4 citations · 9 across the 5 of their papers we have counts for
9 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 …
TransNet: Transfer Knowledge for Few-shot Knowledge Graph Completion
Lihui Liu, Zihao Wang, Dawei Zhou +6
Knowledge graphs (KGs) are ubiquitous and widely used in various applications. However, most real-world knowledge graphs are incomplete, which significantly degrades their performa…
TUCKET: A Tensor Time Series Data Structure for Efficient and Accurate Factor Analysis over Time Ranges
Ruizhong Qiu, Jun-Gi Jang, Xiao Lin +2
Tucker decomposition has been widely used in a variety of applications to obtain latent factors of tensor data. In these applications, a common need is to compute Tucker decomposit…
Neural-Symbolic Reasoning over Knowledge Graphs: A Survey from a Query Perspective
Lihui Liu, Zihao Wang, Hanghang Tong
Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive…
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…
Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Lihui Liu, Zihao Wang, Ruizhong Qiu +5
Despite the superb performance in many tasks, large language models (LLMs) bear the risk of generating hallucination or even wrong answers when confronted with tasks that demand th…