Ecir 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval
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.
Recommended Citation
L. Dietz et al., "Ecir 23 Tutorial: Neuro-Symbolic Approaches for Information Retrieval," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13982 LNCS, pp. 324 - 330, Springer, Jan 2023.
The definitive version is available at https://doi.org/10.1007/978-3-031-28241-6_33
Department(s)
Computer Science
Keywords and Phrases
IR; Neural networks; Semantics
International Standard Book Number (ISBN)
978-303128240-9
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2023
