48 citations · 172 across the 15 of their papers we have counts for
15 papers
Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?
Haohan Zhang, Fengrui Hua, Chengjin Xu +3
The rapid advancement of Large Language Models (LLMs) has spurred discussions about their potential to enhance quantitative trading strategies. LLMs excel in analyzing sentiments a…
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
Zhongwu Chen, Chengjin Xu, Fenglong Su +2
Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive setti…
Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
Xuhui Jiang, Chengjin Xu, Yinghan Shen +7
The flourishing of knowledge graph applications has driven the need for entity alignment (EA) across KGs. However, the heterogeneity of practical KGs, characterized by differing sc…
Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge Graph
Zhongwu Chen, Chengjin Xu, Fenglong Su +2
In the last few years, the solution to Knowledge Graph (KG) completion via learning embeddings of entities and relations has attracted a surge of interest. Temporal KGs(TKGs) exten…
Ultrahyperbolic Knowledge Graph Embeddings
Bo Xiong, Shichao Zhu, Mojtaba Nayyeri +4
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies. The topological structures of real-wor…
TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph
Xueyuan Lin, Chengjin Xu, Haihong E +6
Multi-hop logical reasoning over knowledge graph (KG) plays a fundamental role in many artificial intelligence tasks. Recent complex query embedding (CQE) methods for reasoning foc…
Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
Chengjin Xu, Fenglong Su, Jens Lehmann
Entity alignment aims to identify equivalent entity pairs between different knowledge graphs (KGs). Recently, the availability of temporal KGs (TKGs) that contain time information…
Geometric Algebra based Embeddings for Static and Temporal Knowledge Graph Completion
Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen +1
Recent years, Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a Knowledge Graph (KG) into a g…
Multiple Run Ensemble Learning with Low-Dimensional Knowledge Graph Embeddings
Chengjin Xu, Mojtaba Nayyeri, Sahar Vahdati +1
Among the top approaches of recent years, link prediction using knowledge graph embedding (KGE) models has gained significant attention for knowledge graph completion. Various embe…
TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation
Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury +2
In the last few years, there has been a surge of interest in learning representations of entitiesand relations in knowledge graph (KG). However, the recent availability of temporal…
Motif Learning in Knowledge Graphs Using Trajectories Of Differential Equations
Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1
Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space (usu…
Knowledge Graph Embeddings in Geometric Algebras
Chengjin Xu, Mojtaba Nayyeri, Yung-Yu Chen +1
Knowledge graph (KG) embedding aims at embedding entities and relations in a KG into a lowdimensional latent representation space. Existing KG embedding approaches model entities a…
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury +2
Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal informatio…
Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding
Mojtaba Nayyeri, Chengjin Xu, Yadollah Yaghoobzadeh +2
Knowledge graphs (KGs) represent world's facts in structured forms. KG completion exploits the existing facts in a KG to discover new ones. Translation-based embedding model (Trans…
LogicENN: A Neural Based Knowledge Graphs Embedding Model with Logical Rules
Mojtaba Nayyeri, Chengjin Xu, Jens Lehmann +1
Knowledge graph embedding models have gained significant attention in AI research. Recent works have shown that the inclusion of background knowledge, such as logical rules, can im…