4th Workshop on Semantic Deep Learning (SemDeep-4)

This workshop was held at ISWC 2018 (8-12 October in Monterey, California)

Previous editions: SemDeep-1@ESWC 2017, SemDeep-2@IWCS 2017, SemDeep-3@COLING 2018

Proceedings

Workshop proceedings are now available at http://ceur-ws.org/Vol-2241/

Workshop Description

Semantic Web (SW) Technologies and Deep Learning (DL) share the goal of creating intelligent artifacts. Both disciplines have had a remarkable impact in data and knowledge analysis, as well as knowledge representation, and in fact constitute two complementary directions for modeling expressible linguistic phenomena and solving semantically complex problems. In this context, and following the main foundations set in past editions, SemDeep-4 aims to bring together SW and DL research as well as industrial communities. SemDeep is interested in contributions of DL to classic problems in semantic applications, such as: (semi-automated) ontology learning, ontology alignment, ontology annotation, duplicate recognition, ontology prediction, knowledge base completion, relation extraction, and semantically grounded inference, among many others. At the same time, we invite contributions that analyse the interaction of SW technologies and resources with DL architectures, such as knowledge-based embeddings, collocation discovery and classification, or lexical entailment, to name only a few. This workshop seeks to provide an invigorating environment where semantically challenging problems which appeal to both Semantic Web and Computational Linguistic communities are addressed and discussed.

Topics of Interest Include but Are not Limited to:

  • Structured knowledge in deep learning
    • neural networks and logic rules for semantic compositionality
    • learning and applying knowledge graph embeddings to NLP tasks
    • learning semantic similarity and encoding distances as knowledge graph
    • ontology-based text classification
    • multilingual resources for neural representations of linguistics
    • semantic role labeling
  • Reasoning and inferences and deep learning
    • commonsense reasoning and vector space models
    • reasoning with deep learning methods
  • Learning knowledge representations with deep learning
    • deep learning methods for knowledge-base completion
    • deep ontology learning
    • deep learning models for learning knowledge representations from text
    • deep learning ontological annotations