4 citations · 11 across the 12 of their papers we have counts for
12 papers
Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables
Weizhi Fei, Hang Yin, Zihao Wang +3
Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential first-order queries with $k…
LegalCiteBench: Evaluating Citation Reliability in Legal Language Models
Sijia Chen, Hang Yin, Shunfan Zhou
Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedents can cause serious profession…
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…
Top Ten Challenges Towards Agentic Neural Graph Databases
Jiaxin Bai, Zihao Wang, Yukun Zhou +16
Graph databases (GDBs) like Neo4j and TigerGraph excel at handling interconnected data but lack advanced inference capabilities. Neural Graph Databases (NGDBs) address this by inte…
Enhancing Transformers for Generalizable First-Order Logical Entailment
Tianshi Zheng, Jiazheng Wang, Zihao Wang +5
Transformers, as the fundamental deep learning architecture, have demonstrated great capability in reasoning. This paper studies the generalizable first-order logical reasoning abi…
Meta Operator for Complex Query Answering on Knowledge Graphs
Hang Yin, Zihao Wang, Yangqiu Song
Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (…
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…
-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Hang Yin, Zihao Wang, Weizhi Fei +1
To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential becaus…
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport
Zihao Wang, Weizhi Fei, Hang Yin +3
Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-b…
Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors
Hang Yin, Zihao Wang, Yangqiu Song
Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one. Particularly, answering complex queries based on first-orde…
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
Zihao Wang, Hang Yin, Yangqiu Song
Complex Query Answering (CQA) is an important reasoning task on knowledge graphs. Current CQA learning models have been shown to be able to generalize from atomic operators to more…