DAO-XAI 2021
International Workshop on Data meets Applied Ontologies in Explainable AI

Table of Contents

• Preface
  

Invited Talks

• Learning and Reasoning with Logic Tensor Networks: The Framework and an Application (Abstract of Invited Talk)
  Luciano Serafini

Regular Papers

• Automated and Explainable Ontology Extension based on Deep Learning: A Case Study in the Chemical Domain
  Adel Memarian, Martin Glauer, Fabian Neuhaus, Till Mossakowski, Janna Hastings
• Semantic Queries Explaining Opaque Machine Learning Classifiers
  Jason Liartis, Edmund Dervakos, Orfeas Menis Mastromichalakis, Alexandros Chortaras, Giorgos Stamou
• Complementing Language Embeddings with Knowledge Bases for Specific Domains
  Paolo Tenti, Gabriella Pasi, Rafael Peñaloza
• Knowledge Representation for Explainability in Collaborative Robotics and Adaptation
  Alberto Olivares-Alarcos, Sergi Foix, Guillem Alenyà
• Towards Knowledge-driven Distillation and Explanation of Black-box Models
  Roberto Confalonieri, Pietro Galliani, Oliver Kutz, Daniele Porello, Guendalina Righetti, Nicolas Troquard
• Knowledge-Based Construction of Confusion Matrices for Multi-Label Classification Algorithms using Semantic Similarity Measures
  Houcemeddine Turki, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha

Extended Abstracts

• Can Ontologies help making Machine Learning Accountable? (Extended Abstract)
  Iker Esnaola-Gonzalez
• Using Ontologies for Human-understandable Explanations of Black-box Models (Extended Abstract)
  Roberto Confalonieri, Tillman Weyde, Tarek R. Besold, Fermín Moscoso del Prado Martín