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Where a decade ago data scientists were still engineering features, manually picking out the details we thought salient, they now prefer the data in their raw form. As long as we can assume that all relevant and irrelevant information is present in the input data, we can design deep models that build up intermediate representations to sift out relevant features. However, these models are often domain specific and tailored to the task at hand, and therefore unsuited for learning on heterogeneous knowledge: information of different types and from different domains. If we can develop methods that operate on this form of knowledge, we can dispense with a great deal more ad-hoc feature engineering and train deep models end-to-end in many more domains. To accomplish this, we first need a data model capable of expressing heterogeneous knowledge naturally in various domains, in as usable a form as possible, and satisfying as many use cases as possible. In this position paper, we argue that the knowledge graph is a suitable candidate for this data model. We further describe current research and discuss some of the promises and challenges of this approach. https://doi.org/10.3233/DS-170007 http://purl.org/dc/terms/date 2017-10-17 https://doi.org/10.3233/DS-170007 http://purl.org/dc/terms/isPartOf http://id.crossref.org/issn/2451-8492 https://doi.org/10.3233/DS-170007 http://purl.org/dc/terms/title The knowledge graph as the default data model for learning on heterogeneous knowledge https://doi.org/10.3233/DS-170007 http://purl.org/ontology/bibo/issue 1-2 https://doi.org/10.3233/DS-170007 http://purl.org/ontology/bibo/volume 1 https://doi.org/10.3233/DS-170007 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/PositionPaper https://orcid.org/0000-0001-9079-039X http://schema.org/affiliation https://ror.org/008xxew50 https://orcid.org/0000-0001-9079-039X http://xmlns.com/foaf/0.1/name Victor de Boer https://orcid.org/0000-0002-0189-5817 http://schema.org/affiliation https://ror.org/008xxew50 https://orcid.org/0000-0002-0189-5817 http://xmlns.com/foaf/0.1/name Peter Bloem https://orcid.org/0000-0003-2415-8438 http://schema.org/affiliation https://ror.org/008xxew50 https://orcid.org/0000-0003-2415-8438 http://schema.org/email w.x.wilcke@vu.nl https://orcid.org/0000-0003-2415-8438 http://xmlns.com/foaf/0.1/name Xander Wilcke https://ror.org/008xxew50 http://xmlns.com/foaf/0.1/name Faculty of Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 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