6 papers
is Theoretically Large Enough for Embedding-based Top- Retrieval
Zihao Wang, Hang Yin, Lihui Liu +4
This paper studies the Minimal Embeddable Dimension (MED): the least dimension in which there exists a configuration of object vectors so that every subset of size at most …
Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering
Weizhi Fei, Zihao Wang, hang Yin +3
Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incomplete KGs. While existing neural-s…
Do Reasoning Models Enhance Embedding Models?
Wun Yu Chan, Shaojin Chen, Huihao Jing +5
State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning…
Extending Complex Logical Queries on Uncertain Knowledge Graphs
Weizhi Fei, Zihao Wang, Hang Yin +2
The study of machine learning-based logical query answering enables reasoning with large-scale and incomplete knowledge graphs. This paper advances this area of research by address…
Transformers for Complex Query Answering over Knowledge Hypergraphs
Hong Ting Tsang, Zihao Wang, Yangqiu Song
Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modali…
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
Yao Xu, Shizhu He, Jiabei Chen +6
To address the issues of insufficient knowledge and hallucination in Large Language Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs (KGs). Howe…