activity
20232026
most citedKnowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

31 citations · 57 across the 14 of their papers we have counts for

collaborators

15 papers

cs.CV2026

OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation

Yajing Xu, Yarong Lan, Jiaoyan Chen +6

While text-to-image models exhibit remarkable visual fidelity, they frequently violate fundamental physical commonsense. Existing benchmarks often rely on coarse-grained descriptio…

cs.CL2026

CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering

Zhichao Yan, Shizhao Li, Jiapu Wang +5

Long-form question answering increasingly relies on retrieved evidence to make LLM outputs verifiable, with inline citations tracing claims to source documents. However, existing s…

cs.CL2026

Beyond Factual Accuracy: Evaluating Global Reasoning Integrity in RAG Systems with LogicScore

Zhichao Yan, Yunxiao Zhao, Jiapu Wang +4

Current evaluation methods for Retrieval Augmented Generation (RAG) suffer from \textit{factual myopia}: they relentlessly emphasize factual accuracy yet neglect global logical int…

cs.CL2025

Prompting Large Language Models with Partial Knowledge for Answering Questions with Unseen Entities

Zhichao Yan, Jiapu Wang, Jiaoyan Chen +6

Retrieval-Augmented Generation (RAG) shows impressive performance by supplementing and substituting parametric knowledge in Large Language Models (LLMs). Retrieved knowledge can be…

cs.AI2025

Multi-modal Knowledge Graph Generation with Semantics-enriched Prompts

Yajing Xu, Zhiqiang Liu, Jiaoyan Chen +7

Multi-modal Knowledge Graphs (MMKGs) have been widely applied across various domains for knowledge representation. However, the existing MMKGs are significantly fewer than required…

cs.CL2024★ 2 cited

MKGL: Mastery of a Three-Word Language

Lingbing Guo, Zhongpu Bo, Zhuo Chen +10

Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks. Yet, their application to knowledge graphs (KGs),…

cs.CL2024

Atomic Fact Decomposition Helps Attributed Question Answering

Zhichao Yan, Jiapu Wang, Jiaoyan Chen +3

Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, includ…

cs.LG2024

Contrast All the Time: Learning Time Series Representation from Temporal Consistency

Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor

Representation learning for time series using contrastive learning has emerged as a critical technique for improving the performance of downstream tasks. To advance this effective…

cs.AI2024★ 4 cited

Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion

Wen Zhang, Yajing Xu, Peng Ye +5

Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They ar…

cs.CL2024

TrustUQA: A Trustful Framework for Unified Structured Data Question Answering

Wen Zhang, Long Jin, Yushan Zhu +6

Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs…

cs.CL2024★ 3 cited

Noise-powered Multi-modal Knowledge Graph Representation Framework

Zhuo Chen, Yin Fang, Yichi Zhang +5

The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for…

cs.AI2024★ 31 cited

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey

Zhuo Chen, Yichi Zhang, Yin Fang +12

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…

cs.CV2024

Knowledge-Aware Neuron Interpretation for Scene Classification

Yong Guan, Freddy Lecue, Jiaoyan Chen +2

Although neural models have achieved remarkable performance, they still encounter doubts due to the intransparency. To this end, model prediction explanation is attracting more and…

cs.CL2024★ 1 cited

Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs

Nan Hu, Jiaoyan Chen, Yike Wu +6

Attributed Question Answering (AQA) has attracted wide attention, but there are still several limitations in evaluating the attributions, including lacking fine-grained attribution…

cs.CL2023★ 16 cited

Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model

Yuxia Geng, Jiaoyan Chen, Yuhang Zeng +5

Both graph structures and textual information play a critical role in Knowledge Graph Completion (KGC). With the success of Pre-trained Language Models (PLMs) such as BERT, they ha…