@inproceedings{50c7cec4a2284532bddaac53c46e1ef0,
title = "Sequential Semantic Knowledge Graph Embedding",
abstract = "Knowledge graph embedding is aimed at representing entities and relations of knowledge graph in a low-dimensional continuous vector space. Previous embedding models pay little attention to the sequential semantic information in triples and as a result, may lead to the semantic drift problem. Towards this end, we propose a novel sequential semantic embedding (SeqSemE) model to address this problem in this paper. Firstly, we utilize a sequential language model to capture sequential information of triples and interactions between entities and relations. Secondly, we propose a method of learning two embeddings for each relation to avoid semantic drift. Extensive experiments on link prediction show that our SeqSemE is efficient and effective. It can obtain better performance than previous state-of-the-art embedding models.",
keywords = "Knowledge graph, Knowledge graph embedding, Link prediction",
author = "Shang, \{Yu Ming\} and Heyan Huang and Yan Yuan",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Autonomous Unmanned Systems, ICAUS 2021 ; Conference date: 24-09-2021 Through 26-09-2021",
year = "2022",
doi = "10.1007/978-981-16-9492-9\_153",
language = "English",
isbn = "9789811694912",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1547--1557",
editor = "Meiping Wu and Yifeng Niu and Mancang Gu and Jin Cheng",
booktitle = "Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021",
address = "Germany",
}