13 citations · 35 across the 15 of their papers we have counts for
6 papers · 1 filter
LLM-Based Knowledge Graph Completion Combining Discrete Structural Coding with Similar Entity Information
Jiaqi Wang, Dongying Lin, Yang Yang +3
Knowledge graph completion requires models to use both textual descriptions and relational structure. Existing LLM-based methods either encode KG structure as discrete tokens or re…
Enhancing LLMs in Predictive Political QA with Semi-Structured Data
Yinan Liu, Zihan Zhou, Zichun Jin +3
Predictive political question answering (QA), such as predicting how a political actor will vote, goes beyond factual lookup. External political resources offer rich historical evi…
Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling
Yinan Liu, Wenjin Xu, Zhiyuan Zha +2
Knowledge graphs (KGs) have become the core backbone of numerous downstream tasks such as question answering and recommender systems. However, despite all this, KGs are often very…
Joint Knowledge Base Completion and Question Answering by Combining Large Language Models and Small Language Models
Yinan Liu, Dongying Lin, Sigang Luo +2
Knowledge Bases (KBs) play a key role in various applications. As two representative KB-related tasks, knowledge base completion (KBC) and knowledge base question answering (KBQA)…
OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
Dongying Lin, Yinan Liu, Shengwei tang +2
Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge…
Low-resource Personal Attribute Prediction from Conversation
Yinan Liu, Hu Chen, Wei Shen +1
Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extr…