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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment
TLDR
MTransE, a translation-based model for multilingual knowledge graph embeddings, is proposed to provide a simple and automated solution to achieve cross-lingual knowledge alignment and explore how MTransE preserves the key properties of its monolingual counterpart.
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
TLDR
This work proposes a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner and proposes a relation loss to refine entity representations.
Multi-view Knowledge Graph Embedding for Entity Alignment
TLDR
A novel framework is proposed that unifies multiple views of entities to learn embeddings for entity alignment, and significantly outperforms the state-of-the-art embedding-based entity alignment methods.
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
TLDR
This paper introduces an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions and shows that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches.
A benchmarking study of embedding-based entity alignment for knowledge graphs
TLDR
This paper surveys 23 recent embedding-based entity alignment approaches and categorizes them based on their techniques and characteristics, and proposes a new KG sampling algorithm, with which to generate a set of dedicated benchmark datasets with various heterogeneity and distributions for a realistic evaluation.
Multifaceted protein–protein interaction prediction based on Siamese residual RCNN
TLDR
An end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences.
Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks
TLDR
A new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism, which is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past.
Embedding Uncertain Knowledge Graphs
TLDR
This paper proposes a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space and introduces probabilistic soft logic to infer confidence scores for unseen relation facts during training.
Universal Representation Learning of Knowledge Bases by Jointly Embedding Instances and Ontological Concepts
TLDR
This model is trained on large-scale knowledge bases that consist of massive instances and their corresponding ontological concepts connected via a (small) set of cross-view links and significantly outperforms previous models on instance-view triple prediction task as well as ontology population on ontology-view KG.
Examining Gender Bias in Languages with Grammatical Gender
TLDR
Experiments on modified Word Embedding Association Test, word similarity, word translation, and word pair translation tasks show that the proposed approaches can effectively reduce the gender bias while preserving the utility of the original embeddings.
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