Dear colleagues and researchers,
Please consider submitting a paper for the 2nd International workshop "*Deep Learning meets Ontologies and Natural Language Processing*" which will be held online or in Hersonissos, Greece - June 6th 2021.
** *DeepOntoNLP* - Call for Papers ***
Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP) https://sites.google.com/view/deepontonlp-eswc2021
in conjunction with ESWC 2021 https://2021.eswc-conferences.org/ online or in Hersonissos, Greece
**Important dates**
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Workshop paper submission due: *March 1st, 2021** March 14th, 2021* -
Workshop paper notifications: March 31st, 2021 -
Workshop paper camera-ready versions due: April 9th, 2021 -
Workshop: June 6th, 2021 (Half-Day)
All deadlines are 23:59 anywhere on earth (UTC-12).
**Workshop description**
In recent years, deep learning is applied successfully and achieved state-of-the-art performance in a variety of domains, such as image analysis. Despite this success, deep learning models remain hard to analyse data and understand what knowledge is represented in them, and how they generate decisions.
Deep Learning (DL) meets Natural Language Processing (NLP) to solve human language problems for further applications, such as information extraction, machine translation, search and summarization. Previous works has attested the positive impact of domain knowledge on data analysis and vice versa, for example pre-processing data, searching data, redundancy and inconsistency data, knowledge engineering, domain concepts and relationships extraction, etc. Ontology is a structured knowledge representation that facilitates data access (data sharing and reuse) and assists the DL process as well. DL meets recently ontologies and tries to model data representations with many layers of non-linear transformations.
The combination of DL, ontologies and NLP might be beneficial for different tasks:
· Deep Learning for Ontologies: ontology population, ontology extension, ontology learning, ontology alignment and integration,
· Ontologies for Deep Learning: semantic graph embeddings, latent semantic representation, hybrid embeddings (symbolic and semantic representations),
· Deep Learning for NLP: summarization, translation, named entity recognition, question answering, document classification, etc.
· NLP for Deep Learning: parsing (part-of-speech tagging), tokenization, sentence detection, dependency parsing, semantic role labeling, semantic dependency parsing, etc.
**Objective** This workshop aims at demonstrating recent and future advances in semantic rich deep learning by using Semantic Web and NLP techniques 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 workshop is to bring together an area for experts from industry, science and academia to exchange ideas and discuss results of on-going research in natural language processing, structured knowledge and deep learning approaches.
We invite the submission of original works that is related -- but are not limited to -- the topics below.
**Topics of interests**
· Construction ontology embeddings
· Ontology-based text classification
· Learning ontology embeddings
· Semantic role labelling
· Ontology reasoning with Deep Neural Networks
· Deep learning for ontological semantic annotations
· Spatial and temporal ontology embeddings
· Ontology alignment and matching based on deep learning models
· Ontology learning from text using deep learning models
· Unsupervised Learning
· Text classification using deep models
· Neural machine translation
· Deep question answering
· Deep text summarization
· Deep speech recognition
· and so on.
**Submission**
The workshop is open to submit unpublished work resulting from research that presents original scientific results, methodological aspects, concepts and approaches. All submissions must be PDF documents written in English and formatted according to LNCS instructions for authors https://www.google.com/url?q=https%3A%2F%2Fwww.springer.com%2Ffr%2Fcomputer-science%2Flncs%2Fconference-proceedings-guidelines&sa=D&sntz=1&usg=AFQjCNGhadQkou1B6uTwaCrX2p9HjIC9Iw. Papers are to be submitted through the Easychair Conference Management System https://easychair.org/conferences/?conf=deepontonlp2021.
We welcome the following types of contributions:
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Full research papers (8-10 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics -
Short papers (4-6 pages): Ongoing works with relevant preliminary results, opened to discussion.
At least one author of each accepted paper must register for the workshop, in order to present the paper, there, and to the conference. For further instructions please refer to the ESWC 2021 https://www.google.com/url?q=https%3A%2F%2F2021.eswc-conferences.org%2F&sa=D&sntz=1&usg=AFQjCNEOdqEjLIkWyjAg16Zm5dm7MZ9kyg page.
**Publication**
The best papers from this workshop may be included in the supplementary proceedings of ESWC 2021.
**Workshop chairs**
*Sarra BEN ABBES*, Engie, France
*Rim HANTACH*, Engie, France
*Philippe CALVEZ*, Engie, France
**Program Committee**
Nada Mimouni, CNAM, France Lynda Temal, Engie, France Davide Buscaldi, LIPN, Université Sorbonne Paris Nord, France Valentina Janev, Mihajlo Pupin Institute, Serbia Mohamed Hedi Karray, LGP-INP-ENIT, Université de Toulouse, France Efstratios Kontopoulos, Catalink Ltd, Cyprus Wei Hu, Nanjing University, China Sanju Tiwari, Universidad Autonoma de Tamaulipas, Mexico Linda Elmhadhbi, Université de Toulouse, France Amir Laadhar, Aalborg University, Denmark Yannis Haralambous, IMT Atlantique, France