[DL] [CFP] Special Session on Deep Learning and Ontology @ICMLA-2020

Sarra Ben Abbès benabbessarra at gmail.com
Wed Jul 22 15:53:55 CEST 2020

Dear colleagues,

*The 1st Special Session on Deep Learning and Ontology*

*(DLOnto 2020)*


* As part of The 19th IEEE International Conference on Machine Learning and
Applications (IEEE ICMLA 2020) https://www.icmla-conference.org/icmla20/

*December 14-17, 2020 Miami, Florida, USA*

*Scope and Objectives*

In recent years, deep learning is applied successfully and achieved
state-of-the-art performance in a variety of domains, such as image
analysis and data mining. Despite this success, deep learning models remain
hard to analyze and understand what knowledge is represented in them, and
how they generate decisions. Deep learning meets recently ontologies and
tries to model data representations with many layers of non-linear
transformations. Ontology is a structured knowledge representation that
facilitates data access (data sharing and reuse) and assists the deep
learning process as well.

The combination of deep learning and ontologies might be beneficial for
different tasks: (1) Deep Learning for Ontologies: ontology population,
ontology extension, ontology learning, ontology alignment and integration,
and (2) Ontologies for Deep Learning: semantic graph embeddings, latent
semantic representation, hybrid embeddings (symbolic and semantic

This special session aims at demonstrating recent and future advances in
semantic rich deep learning by using ontology which can reduce the semantic
gap between the data, applications, machine learning process, in order to
obtain a semantic-aware approaches. In addition, the goal of this session
is to bring together an area for experts from industry, science and
academia to exchange ideas and present results of on-going research in
structured knowledge and deep learning approaches.

This special session invites submissions of original works that is related
–but are not limited to – the topics below:

   - Approaches for construction ontology embeddings
   - Ontology-based text classification
   - Learning ontology embeddings
   - Semantic role labelling
   - Ontology reasoning with Deep Neural Networks
   - Ontology debugging and completion using deep learning methods
   - Deep learning for ontological semantic annotations
   - Spatial and temporal ontology embeddings
   - Ontology alignment and matching based on deep learning models
   - Application of deep ontologies in specific domains (e.g. energy,
   medical, IoT)
   - Ontology learning from text using deep learning models
   - Deep Linked Data
   - Real-life and industrially relevant applications:
      - Recommender Systems based on Knowledge Graphs
      - Knowledge Graph-Based Sentiment Analysis
      - Question Answering exploiting Knowledge Graphs Embeddings
      - Link Prediction

*Submission Guidelines and Instructions *

Papers submitted for reviewing should conform to IEEE specifications.
Manuscript templates can be downloaded from IEEE website
 The maximum length of papers is 8 pages. All the papers will go through
double-blind peer review process. *Authors’ names and affiliations should
not appear in the submitted paper*. Authors’ prior work should be cited in
the third person. Authors should also avoid revealing their identities
and/or institutions in the text, figures, links, etc.

Papers must be submitted via the CTM System
<https://cmt3.research.microsoft.com/ICMLA2020> by selecting the track
“Special Session on Deep Learning and Ontology”. All accepted papers must
be presented by one of the authors, who must register. Detailed
instructions for submitting papers can be found at How to Submit

*Paper Publication: *

Accepted papers will be published in the ICMLA 2020 conference proceedings
(to be published by IEEE).

*Important Dates: *

*Submission Deadline: August 6, 2020*
Notification of Acceptance: September 4, 2020
Camera-ready papers & Pre-Registration: October 1, 2020

*Special Session Organizers:*

Sarra Ben Abbès, ENGIE, France

Rim Hantach, ENGIE, France

Philippe Calvez, ENGIE, France

*Program committee: *

Lynda Temal, ENGIE, France

Nada Mimouni, CNAM, France

Nouha Omrane, JEMS, France
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