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
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