activity
20212026
most citedChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

70 citations · 114 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2026

Ride the Wave: Precision-Allocated Sparse Attention for Smooth Video Generation

Wentai Zhang, Ronghui Xi, Shiyao Peng +4

Video Diffusion Transformers have revolutionized high-fidelity video generation but suffer from the massive computational burden of self-attention. While sparse attention provides…

cs.CL2023★ 70 cited

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models

Haoran Luo, Haihong E, Zichen Tang +9

Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge…

cs.AI2023★ 2 cited

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

Haoran Luo, Haihong E, Yuhao Yang +10

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to…

cs.AI2023★ 18 cited

HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

Haoran Luo, Haihong E, Yuhao Yang +7

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxilia…

cs.AI2022★ 5 cited

NQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs

Haoran Luo, Haihong E, Yuhao Yang +6

Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary rel…

cs.AI2022★ 9 cited

DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing

Haoran Luo, Haihong E, Ling Tan +3

In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is co…

cs.LG2022★ 5 cited

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…

cs.AI2022★ 1 cited

FLEX: Feature-Logic Embedding Framework for CompleX Knowledge Graph Reasoning

Xueyuan Lin, Haihong E, Gengxian Zhou +4

Current best performing models for knowledge graph reasoning (KGR) introduce geometry objects or probabilistic distributions to embed entities and first-order logical (FOL) queries…

cs.CL2021★ 4 cited

EchoEA: Echo Information between Entities and Relations for Entity Alignment

Xueyuan Lin, Haihong E, Wenyu Song +1

Entity alignment (EA) plays an important role in automatically integrating knowledge graphs (KGs) from multiple sources. Recent approaches based on Graph Neural Network (GNN) obtai…