69 citations · 83 across the 6 of their papers we have counts for
7 papers
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Han Xie, Da Zheng, Jun Ma +9
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be…
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang +6
How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state…
Efficient and effective training of language and graph neural network models
Vassilis N. Ioannidis, Xiang Song, Da Zheng +6
Can we combine heterogenous graph structure with text to learn high-quality semantic and behavioural representations? Graph neural networks (GNN)s encode numerical node attributes…
End-to-End Conversational Search for Online Shopping with Utterance Transfer
Liqiang Xiao, Jun Ma2, Xin Luna Dong +6
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratc…
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Xin Luna Dong, Xiang He, Andrey Kan +19
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question a…
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Giannis Karamanolakis, Jun Ma, Xin Luna Dong
Extracting structured knowledge from product profiles is crucial for various applications in e-Commerce. State-of-the-art approaches for knowledge extraction were each designed for…