Knowledge graphs are not just theoretical constructs; they're at work in various real-world applications. Let’s examine some prominent use cases where these graphs are making a tangible difference.
Use Cases of Knowledge Graphs
Contextual Results: When you search for "Apple" on Google, how does it know whether you mean the tech company or the fruit? Knowledge graphs analyze search context and user behavior to provide relevant results.
Information Panels: Noticed the concise boxes on the right side of Google Search with quick information about celebrities, places, or events? That's powered by their knowledge graph, pulling related data into one concise snapshot.
Understanding Queries: When you ask Siri or Alexa about the weather or news headlines, knowledge graphs assist in deciphering user intent and fetching accurate responses.
Personalized Suggestions: Based on your interactions and habits, these assistants can suggest songs, reminders, or even recipes, all thanks to the interconnected data in their knowledge graphs.
Tailored Choices: Platforms like Netflix or Spotify use knowledge graphs to understand user preferences, linking viewed or listened content to potential new choices. This is why you get movie suggestions in genres you favor or music playlists that align with your taste.
Discoverability: By recognizing content relationships, these platforms can introduce users to lesser-known movies, shows, or tracks that align with their established preferences.
Knowledge Bases and Customer Support
Faster Solutions: When you query a tech issue in a support forum or help center, knowledge graphs connect your problem to potential solutions, filtering out irrelevant content.
Predictive Support: By analyzing common queries and issues, companies can proactively offer solutions or tips to users even before they face a problem.
Social Media Platforms
Friend Recommendations: Ever wondered how Facebook suggests people you might know? By analyzing mutual friends, groups, or pages liked, the platform's knowledge graph identifies potential connections.
Content Suggestions: Platforms like LinkedIn or Twitter curate content on your feed based on your interactions, following list, and interests, ensuring you see posts most relevant to you.
By synthesizing vast amounts of data and discerning the intricate connections between them, knowledge graphs empower various platforms and tools to function with heightened efficiency and accuracy, enhancing user experience manifold.
Knowledge graphs offer a versatile approach to data management and insights. Various industries, with their unique challenges and datasets, have found value in integrating knowledge graphs into their operations. Let's delve deeper.
Data Integration: Hospitals deal with multiple data sources, from patient records to lab results. Knowledge graphs integrate this data, offering a comprehensive patient view.
Predictive Analysis: By identifying patterns in patient data, knowledge graphs can help forecast potential health issues or treatment outcomes.
Research Acceleration: Medical researchers can identify correlations faster, potentially speeding up drug discoveries or treatment methodologies.
Fraud Detection: Knowledge graphs can swiftly recognize unusual transaction patterns, helping in early detection of potential frauds.
Risk Assessment: By analyzing transaction histories, market trends, and more, banks can make better-informed lending decisions.
Customer Insights: Banks can gain a clearer understanding of customer behaviors and preferences, tailoring services accordingly.
Product Recommendations: Knowledge graphs analyze user purchase histories, browsing patterns, and preferences, enabling precise product suggestions.
Inventory Management: By understanding product relationships and sales patterns, businesses can optimize stock levels.
Supplier Relations: Knowledge graphs can highlight supply chain efficiencies, helping businesses choose or negotiate with suppliers effectively.
Research & Development
Data Collation: Knowledge graphs enable researchers to pull data from various sources, breaking down silos.
Pattern Discovery: With interconnected data, researchers can easily spot trends, leading to faster, more accurate conclusions.
Collaboration Boost: By visualizing data connections, teams can align their research efforts, fostering better collaboration.
Property Insights: Knowledge graphs can link properties to various parameters like neighborhood amenities, historical prices, and ownership data, aiding both buyers and sellers.
Trend Prediction: By analyzing market movements and property relations, real estate firms can forecast price trends.
Tenant-Landlord Relations: Firms can better understand tenant preferences, previous rental histories, and align services accordingly.
In essence, while the applications might differ, the core value of knowledge graphs across industries lies in their ability to interlink data, provide contextual insights, and streamline operations.
Mayank is Lettria’s Product Content Manager. He’s also a YouTube content creator with 20K+ subscribers, and a Substack newsletter writer.