Call for Papers ESWC 2019 Machine Learning Track

Machine Learning (ML) methods and techniques are playing an increasingly important role for the Semantic Web showing a valuable role for solving tasks such as ontology mapping, entity (inter-)linking, rule mining, entity retrieval and recommendation. In particular, with the wide availability of large amounts of structured and/or semi-structured data on the Web, machine learning techniques have been essential for extracting, fusing and integrating data and knowledge, for instance, for knowledge graph construction or knowledge base augmentation. In addition, neural networks and deep learning-based approaches for distributional semantics and word/entity embeddings have gained wide popularity for a range of tasks.

This track invites high quality submissions that focus on the definition, adoption and assessment of new ML methods for the SW. Papers showing how ML methods and techniques have been applied to develop and improve the SW field or showing how SW technologies and resources have been used to enhance different ML tasks are also welcome. Reproducibility of all presented works is strictly required and should ideally be supported by providing links to used datasets, source code, queries/test cases or live deployments.