ECIR 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval
ECIR 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval
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DOI：
10.1007/978-3-031-28241-6_33
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Laura Dietz;Hannah Bast;Shubham Chatterjee;Jeffrey Dalton;E. Meij;A. D. Vries
中科院分区：
文献类型：
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作者：
Laura Dietz;Hannah Bast;Shubham Chatterjee;Jeffrey Dalton;E. Meij;A. D. Vries
本教程将概述信息检索的神经符号方法的最新进展。十年前，知识图谱和语义注释技术引发了关于如何最好地利用符号知识的积极研究。同时，神经方法已经被证明是通用的和高效的。从神经网络的角度来看，相同的表示方法可以服务于文档排序或知识图推理。端到端训练允许优化下游任务的复杂方法。我们正处于符号和神经研究进展合并为神经符号方法的时刻。基本的研究问题是如何最好地结合联合收割机的符号和神经的方法，什么样的符号/神经的方法是最适合的用例，以及如何最好地整合这两个想法，以推进信息检索的艺术状态。
This tutorial will provide an overview of recent advances on neuro-symbolic approaches for information retrieval. A decade ago, knowledge 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 reasoning. 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 symbolic and neural approaches, what kind of symbolic/neural approaches are most suitable for which use case, and how to best integrate both ideas to advance the state of the art in information retrieval.