Deep Learning meets Ontologies and Natural Language Processing
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. In this context, 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 such as ontology learning, semantic graph embeddings, summarization, semantic role labeling. 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.
4th Workshop on Foundational Ontology (FOUST IV)
Foundational ontologies are attempts to systematise categories of thought or reality which are common to all or almost all subject-matters. Commonly considered examples of such categories include ‘object’, ‘quality’, ‘function’, ‘role’, ‘process’, ‘event’, ‘time’, and ‘place’. Among existing foundational ontologies, there is both a substantial measure of agreement and some dramatic disagreements. There is currently no uniform consensus concerning how a foundational ontology should be organised, how far its ‘reach’ should be (e.g., is the distinction between physical and non-physical entities sufficiently fundamental to be included here?), and even what role it should play in relation to more specialised domain ontologies. The purpose of the FOUST workshop is to provide a forum for researchers to present work on specific foundational ontologies as well as foundational ontologies in general and their relations to each other and to the wider ontological enterprise.