The second edition of this workshop aims at demonstrating recent and future advancesin semantic rich deep learning by using Semantic Web and NLP techniques which canreduce the semantic gap between the data, applications, machine learning process, inorder to obtain a semantic-aware approaches. NLP for Deep Learning: parsing (part-of-speecb tagging), tokenization, sentence detection, dependency parsing, semantic role labeling, semantic dependency parsing, etc.
Deep Learning for NLP: summarization, translation, named entity recognition,question answering, document classification, etc.Ĥ. Ontologies for Deep Learning: semantic graph embeddings, latent semantic rep-resentation, hybrid embeddings (symbolic and semantic representations),ģ. Deep Learning for Ontologies: ontology population, ontology extension, ontology learning, ontology alignment and integration,Ģ. The combination of DL, ontologies and NLP might be beneficial for different tasks:ġ. DL 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 DL process as well. Previous works have 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.
Despite this success, deep learning models remain hard to analyse data and understand what knowledge is represented in them, and how they generate decisions.ĭeep Learning (DL) meets Natural Language Processing (NLP) to solve human language problems for further applications such as information extraction, machine translation, search and summarization. In recent years, deep learning is applied successfully and achieved state-of-the-art performance in a variety of domains, such as image analysis. Organisers: Sarra Ben Abbès, Rim Hantach and Philippe Calvez This tutorial is aimed at both novice ontologists (who may directly benefit from having this easy-to-use and freely available tool available, in their own modelling work) and more experienced ontology engineering researchers (who may find the tool useful as an aid in method development and evaluation, or as a teaching aid when introducing ontology engineering to less experienced colleagues). During this tutorial, we will demonstrate CoModIDE and teach the participants to use it. The authors have developed what we believe is the first graphical drag-and-drop-based tool for modular pattern-based ontology engineering, CoModIDE. However, the tooling required to fully realize pattern-based approaches to ontology development, have long been lacking consequently, comparatively few ontology engineering projects have in fact been carried out using this promising approach. Modular ontology modeling (MOMo) using ontology design patterns enables non-experts to develop ontologies with reasonable degree of correctness and efficiency. Organisers: Cogan Shimizu and Karl Hammar