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This tutorial will provide an overview of recent advances on neuro- symbolic approaches for information retrieval. A decade ago, knowl- edge graphs and semantic annotations technology led to active research on how to best leverage symbolic knowledge. At the same time, neural methods have demonstrated to be versatile and highly effective. From a neural network perspective, the same representation approach can service document ranking or knowledge graph rea- soning. End-to-end training allows to optimize complex methods for downstream tasks. We are at the point where both the symbolic and the neural research advances are coalescing into neuro-symbolic approaches. The underlying research questions are how to best combine sym- bolic and neural approaches, what kind of symbolic/neural ap- proaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval. Materials are available online: https://github.com/laura-dietz/ neurosymbolic-representations-for-IR