Want Explainability and Reusability in your AI processes? Knowledge Graphs vs. a Vector DB approach

I've written a lot recently on the benefits of transparent AI approaches. As everyone leans in hard on AI and it becomes more pervasive in our lives, it is clear that concerns about transparent and explainable processes are a key factor in addressing some of the less apocalyptic fears about AI.

In this short article, I will explore how Knowledge Graphs, a powerful data structure, addresses these critical challenges, surpassing traditional Vector Databases in several key aspects.

Semantic knowledge graphs are better suited for transparent and explainable AI compared to vector databases for several reasons:

• Explicit representation of relationships: Knowledge graphs explicitly represent relationships between entities through edges. Each edge in the graph represents a specific semantic connection between two entities. This makes tracing the reasoning behind recommendations or decisions more accessible, as the relationships between data points are well-defined and interpretable.
• Human-readable structure: Knowledge graphs use a graph-based data model, which is inherently human-readable and intuitive. The nodes represent entities, and the edges represent relationships. This simplicity allows domain experts to understand and validate the graph's structure, leading to better transparency and confidence in the AI system's outputs.
• Contextual reasoning: Knowledge graphs enable context-aware reasoning by traversing and exploring the graph. When making recommendations or predictions, the AI system can consider the entire graph and the semantic relationships between entities, leading to more informed and interpretable decisions.
• Query-driven interpretability: Knowledge graphs support querying, which allows users to ask specific questions about the data and receive interpretable answers. For example, users can ask why a certain recommendation was made or how two entities are related, and the AI system can provide direct explanations based on the graph structure.
• Rule-based inference: Knowledge graphs can incorporate rule-based systems that encode domain-specific knowledge and logical constraints. These rules can be easily interpreted and understood, providing a clear basis for decision-making and recommendations.
• Explainable graph algorithms: Many graph-based algorithms, such as PageRank or community detection, are inherently interpretable and can explain specific patterns or outcomes in the data.

In contrast, while efficient and scalable for similarity search and high-dimensional data, vector databases lack the explicit representation of relationships found in knowledge graphs. They often rely on numerical embeddings that may not carry semantic meaning. As a result, the reasoning behind decisions made by AI systems using vector databases might be less transparent and more challenging to explain to end-users.

While vector databases can be combined with techniques like post-hoc feature importance to provide some explanation, they still need knowledge graphs' direct, human-readable interpretability. This limitation can be a significant drawback, especially in domains where transparency, accountability, and user trust are crucial, such as healthcare, finance, or legal applications.

Knowledge Graphs offer a compelling solution to the challenges of transparency, explainability, and reusability in AI. Their explicit representation of semantic relationships fosters understanding, accountability, and user trust, while the human-readable structure ensures data reusability and adaptability. Progress Semaphore and Progress MarkLogic help our customers create transparent, explainable and reuseable AI processes.