26 citations · 44 across the 11 of their papers we have counts for
11 papers
GR-Agent: Adaptive Graph Reasoning Agent under Incomplete Knowledge
Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang +5
Large language models (LLMs) achieve strong results on knowledge graph question answering (KGQA), but most benchmarks assume complete knowledge graphs (KGs) where direct supporting…
Certainty in Uncertainty: Reasoning over Uncertain Knowledge Graphs with Statistical Guarantees
Yuqicheng Zhu, Jingcheng Wu, Yizhen Wang +4
Uncertain knowledge graph embedding (UnKGE) methods learn vector representations that capture both structural and uncertainty information to predict scores of unseen triples. Howev…
What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge
Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang +5
Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) is an increasingly explored approach for combining the reasoning capabilities of large language models with the struct…
Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness
Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang +4
Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) is a technique that enhances Large Language Model (LLM) inference in tasks like Question Answering (QA) by retrieving…
Supposedly Equivalent Facts That Aren't? Entity Frequency in Pre-training Induces Asymmetry in LLMs
Yuan He, Bailan He, Zifeng Ding +8
Understanding and mitigating hallucinations in Large Language Models (LLMs) is crucial for ensuring reliable content generation. While previous research has primarily focused on "w…
Ontology Embedding: A Survey of Methods, Applications and Resources
Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho +3
Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many…
Untargeted Adversarial Attack on Knowledge Graph Embeddings
Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru +3
Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations…
A Language Model based Framework for New Concept Placement in Ontologies
Hang Dong, Jiaoyan Chen, Yuan He +2
We investigate the task of inserting new concepts extracted from texts into an ontology using language models. We explore an approach with three steps: edge search which is to find…
Language Models as Hierarchy Encoders
Yuan He, Zhangdie Yuan, Jiaoyan Chen +1
Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs). While previous research has implicitly leveraged these hierarchies to…
Exploring Large Language Models for Ontology Alignment
Yuan He, Jiaoyan Chen, Hang Dong +1
This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equiva…
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities
Jiaoyan Chen, Hang Dong, Janna Hastings +6
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines.…