29 citations · 286 across the 54 of their papers we have counts for
7 papers · 1 filter
An Industry Evaluation of Embedding-based Entity Alignment
Ziheng Zhang, Jiaoyan Chen, Xi Chen +4
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of u…
OWL2Vec*: Embedding of OWL Ontologies
Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz +3
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and…
Ontology-guided Semantic Composition for Zero-Shot Learning
Jiaoyan Chen, Freddy Lecue, Yuxia Geng +2
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relatio…
Generative Adversarial Zero-shot Learning via Knowledge Graphs
Yuxia Geng, Jiaoyan Chen, Zhuo Chen +4
Zero-shot learning (ZSL) is to handle the prediction of those unseen classes that have no labeled training data. Recently, generative methods like Generative Adversarial Networks (…
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Ernesto Jiménez-Ruiz, Asan Agibetov, Jiaoyan Chen +2
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic…
An Internal Clock Based Space-time Neural Network for Motion Speed Recognition
Junwen Luo, Jiaoyan Chen
In this work we present a novel internal clock based space-time neural network for motion speed recognition. The developed system has a spike train encoder, a Spiking Neural Networ…
Correcting Knowledge Base Assertions
Jiaoyan Chen, Xi Chen, Ian Horrocks +2
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues. One common issue is the presence of erroneous assertions, often caused by lexical or seman…