6 citations · 7 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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.…