scholarly journals Benchmarking Neural Embeddings for Link Prediction in Knowledge Graphs Under Semantic and Structural Changes

2021 ◽  
Author(s):  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Structural Changes ◽  
Knowledge Graphs

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Trans4E: Link Prediction on Scholarly Knowledge Graphs

2021 ◽  
Author(s):  
Mojtaba Nayyeri ◽  
Gokce Muge Cil ◽  
Sahar Vahdati ◽  
Francesco Osborne ◽  
Mahfuzur Rahman ◽  
...  
Keyword(s):  
Link Prediction ◽  
Knowledge Graphs

scholarly journals Efficient energy-based embedding models for link prediction in knowledge graphs

2016 ◽  
Vol 47 (1) ◽  
pp. 91-109 ◽  
Author(s):  
Pasquale Minervini ◽  
Claudia d’Amato ◽  
Nicola Fanizzi
Keyword(s):  
Link Prediction ◽  
Efficient Energy ◽  
Knowledge Graphs

scholarly journals Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction

2020 ◽  
Vol 34 (03) ◽  
pp. 3065-3072 ◽  
Author(s):  
Zhanqiu Zhang ◽  
Jianyu Cai ◽  
Yongdong Zhang ◽  
Jie Wang
Keyword(s):  
Coordinate System ◽  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Polar Coordinate System ◽  
Radial Coordinate ◽  
Polar Coordinate ◽  
Benchmark Datasets ◽  
Knowledge Graphs ◽  
Semantic Hierarchies

Knowledge graph embedding, which aims to represent entities and relations as low dimensional vectors (or matrices, tensors, etc.), has been shown to be a powerful technique for predicting missing links in knowledge graphs. Existing knowledge graph embedding models mainly focus on modeling relation patterns such as symmetry/antisymmetry, inversion, and composition. However, many existing approaches fail to model semantic hierarchies, which are common in real-world applications. To address this challenge, we propose a novel knowledge graph embedding model—namely, Hierarchy-Aware Knowledge Graph Embedding (HAKE)—which maps entities into the polar coordinate system. HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy. Specifically, the radial coordinate aims to model entities at different levels of the hierarchy, and entities with smaller radii are expected to be at higher levels; the angular coordinate aims to distinguish entities at the same level of the hierarchy, and these entities are expected to have roughly the same radii but different angles. Experiments demonstrate that HAKE can effectively model the semantic hierarchies in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.

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