 Surprising Ways in Which Knowledge Graphs Unlock Efficiency

The world is currently living in an age of information, with everyone from individuals to organizations inundated with it. In this scenario, knowledge graphs have emerged as a powerful tool for organizing vast amounts of data. They are akin to a web of knowledge, connecting everything through relationships. A machine uses this web to understand the world in a way that mimics human cognition. This is why knowledge graphs are at the forefront of artificial intelligence. In fact, they were ranked among the emerging technologies with the greatest potential to disrupt the overall market, according to Gartner. So, let’s dive deep into the fundamentals of knowledge graphs, explore their applications, and understand how they tackle challenges in surprising ways.

What are Knowledge Graphs and How do They Work in AI?

Knowledge graphs link people, places, things, and even ideas via relationships. Here’s how they work:

1. Building Blocks

A. Nodes

These represent entities, like people (APJ Abdul Kalam), places (Mumbai), or things (phones).

B. Edges

They show relationships between entities (Kalam was a famous Indian scientist). Edges can also be labeled to specify the type of connection (job).

C. Labels

They provide additional information about nodes and edges, like dates, properties, or categories.

2. Structured Approach

Most traditional databases store data in tables, whereas knowledge graphs use a graph structure. It allows for a flexible and intuitive representation of real-world connections consequently.

3. Comprehensive Knowledge Base

Knowledge graphs integrate information from various sources, such as text documents, web pages, and existing databases.

4. Operations

They facilitate the following in artificial intelligence and machine learning models:

A. Reason and Infer

Analyze relationships between entities to draw conclusions and answer complex queries.

B. Understand Context

Provide context by considering the relationships between entities and avoid misinterpretations subsequently.

C. Support Learning

Allow systems to improve continuously with the help of new information.

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How are Knowledge Graphs Different From Traditional Data Structures?

1. Data Representation

Traditional structures use tables, trees, or arrays to store data in a structured format. Each record is independent, and relationships are represented through foreign keys or indices. On the other hand, knowledge graphs rely on nodes, edges, and labels to create a grid of interconnected data. Node represents entities, whereas labels describe attributes of both nodes and edges.

2. Relationships

Relationships are often implicit in traditional structures, and need to be inferred using joins or foreign keys in relational databases. At the opposite end of the spectrum are the explicit connections between entities in knowledge graphs. Furthermore, these connections are directly stored and easily navigable, making it easy to traverse interconnected data.

3. Flexibility and Schema

One needs a predefined schema that dictates the data structure in traditional structures. Any changes to the schema can be complex and disruptive. Knowledge graphs have the advantage of being not just schema-less but also flexible. This, in turn, allows for the addition of new types of entities and relationships without much reconfiguration.

4. Integration and Interoperability

When it comes to traditional structures, it is challenging to integrate data from multiple sources due to differing schemas and the need for data transformation. In knowledge graphs, it is easy to integrate diverse data because of flexible schema and the ability to directly link related data.

5. Querying and Semantics

While traditional structures typically use structured query languages like SQL to retrieve and manipulate data, knowledge graphs rely on graph query languages like SPARQL or Cypher. These are particularly designed to traverse relationships efficiently.

6. Contextualization and Insight

Traditional structures provide data in a more siloed manner where context is often fragmented and needs to be pieced together. On the other hand, knowledge graphs offer rich contextualization by capturing the semantics of data. The connections between entities add layers of meaning, enabling deeper insights.

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What are Some Real-World Examples of Knowledge Graphs Being Used to Solve Complex Problems?

Knowledge graphs are used extensively across various industries, from data analysis to problem-solving. Let’s take a look at a few examples:

1. Fighting Fraud 

Banks leverage knowledge graphs to identify suspicious financial activity. They detect unusual patterns by mapping relationships between accounts, transactions, and entities (people, businesses).

2. Recommending Products

Most e-commerce platforms use knowledge graphs to understand customer preferences and product attributes. They can then suggest items based on factors such as customer preferences, connections between products, etc.

3. Helping Scientific Research

Researchers rely on knowledge graphs to navigate scientific data. They are particularly useful in enabling researchers to identify potential treatment pathways by connecting genes, diseases, drugs, and clinical trials.

4. Combating Misinformation

Search engines employ knowledge graphs to understand the context and relationships between search queries and information sources. As a result, they filter out misleading content and provide users with accurate and trustworthy search results.

5. Personalizing Medicine 

Knowledge graphs connect patient data, medical history, and treatment options. Therefore, they allow doctors to customize treatment plans by considering a patient’s chart and potential drug combinations.

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What are the Limitations of Knowledge Graphs in AI and How Can They Be Overcome?

1. Scalability

Knowledge graphs can be large and complex, resulting in performance bottlenecks in storage, querying, and updating. Implement distributed and parallel processing techniques. They, therefore, also utilize graph databases designed for high scalability, such as Neo4j or Amazon Neptune.

2. Data Quality

Inconsistent, incomplete, or inaccurate data can affect data analysis. Data integration from multiple sources can also introduce discrepancies. To overcome this, establish robust data governance practices and audit the data to maintain consistency and accuracy.

3. Complexity

It can be difficult to write and optimize queries for large and complex knowledge graphs, especially for those unfamiliar with graph query languages. The way around this is to undertake comprehensive training for graph query languages such as SPARQL or Cypher. Lastly, develop and use query optimization tools.

4. Integration

Integrating knowledge graphs with legacy databases can be tricky owing to differences in data models and architectures. To bridge the gap, we must not only use middleware and APIs to facilitate integration but also employ data transformation and mapping techniques.

5. Privacy

Knowledge graphs containing sensitive information can pose privacy and security risks if not managed properly. To protect sensitive data, implement strong access controls and encryption methods. Anonymization techniques can also be used where possible.

6. Evolution

Knowledge graphs can have biases if not updated to reflect changes in the real world. Keeping the graph relevant requires continuous effort. Additionally, automate update processes using machine learning and natural language processing to integrate new data.

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How Can Professionals Leverage Knowledge Graphs to Drive Innovation in Their Respective Fields?

1. Identify Bottlenecks

Reflect on challenges hindering progress in your field. Are there difficulties connecting disparate data points? Knowledge graphs can reveal hidden relationships and patterns, helping with problem-solving accordingly.

2. Leverage Data

Many decisions rely on scattered information. To avoid this, collate all your information in a single place with knowledge graphs that integrate data from various sources and help make data-driven choices consequently.

3. Personalize Your Approach

Tailor your approach by using knowledge graphs to handle individual entities and their unique connections. This is particularly useful in fields such as medicine, education, or marketing, where you need to consider specific details and relationships.

4. Enhance Collaboration

Foster collaboration within your team since graphs function as a shared knowledge base. A central source of interconnected information allows professionals to work together effectively and build upon each other’s findings.

5. Automate Workflows

Integrating knowledge graphs with various AI tools will automate routine tasks that involve information retrieval. You can then focus on tasks that impact innovation.

Elevate Your Career With Emeritus

Knowledge graphs are quite useful and have the potential to revolutionize multiple industries. In fact, many companies will look to drive up their investment in technology and look for professionals to manage this process. Emeritus offers comprehensive online artificial intelligence courses and machine learning courses to help impart the skills needed to excel in the changing tech industry. Moreover, they boast a curriculum designed by industry experts to provide practical insights. Join up and take the first step toward building a successful future.

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