The Intersection of Machine Learning and Semantic Web Technologies: Enhancing Model Explainability

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Sep 28, 2023

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The Intersection of Machine Learning and Semantic Web Technologies: Enhancing Model Explainability

Introduction:

Machine Learning (ML) techniques, particularly Artificial Neural Networks, have gained significant attention for their predictive capabilities. However, the lack of explainability in these models poses challenges in high-stakes domains like healthcare and transport. To address this, researchers have explored the use of Semantic Web Technologies, which offer semantically interpretable tools for reasoning on knowledge bases. This article aims to explore the combinations of Semantic Web Technologies and ML to enhance model explainability, identify important application domains, evaluate model explanations, and propose actionable advice for future research.

Connecting ML and Semantic Web Technologies:

To achieve explainability, researchers have integrated Semantic Web Technologies with ML models, primarily in supervised classification tasks using Neural Networks and unsupervised embedding tasks. Neural Networks are the dominant prediction model in supervised learning approaches, often utilizing taxonomical information from knowledge bases. Notably, the combination of Semantic Web Technologies and ML does not compromise performance; instead, these systems often achieve state-of-the-art results, overcoming the perceived trade-off between accuracy and interpretability.

Application Domains and Tasks:

Within the healthcare domain, interpretable ML models have been proposed, leveraging taxonomical knowledge to enhance performance and interpretability. This is due to the high stakes nature of healthcare and the availability of medical ontologies. Additionally, recommendation systems that combine embedding models with knowledge graphs have been extensively researched. However, most systems offer static explanations with limited user interaction, highlighting the need for adaptive and interactive explanations that enable users to scrutinize and engage with the explanations.

Challenges and Future Research:

One central challenge in incorporating Semantic Web Technologies with ML is knowledge matching, which requires automated and reliable methods for aligning ML data with knowledge base entities. Future research should focus on mitigating the potential lack of data interconnectedness and the increased complexity of such systems. Furthermore, true explainable systems should incorporate reasoning and external knowledge that is human-understandable. Meaningful progress in Explainable AI (XAI) requires the development of standard design patterns and common evaluation criteria for combining ML with Semantic Web Technologies.

Actionable Advice:

  • 1. Focus on developing novel explanation algorithms that incorporate reasoning and external knowledge to enhance human-understandability.
  • 2. Explore the use of structured knowledge bases to enable users to scrutinize and interact with explanations in various forms, promoting user comprehension and engagement.
  • 3. Establish common grounds for model evaluation and comparison, replacing subjective assessments with rigorous practices, and incorporating user feedback in the evaluation process.

Conclusion:

The integration of Semantic Web Technologies with ML offers exciting opportunities for model explainability. By leveraging taxonomical knowledge and knowledge graphs, researchers have demonstrated enhanced interpretability without sacrificing performance. The healthcare domain and recommendation systems have been important drivers of research in this field. However, future research should address challenges related to knowledge matching, data interconnectedness, and system complexity. Additionally, the development of adaptive and interactive explanations, along with the establishment of common evaluation criteria, will contribute to the progress of XAI.

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