Abstract
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. Materials are available online: https://github.com/laura-dietz/neurosymbolic-representations-for-IR.
Recommended Citation
L. Dietz et al., "Neuro-Symbolic Representations for Information Retrieval," SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3436 - 3439, Association for Computing Machinery, Jul 2023.
The definitive version is available at https://doi.org/10.1145/3539618.3594246
Department(s)
Computer Science
Publication Status
Public Access
Keywords and Phrases
document representation; entities; knowledge graph; neural networks; neuro-symbolic representation
International Standard Book Number (ISBN)
978-145039408-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Association for Computing Machinery, All rights reserved.
Publication Date
19 Jul 2023
Comments
National Science Foundation, Grant 1846017