This half-day workshop will take place at the ESWC 2017 in Portoroz on 29 May 2017 in the afternoon
This workshop aims to bring together Semantic Web resources and deep learning. Semantic Web technologies and deep learning share the goal of creating intelligent artifacts that emulate human capacities such as reasoning, validating, and predicting. Both fields have been considerably impacting data and knowledge analysis as well as representation. Deep learning represents a set of machine learning algorithms that learn data representations by means of transformations with multiple processing layers. This algorithmic set has frequently been applied to feature learning, such as morphological tagging or speaker verification. Semantic Web technologies and knowledge representation boost the re-use and sharing of knowledge in a structured and machine readable fashion. Semantic resources, such as WikiData or BabelNet, and methods have been successfully applied to semantic data mining.
Machine learning has been successfully applied to (semi-automated) ontology learning, ontology alignment, ontology annotation, duplicate recognition, and ontology prediction. Ontologies have been repeatedly utilized as input to machine learning tasks and as background knowledge to guide such tasks. Hybrid approaches, such as knowledge graph embeddings, hold the potential of improving the effectiveness of knowledge-related tasks. This workshop offers a platform for discussing such hybrid approaches and for fostering future collaborations between those two fields.
We thus invite submissions that illustrate how deep learning tasks can benefit from Semantic Web resources and technologies. At the same time, we are interested in submissions that show how knowledge representation can assist in deep learning tasks and how knowledge representation systems can build on top of deep learning results.