Papers by Li Aodi

1 papers
Neural-based Mixture Probabilistic Query Embedding for Answering FOL queries on Knowledge Graphs (2022.emnlp-main)

Copied to clipboard

Challenge: Existing methods to embed entities and first-order logical queries in a vector space are often violated in real applications and limit their performance.
Approach: They propose a Neural-based Mixture Probabilistic Query Embedding Model that embeds entities and first-order logical queries in a vector space.
Outcome: The proposed model outperforms state-of-the-art methods on benchmark datasets.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations