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Comprehensible Artificial Intelligence on Knowledge Graphs: A Survey of Interpretable Machine Learning and Explainable AI Methods


Core Concepts
This survey provides a clear distinction between Interpretable Machine Learning and Explainable Artificial Intelligence, and taxonomizes the research field of Comprehensible Artificial Intelligence on Knowledge Graphs.
Abstract
This survey provides a comprehensive overview of the research on Comprehensible Artificial Intelligence (CAI) on Knowledge Graphs (KGs). It starts by introducing the concepts of Interpretable Machine Learning (IML) and Explainable Artificial Intelligence (XAI), and defines CAI as the overarching term that encompasses both. The survey then presents a taxonomy for CAI on KGs, covering the representation of KGs (symbolic, sub-symbolic, neuro-symbolic), the tasks (link prediction, node/graph clustering, recommendation), the foundational methods (translational learning, neural networks, rule-based learning), and the type of comprehensibility (IML, XAI). Under the IML on KGs section, the survey discusses three main lines of research: rule mining methods, pathfinding methods, and embedding methods. These methods aim to create inherently interpretable AI models for tasks on KGs. The XAI on KGs section covers four lines of research: rule-based learning methods, decomposition methods, surrogate methods, and graph generation methods. These methods focus on explaining the outputs of black-box AI models using the structure and semantics of KGs. The survey provides a detailed overview of the key methods and approaches in each line of research, highlighting their strengths, limitations, and research challenges. It concludes by identifying future research directions in the field of CAI on KGs.
Stats
"Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives." "Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way." "Comprehensible Artificial Intelligence has two major sides: Explainable Artificial Intelligence and Interpretable Machine Learning."
Quotes
"CAI is a set of methods that enable stakeholders to understand and retrace the output of AI models." "Knowledge Graphs are human- and machine-readable representations of semantically linked data, referred to as knowledge over a particular domain." "Semantically linked data is predestined to create AI models with human-understandable decision-making processes, which shall be the notion of CAI in this survey."

Key Insights Distilled From

by Simon Schram... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03499.pdf
Comprehensible Artificial Intelligence on Knowledge Graphs

Deeper Inquiries

How can the concepts of IML and XAI be further integrated to create more comprehensive and synergistic approaches for CAI on Knowledge Graphs?

In order to create more comprehensive and synergistic approaches for Comprehensible Artificial Intelligence (CAI) on Knowledge Graphs, the concepts of Interpretable Machine Learning (IML) and Explainable Artificial Intelligence (XAI) can be further integrated in the following ways: Hybrid Models: Develop hybrid models that combine the strengths of both IML and XAI. For example, integrating rule mining methods from IML with decomposition-based methods from XAI to provide both interpretable rules and feature importance explanations for predictions on Knowledge Graphs. Interpretability Throughout the Process: Ensure interpretability is maintained throughout the entire AI model's lifecycle, from data preprocessing to model training and inference. This can involve using interpretable features, transparent algorithms, and explainable predictions at each stage. Feedback Loop: Establish a feedback loop between IML and XAI components to continuously improve the interpretability and explainability of the AI models on Knowledge Graphs. This can involve using explanations generated by XAI methods to refine the rules learned by IML models and vice versa. Human-in-the-Loop: Incorporate human feedback and domain knowledge into the AI models by allowing users to interact with and provide feedback on the explanations generated. This can help improve the relevance and usefulness of the explanations for end-users. Visualization Techniques: Utilize advanced visualization techniques to present the explanations in a clear and intuitive manner. Visual representations can enhance the understanding of complex AI decisions and make the explanations more actionable for users.

What are the potential limitations and biases of the current rule mining, pathfinding, and embedding methods for IML on Knowledge Graphs, and how can they be addressed?

Limitations of Rule Mining: Rule mining methods may suffer from scalability issues when applied to large Knowledge Graphs, leading to longer processing times and increased computational resources. To address this, optimizing the rule mining algorithms for efficiency and parallel processing can help overcome scalability limitations. Biases in Pathfinding Methods: Pathfinding methods may introduce biases based on the selection of paths or the representation of the graph structure. To mitigate biases, incorporating diversity in path selection, considering alternative paths, and evaluating the impact of different path choices can help reduce bias in the explanations generated. Challenges in Embedding Methods: Embedding methods may face challenges in capturing the full semantic meaning of entities and relations in the Knowledge Graph, leading to information loss and inaccuracies in predictions. To address this, enhancing the embedding models with contextual information, incorporating multi-modal data sources, and fine-tuning the embedding parameters can improve the accuracy and interpretability of the embeddings. Addressing Biases: To address biases in rule mining, pathfinding, and embedding methods, it is essential to conduct thorough bias assessments, implement fairness measures, and diversify the training data to reduce bias in the AI models. Additionally, incorporating ethical guidelines and standards in the development process can help mitigate biases and ensure the fairness of the AI applications on Knowledge Graphs.

How can the explanations generated by XAI methods on Knowledge Graphs be made more actionable and useful for end-users in real-world applications?

Contextualization: Provide contextual information along with the explanations to help end-users understand the relevance and significance of the AI decisions in the context of their specific tasks or domains. Personalization: Tailor the explanations to the individual preferences and expertise levels of the end-users to ensure the information is presented in a way that is easily understandable and actionable for each user. Interactive Interfaces: Develop interactive interfaces that allow end-users to explore the explanations, ask questions, and provide feedback. This interactive approach can enhance user engagement and facilitate better decision-making based on the explanations. Actionable Insights: Translate the explanations into actionable insights or recommendations that end-users can directly implement in their workflows or decision-making processes. This can help bridge the gap between understanding the AI decisions and taking practical actions based on the insights provided. Continuous Improvement: Continuously gather feedback from end-users on the usefulness and effectiveness of the explanations and incorporate this feedback into the refinement and enhancement of the XAI methods. This iterative process can lead to more actionable and valuable explanations for real-world applications on Knowledge Graphs.
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