Overview: Gemini Limitations and Knowledge Graphs

Updates on Google Gemini Usage Limits

Google recently unveiled clear details on Gemini usage limits across its various tiers. This clarification comes as an update to a help center article, which details "Gemini App Limits and Updates for Google AI Subscribers."

Previously, Gemini's usage limits were vague, often described as "limited access" or with ambiguous phrasing suggesting a potential restriction on the number of prompts, conversations, or certain features within a specified timeframe.

Now, the article clearly states that users receive up to five prompts daily with the Gemini 2.5 Pro model in free accounts. In contrast, AI Pro plan subscribers get 100 prompts daily, while this number increases to 500 prompts daily for AI Ultra subscribers.

For free accounts, features are limited to five in-depth research reports and 100 AI-generated images daily. If you need to generate more than 100 images per day using AI, upgrading to a Pro or Ultra account will provide you with the ability to create up to 1000 images.

Introduction to Knowledge Graphs

A Knowledge Graph, also known as a semantic network, is an organized knowledge base that uses a graph-structured data model to represent real-world entities such as people, places, events, or abstract concepts, and to illustrate the complex relationships between them. This information is typically stored in a graph database and displayed as a graphical structure, giving it the name "Knowledge Graph" (IBM, July 22, 2025). The aim of a Knowledge Graph is to add context to data through linking and semantic metadata, providing an effective framework for integrating, unifying, analyzing, and sharing data (Wikipedia, September 5, 2025).

Key Components of a Knowledge Graph

A Knowledge Graph consists of several essential elements that work together to build an integrated network of information:

• Nodes: Represent individual entities in the real world, such as people, places, organizations, events, or abstract concepts (IBM, July 22, 2025).
• Edges: Define the relationships and links between different nodes. These edges illustrate how entities are connected to each other, such as "worked for" or "part of" (IBM, July 22, 2025).
• Properties: Used to add descriptive information or attributes to each node or edge, providing additional details about entities and relationships (Wikipedia, September 5, 2025).
• Schema or Ontology: This component provides an organizational framework that defines the possible types of entities, allowed relationships, and the structuring of knowledge within the graph, ensuring consistency and semantic understanding (IBM, July 22, 2025).

Benefits of Using Knowledge Graphs

Knowledge Graphs offer a wide range of benefits that extend across various sectors and industries:

• Enhanced Search and Semantic Understanding: They enable search engines and question-answering systems to provide more accurate and comprehensive answers by understanding complex relationships between entities, going beyond mere keyword searches (IBM, July 22, 2025).
• Improved Recommendation Systems: Used to create intelligent recommendation engines in areas like e-commerce and entertainment (e.g., Netflix), offering personalized suggestions for products or content based on user behavior and popular purchasing trends (IBM, July 22, 2025).
• Support for Business Decision-Making: They help organizations integrate data from diverse and varied sources, uncovering new insights to support strategic business decisions and reducing the need for manual data collection (IBM, July 22, 2025).
• Combating Fraud and Financial Crime: Used in the financial sector to analyze money flows, identify suspicious patterns, and detect non-compliant customers, thereby enhancing efforts to prevent financial crimes (IBM, July 22, 2025).
• Healthcare and Scientific Research Applications: They contribute to organizing complex relationships within medical research, helping to verify diagnoses and determine appropriate treatment plans based on individual needs, and expanding usage in fields such as genomics and proteomics (IBM, July 22, 2025) (Wikipedia, September 5, 2025).
• Ability to Create New Knowledge: By linking data points that may not have been previously connected, Knowledge Graphs can uncover new knowledge and insights (IBM, July 22, 2025).

Challenges in Building Knowledge Graphs

Despite their many benefits, the process of building and developing Knowledge Graphs faces significant challenges:

• Data Integration and Standardization: Knowledge Graphs often consist of datasets from multiple sources with differing structures, making the process of integrating and standardizing this data a complex task requiring considerable effort (IBM, July 22, 2025).
• Entity Alignment: Given the lack of a single standard for building or representing Knowledge Graphs, identifying identical entities across different graphs presents a non-trivial challenge, and is currently an active area of research (Wikipedia, September 5, 2025).
• Maintenance and Updating of Graphs: Knowledge Graphs require continuous maintenance and updating to preserve their accuracy and relevance as data and knowledge evolve in the real world.
• Complexity in Design and Construction: Building robust and effective Knowledge Graphs demands deep domain expertise, in addition to advanced techniques in data modeling and artificial intelligence.
• Cost and Resources: The process of building and maintaining Knowledge Graphs, especially large ones, can be expensive and consume a significant amount of human and technical resources.

Future Trends in Knowledge Graphs

The field of Knowledge Graphs is experiencing rapid developments, driven by innovations in artificial intelligence and machine learning. Among the most prominent expected future trends are:

• Integration with Large Language Models (LLMs): LLMs are expected to be increasingly used to enhance the construction of Knowledge Graphs, ranging from information extraction and entity identification to entity alignment across multiple graphs (Wikipedia, September 5, 2025).
• Applications of Graph Neural Networks (GNNs): GNNs will play a crucial role in learning deep representations of entities and relationships within Knowledge Graphs, enhancing their ability to reason and predict and expanding the scope of their applications (Wikipedia, September 5, 2025).
• Expanded Use in New Domains: Knowledge Graphs are expected to spread further into areas outside their traditional scope (such as search engines), to include advanced scientific research (especially in biology and medicine) and specialized business applications (Wikipedia, September 5, 2025).
• Virtual Knowledge Graphs: There will be increased reliance on virtual Knowledge Graphs that do not store information in specialized databases but rather dynamically derive it from existing relational databases or data warehouses, offering greater flexibility and efficiency (Wikipedia, September 5, 2025).
• Focus on Interoperability and Standards: Efforts will continue to standardize methods for building and representing Knowledge Graphs, facilitating information exchange and alignment between different systems and supporting a more interconnected knowledge ecosystem.