|  | Vol-2998 urn:nbn:de:0074-2998-3 Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

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

### Proceedings of the Workshop on Data meets Applied Ontologies in Explainable AI (DAO-XAI 2021)
part of Bratislava Knowledge September (BAKS 2021)

### Bratislava, Slovakia, September 18th to 19th, 2021

Edited by

### Roberto Confalonieri *
Oliver Kutz *
Diego Calvanese *,**

* Free University of Bozen-Bolzano, Faculty of Computer Science, Italy
** Department of Computing Science, Umeå University, Sweden

## 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

2021-10-27: submitted by Roberto Confalonieri, metadata incl. bibliographic data published under Creative Commons CC0
2021-11-02: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|