Differentiating Concepts and Instances for Knowledge Graph Embedding

EMNLP 2018  ·  Xin Lv, Lei Hou, Juanzi Li, Zhiyuan Liu ·

Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Link Prediction YAGO39K TransC (bern) Hits@1 0.298 # 1
Hits@10 0.698 # 1
Hits@3 0.502 # 1
MRR 0.42 # 1
Triple Classification YAGO39K TransC (bern) Accuracy 93.8 # 1
F1-Score 93.7 # 1
Precision 94.8 # 1
Recall 92.7 # 1

Methods


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