6 citations · 7 across the 6 of their papers we have counts for
6 papers
KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering
Yike Wu, Nan Hu, Guilin Qi +11
Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, parti…
Knowledge Fusion via Bidirectional Information Aggregation
Songlin Zhai, Guilin Qi, Yue Wang +1
Knowledge graphs (KGs) are the cornerstone of the semantic web, offering up-to-date representations of real-world entities and relations. Yet large language models (LLMs) remain la…
Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs
Yu Li, Yi Huang, Guilin Qi +7
Knowledge graphs are widely used in industrial applications, making error detection crucial for ensuring the reliability of downstream applications. Existing error detection method…
Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study
Keyu Wang, Guilin Qi, Jiaqi Li +1
Large language models (LLMs) have shown significant achievements in solving a wide range of tasks. Recently, LLMs' capability to store, retrieve and infer with symbolic knowledge h…
DNG: Taxonomy Expansion by Exploring the Intrinsic Directed Structure on Non-gaussian Space
Songlin Zhai, Weiqing Wang, Yuanfang Li +1
Taxonomy expansion is the process of incorporating a large number of additional nodes (i.e., "queries") into an existing taxonomy (i.e., "seed"), with the most important step being…
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Guibing Guo, Songlin Zhai, Fajie Yuan +2
Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i.e., the gap between images…