Revathy V: Explainable Process Recommendation by Contextual Grounded Dynamic Multimodal Process KG

Abstract
Can I eat this food or not, and why? Which AI pipeline is best for a given task and dataset? These questions differ from factual questions and answering tasks as they involve processes with interacting entities. Recipes consist of ingredients, methods, and interactions, while AI pipelines include datasets, models, and tasks. Each entity must be analyzed independently, and a collective inference, known as compositional reasoning, is required to draw the conclusion.

Existing process recommendation methods rely on the availability of structured data but struggle with unstructured data like recipes and AI pipelines. These datasets are often lengthy and noisy, making it hard to capture interactions and derive relevant insights. Additionally, natural language descriptions don’t provide the necessary domain knowledge. For example, recipes don’t state that potatoes are healthy carbs with a high glycemic index. Domain-specific knowledge is needed for effective analysis and recommendations.

While neural networks excel in pattern recognition, they struggle with compositional reasoning. This work introduces a neurosymbolic framework for explainable process recommendations using Dynamic Multimodal Process Knowledge Graphs (DMPKGs). DMPKGs provide structured process representations grounded in multi-contextual knowledge for reasoning, explainability, and traceability while utilizing neural networks for pattern recognition. They enable modular entity inference and capture interactions for dynamic decision-making. DMPKGs allow continuous updates and store multimodal data, improving recommendation accuracy and explainability. Two use cases, recipe suitability analysis, and AI pipeline recommendation, are explored to demonstrate the effectiveness of this approach in process recommendation.

Links:
Dynamic Multimodal Process Knowledge Graphs(DMPKG): https://ieeexplore.ieee.org/document/...