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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 

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   null (Ed.)

   Ad hoc table retrieval is the problem of identifying the most relevant datasets to a user's query. We present an approach to the problem that builds a knowledge graph by combining information about the collection of tables with external sources such as WordNet and pretrained Glove embeddings. We apply multi-relational graph convolutional networks to learn embeddings for the knowledge graph nodes and utilize three different methods to create vectors representing the tables and queries from these embeddings. We create a novel learning-to-rank neural architecture that incorporates the multiple embeddings in order to improve table retrieval results. We evaluate our approach using two large collections of tables from public WikiTables and Web tables data, demonstrating substantial improvements over state-of-the-art methods in table retrieval. 

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3. Crossing Variational Autoencoders for Answer Retrieval

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