Muhao Chen

USC
0000-0003-0118-3147
https://muhaochen.github.io/
Publications60
h-index12
Citations667
Highly Influential Citations96
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  • Publications
  • Influence
Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
TLDR
We propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution for cross-lingual knowledge alignment. Expand
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Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
TLDR
In this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Expand
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Multi-view Knowledge Graph Embedding for Entity Alignment
TLDR
We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Expand
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Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
TLDR
We present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. Expand
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Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
TLDR
We propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. Expand
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TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs
TLDR
In this paper, we propose a novel edge-centric embedding model TransEdge, which contextualizes relation representations in terms of specific head-tail entity pairs. Expand
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Examining Gender Bias in Languages with Grammatical Gender
TLDR
In this paper, we propose new metrics for evaluating gender bias in word embeddings of gendered languages and further demonstrate evidence ofgender bias in bilingual embeddINGS which align these languages with English. Expand
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Embedding Uncertain Knowledge Graphs
TLDR
We propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Expand
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A benchmarking study of embedding-based entity alignment for knowledge graphs
TLDR
We study embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. Expand
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Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
TLDR
We propose a novel two-view KG embedding model, JOIE, with the goal to produce better knowledge embedding and enable new applications that rely on multi-view knowledge. Expand
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