Concept-driven representation learning model for knowledge graph completion
Y Xiang, H He, Z Yu, Y Huang - Expert Systems with Applications, 2024 - Elsevier
Abstract Knowledge graph completion (KGC) aims to address the problem of incomplete
knowledge graph (KG) by predicting missing entities or relations. Representation learning …
knowledge graph (KG) by predicting missing entities or relations. Representation learning …
Knowledge Graph Compression Enhances Diverse Commonsense Generation
Generating commonsense explanations requires reasoning about commonsense
knowledge beyond what is explicitly mentioned in the context. Existing models use …
knowledge beyond what is explicitly mentioned in the context. Existing models use …
Cyclical Contrastive Learning Based on Geodesic for Zero-shot Cross-lingual Spoken Language Understanding
X Cheng, Z Zhu, B Yang, X Zhuang, H Li… - Findings of the …, 2024 - aclanthology.org
Owing to the scarcity of labeled training data, Spoken Language Understanding (SLU) is still
a challenging task in low-resource languages. Therefore, zero-shot cross-lingual SLU …
a challenging task in low-resource languages. Therefore, zero-shot cross-lingual SLU …
Context-Aware Commonsense Knowledge Graph Reasoning With Path-Guided Explanations
Commonsense knowledge graphs (CKGs) store massive commonsense knowledge as
triples whose nodes consist of free-form texts. CKG reasoning aims to predict missing nodes …
triples whose nodes consist of free-form texts. CKG reasoning aims to predict missing nodes …
ATAP: Automatic Template-Augmented Commonsense Knowledge Graph Completion via Pre-Trained Language Models
F Zhang, Y Ding, J Cheng - … of the 2024 Conference on Empirical …, 2024 - aclanthology.org
The mission of commonsense knowledge graph completion (CKGC) is to infer missing facts
from known commonsense knowledge. CKGC methods can be roughly divided into two …
from known commonsense knowledge. CKGC methods can be roughly divided into two …
Efficient Knowledge Infusion via KG-LLM Alignment
To tackle the problem of domain-specific knowledge scarcity within large language models
(LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective …
(LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective …
KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining
Dialogical Argument Mining (DialAM) is an important branch of Argument Mining (AM).
DialAM-2024 is a shared task focusing on dialogical argument mining, which requires us to …
DialAM-2024 is a shared task focusing on dialogical argument mining, which requires us to …
[PDF][PDF] HiHo: A Hierarchical and Homogenous Subgraph Learning Model for Knowledge Graph Relation Prediction
J Ma, Y Ma, F Zhang, Y Wang, X Luo, C Li, Y Qiao - semantic-web-journal.net
Relation prediction in Knowledge Graphs (KGs) aims to anticipate the connections between
entities. While both transductive and inductive models are incorporated for context …
entities. While both transductive and inductive models are incorporated for context …