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

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Glasp

Aug 01, 2023 • 3 min read

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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 a challenge in high stakes domains such as healthcare and transportation. To address this issue, researchers have explored the integration of Semantic Web Technologies to enhance model explainability. This article aims to explore the proposed combinations of ML and Semantic Web Technologies, important application domains, evaluation methods, and future directions in this research field.

Connecting ML and Semantic Web Technologies for Explainability:

Doran et al. argue that truly explainable systems should incorporate reasoning elements that utilize knowledge bases to create human-understandable and unbiased explanations. The use of Semantic Web Technologies alongside ML algorithms primarily focuses on two types of models: supervised classification tasks using Neural Networks and unsupervised embedding tasks. Neural Networks are widely used in supervised learning, often incorporating taxonomical information from knowledge bases. These integrated systems achieve state-of-the-art performance without sacrificing interpretability.

Applications in Healthcare and Recommendation Systems:

In the domain of healthcare, numerous interpretable ML models have been proposed, leveraging taxonomical knowledge to improve performance and interpretability. The high stakes nature of healthcare and the availability of medical ontologies contribute to the abundance of such systems. Recommendation systems, on the other hand, commonly combine embedding models with knowledge graphs. However, most systems offer static explanations with limited user interaction, posing a challenge for future research.

Challenges and Future Directions:

One central challenge in combining ML with Semantic Web Technologies is knowledge matching, which involves matching ML data with knowledge base entities. Future research should focus on developing automated and reliable methods for knowledge matching, as well as addressing the potential lack of data interconnectedness and increased system complexity. Additionally, truly explainable systems should incorporate reasoning and external knowledge in a human-understandable manner. Explanations should also be adaptive and interactive to maximize user benefit.

Actionable Advice:

1. Explore the integration of Semantic Web Technologies with ML algorithms to enhance model explainability in high stakes domains such as healthcare and transportation.

2. Incorporate reasoning elements and knowledge bases to create human-understandable and unbiased explanations.

3. Focus on developing automated and reliable methods for knowledge matching and establish common evaluation criteria for model explainability.

Conclusion:

The integration of ML and Semantic Web Technologies presents exciting opportunities for enhancing model explainability. The review suggests that the combination of these technologies does not compromise prediction performance but often improves it. The domains of healthcare and recommendation systems drive much of the research in this field. Meaningful progress requires not only novel explanation algorithms but also common grounds for evaluation and comparison. By addressing the challenges and future directions outlined, researchers can advance the field of explainable AI and create more transparent and trustworthy models.

Resource:

  1. "semex_paper1.pdf", https://ceur-ws.org/Vol-2465/semex_paper1.pdf (Glasp)
  2. "自律分散型でコミュニティが 成長し続ける5つのポイント。100人カイギを例に。|シェア街メディア|note", https://note.com/sharemachi/n/n083921f65289?fbclid=IwAR0NeWS4LUteOyk37H8rCFOuwPrHQ1XVlr79qMDzqBGNNRefYv-YJQvpf60 (Glasp)

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