TY - GEN
T1 - Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification
AU - Li, Yanhu
AU - Zhang, Taolin
AU - Li, Dongyang
AU - He, Xiaofeng
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Few-shot relation classification (RC) aims to determine the labeled relation between two entities in a given sentence using only a few training instances. Previous studies integrate models with explicit triple knowledge, using the inherent concepts of entities to improve the instance representation. However, these studies neglect the implicit structural knowledge present in the knowledge graph (KG). In this paper, we present SKProto, a knowledge-enhanced prototypical network that leverages deep structured semantic knowledge from the multi-hop neighbors of entity-linked concepts. Specifically, we propose a concept-guided hybrid attention mechanism to learn implicit structural semantic knowledge for enhancing the context-aware instance representation. To further distinguish subtle semantic differences among the concepts, the multi-granularity semantic distinction approach is proposed to construct the negative samples with various difficulties (i.e. hard, medium, and easy) based on the conceptual hierarchical structure. Experimental results on the FewRel 2.0 benchmark show that SKProto outperforms state-of-the-art models. We also demonstrate that SKProto has better robustness than other competitive models in low-shot scenarios.
AB - Few-shot relation classification (RC) aims to determine the labeled relation between two entities in a given sentence using only a few training instances. Previous studies integrate models with explicit triple knowledge, using the inherent concepts of entities to improve the instance representation. However, these studies neglect the implicit structural knowledge present in the knowledge graph (KG). In this paper, we present SKProto, a knowledge-enhanced prototypical network that leverages deep structured semantic knowledge from the multi-hop neighbors of entity-linked concepts. Specifically, we propose a concept-guided hybrid attention mechanism to learn implicit structural semantic knowledge for enhancing the context-aware instance representation. To further distinguish subtle semantic differences among the concepts, the multi-granularity semantic distinction approach is proposed to construct the negative samples with various difficulties (i.e. hard, medium, and easy) based on the conceptual hierarchical structure. Experimental results on the FewRel 2.0 benchmark show that SKProto outperforms state-of-the-art models. We also demonstrate that SKProto has better robustness than other competitive models in low-shot scenarios.
KW - Contrastive Learning
KW - Few-shot Learning
KW - Knowledge Graph
KW - Relation Classification
UR - https://www.scopus.com/pages/publications/85173560248
U2 - 10.1007/978-3-031-33380-4_11
DO - 10.1007/978-3-031-33380-4_11
M3 - 会议稿件
AN - SCOPUS:85173560248
SN - 9783031333798
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 138
EP - 149
BT - Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Proceedings
A2 - Kashima, Hisashi
A2 - Ide, Tsuyoshi
A2 - Peng, Wen-Chih
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
Y2 - 25 May 2023 through 28 May 2023
ER -