Why Knowledge Graph for Generative AI

Knowledge Graph is not new in the Technology World. In the late 1980s, the University of Groningen and University of Twenty jointly began a project called Knowledge Graphs, focusing on the design of semantic networks with edges restricted to a limited set of relations, to facilitate algebras on the graph. In subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.

In 2012, Google introduced their Knowledge Graph, building on DBpedia and Freebase among other sources. They later incorporated RDFa, Microdata, JSON-LD content extracted from indexed web pages, including the CIA World Factbook, Wikidata, and Wikipedia. Entity and relationship types associated with this knowledge graph have been further organized using terms from the schema.org vocabulary. The Google Knowledge Graph became a successful complement to string-based search within Google, and its popularity online brought the term into more common use.

Since then, several large multinationals have advertised their knowledge graphs use, further popularising the term. These include Facebook, LinkedIn, Airbnb, Microsoft, Amazon, Uber and eBay.

In 2019, combined its annual international conferences on “Big Knowledge” and “Data Mining and Intelligent Computing” into the International Conference on Knowledge Graph.

In the evolving landscape of artificial intelligence, the fusion of Knowledge Graphs with Generative AI emerges as a groundbreaking synergy, propelling the capabilities of AI to a new heights.

• Why Knowledge Graphs?
  Knowledge Graphs serve as the natural architecture for encapsulating complex knowledge. They outshine traditional databases by offering more efficient traversal methods, then traditional queries.
• Transforming Data into Knowledge
  One of the most compelling advantages of Knowledge Graphs is their ability to convert unstructured data into a structured, meaningful format. When combined with structured data, this transformation unlocks the full potential of data, enhancing the depth and breadth of insights available to Large Language Models (LLMs).
• Enhancing Generative AI with RAG
  By integrating Knowledge Graphs through Retrieval Augmented Generation (RAG), we significantly boost the accuracy and consistency of Generative AI outcomes. This integration ensures that AI models are not just generating content but are doing so with a foundation of reliable and rich knowledge.
• Empowering Conversational BI and Self-Serve Analytics
  Moreover, Knowledge Graphs enable the development of conversational Business Intelligence and self-serve analytics platforms. These platforms allow users to query data using natural language, making data access more intuitive and user-friendly than ever before.
• Structured Context for Generative AI: Generative AI models (such as GPT, DALL-E, or BERT-based systems) excel at generating text or images based on patterns in data, but they lack intrinsic knowledge of the world. Knowledge graphs provide structured and semantically rich representations of real-world entities, relationships, and facts. This structured data allows the AI to:
• Generate content grounded in real-world facts.
• Disambiguate context by understanding entity relationships (e.g., distinguishing between “Apple” the company and “apple” the fruit).
• Enhance output relevance by embedding contextual relationships and hierarchical knowledge into the generation process.
• Improved Accuracy and Consistency: Generative AI can sometimes produce factually incorrect or inconsistent outputs, especially when handling complex domains like healthcare, finance, or legal content. Integrating a knowledge graph allows the AI to refer to a structured knowledge base that ensures:
• Fact-checking and validation during generation, leading to more accurate outputs.
• Consistency in terms of how entities, terms, and relationships are represented across multiple generations.

For example, a healthcare AI generating treatment recommendations would use the graph to ensure all treatments are based on validated relationships between symptoms, diseases, and treatments, reducing errors.

• Enhanced Reasoning and Inference: Knowledge graphs allow Generative AI to reason and make inferences that go beyond simple data pattern recognition. By embedding logic and ontologies into the graph, AI systems can:
• Derive new facts based on existing relationships (e.g., if a person is a doctor and works in a hospital, the AI can infer that this person likely treats patients).
• Generate outputs that are not only textually coherent but also logically valid in the context of the domain.

This enhances the ability of AI to simulate human-like understanding and problem-solving capabilities, as it can reason based on both explicit and implicit knowledge.

• Personalization and Recommendation

Knowledge graphs support personalization in generative AI by linking entities, attributes, and user preferences. In use cases like recommendation engines or personalized content creation, AI models can leverage knowledge graphs to:

• Understand and model user preferences, history, and relationships between products or content.
• Generate personalized suggestions (e.g., content or product recommendations) by tracing paths through the graph that match the user’s interests or needs.
• Provide contextual relevance by leveraging the graph’s ability to correlate entities related to user behavior.
• Natural Language Understanding and Generation

Natural language models benefit from the deep connections present in knowledge graphs. For tasks such as question answering, text summarization, and complex dialogue systems, Generative AI can use a knowledge graph to:

• Disambiguate meanings and choose the correct context for generating responses.
• Generate cohesive answers based on the relationships stored in the knowledge graph.
• Provide accurate explanations or generate in-depth reports by structuring the generated content around the relationships in the graph.
• Domain-Specific Knowledge Integration

Generative AI can struggle with specialized domains that require expert knowledge (e.g., law, healthcare, or engineering). Knowledge graphs offer a mechanism to inject domain-specific ontologies and data into the model, allowing the AI to:

• Adapt to industry-specific use cases with a deeper understanding of the domain.
• Generate content that adheres to specific rules and terminologies in those fields.
• Ensure compliance with domain regulations or standards when generating specialized content.

For example, in legal AI systems, a knowledge graph can help ensure that contract generation or case law analysis follows appropriate legal frameworks by structuring legal precedents and terms within the graph.

• Continuous Learning and Updates: Generative AI models can become outdated over time, particularly if they are not frequently retrained. However, knowledge graphs can be continuously updated with new facts and information from various sources, such as structured databases, APIs, or even unstructured data like articles or web pages. This enables AI models to:
• Incorporate real-time knowledge into their generative processes without needing full retraining.
• Stay relevant and up-to-date with evolving information, such as scientific research, market trends, or new regulations.

The synergy between Knowledge Graphs and Generative AI is not just an enhancement; it’s a way to go in how we approach AI development and deployment. By leveraging this powerful combination, we can unlock new possibilities, from improving decision-making processes to creating more natural and efficient user interfaces.