activity
20192023
most citedInteraction Embeddings for Prediction and Explanation in Knowledge Graphs

150 citations · 193 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.AI20231 cited

A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning

Long Jin, Zhen Yao, Mingyang Chen +2

Knowledge Graph Embedding (KGE) has proven to be an effective approach to solving the Knowledge Graph Completion (KGC) task. Relational patterns which refer to relations with speci…

cs.AI20226 cited

Neural-Symbolic Entangled Framework for Complex Query Answering

Zezhong Xu, Wen Zhang, Peng Ye +2

Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query…

cs.AI2022

PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application

Wen Zhang, Chi-Man Wong, Ganqinag Ye +4

In recent years, knowledge graphs have been widely applied as a uniform way to organize data and have enhanced many tasks requiring knowledge. In online shopping platform Taobao, w…

cs.AI201921 cited

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Wen Zhang, Bibek Paudel, Liang Wang +5

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings c…

cs.AI2019150 cited

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

Wen Zhang, Bibek Paudel, Wei Zhang +2

Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions --- bi-di…