@inproceedings{lv-etal-2018-differentiating,
title = "Differentiating Concepts and Instances for Knowledge Graph Embedding",
author = "Lv, Xin and
Hou, Lei and
Li, Juanzi and
Liu, Zhiyuan",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D18-1222/",
doi = "10.18653/v1/D18-1222",
pages = "1971--1979",
abstract = "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 \url{https://github.com/davidlvxin/TransC}."
}
Markdown (Informal)
[Differentiating Concepts and Instances for Knowledge Graph Embedding](https://preview.aclanthology.org/jlcl-multiple-ingestion/D18-1222/) (Lv et al., EMNLP 2018)
ACL