| Vol-2529 urn:nbn:de:0074-2529-2 Copyright © 2019 for the individual papers by the papers' authors. Copyright © 2020 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).

# FCA4AI 2019
What can FCA do for Artificial Intelligence?

### Proceedings of the 7th International Workshop "What can FCA do for Artificial Intelligence"?
co-located with International Joint Conference on Artificial Intelligence (IJCAI 2019)

### Macao, China, August 10, 2019.

Edited by

### Sergei O. Kuznetsov *
Amedeo Napoli **
Sebastian Rudolph ***

* NRU HSE, Faculty of Computer Science, Moscow, Russia
** Université de Lorraine, CNRS, Inria, LORIA, F-54000, Nancy, France
*** Technische Universität, Dresden, Germany

## Table of Contents

- Preface

- Enabling Natural Language Analytics over Relational Data Using Formal Concept Analysis (short paper) 7-18
  C. Anantaram, Mouli Rastogi, Mrinal Rawat, Pratik Saini
- Using Formal Concept Analysis to Explain Black Box Deep Learning Classification Models (short paper) 19-26
  Amit Sangroya, C. Anantaram, Mrinal Rawat, Mouli Rastogi
- Validating Correctness of Textual Explanation with Complete Discourse Trees 29-38
  Boris Galitsky, Dmitry Ilvovsky
- Least General Generalization of the Linguistic Structures (short paper) 39-44
  Boris Galitsky, Dmitry Ilvovsky
- Truth and Justification in Knowledge Representation 45-56
  Andrei Rodin, Serge Kovalyov
- FCA-based Approach to Machine Learning (short paper) 57-64
  Dmitry V. Vinogradov
- Clustering of Biomedical Data Using the Greedy Clustering Algorithm Based on Interval Pattern Concepts (short paper) 65-74
  Alexey V. Galatenko, Stepan A. Nersisyan, Vera V. Pankratieva
- Increasing the Efficiency of Packet Classifiers with Closed Descriptions 75-86
  Elizaveta F. Goncharova, Sergei O. Kuznetsov

2019-12-16: submitted by Amedeo Napoli, Tatiana Makhalova, metadata incl. bibliographic data published under Creative Commons CC0
2020-01-04: published on CEUR-WS.org |valid HTML5|