Embedding Uncertain Temporal Knowledge Graphs

Tongxin Li, Weiping Wang, Xiaobo Li, Tao Wang (), Xin Zhou and Meigen Huang
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Tongxin Li: School of Systems Engineering, National University of Defense Technology, Changsha 410000, China
Weiping Wang: School of Systems Engineering, National University of Defense Technology, Changsha 410000, China
Xiaobo Li: School of Systems Engineering, National University of Defense Technology, Changsha 410000, China
Tao Wang: School of Systems Engineering, National University of Defense Technology, Changsha 410000, China
Xin Zhou: School of Systems Engineering, National University of Defense Technology, Changsha 410000, China
Meigen Huang: School of Systems Engineering, National University of Defense Technology, Changsha 410000, China

Mathematics, 2023, vol. 11, issue 3, 1-16

Abstract: Knowledge graph (KG) embedding for predicting missing relation facts in incomplete knowledge graphs (KGs) has been widely explored. In addition to the benchmark triple structural information such as head entities, tail entities, and the relations between them, there is a large amount of uncertain and temporal information, which is difficult to be exploited in KG embeddings, and there are some embedding models specifically for uncertain KGs and temporal KGs. However, these models either only utilize uncertain information or only temporal information, without integrating both kinds of information into the underlying model that utilizes triple structural information. In this paper, we propose an embedding model for uncertain temporal KGs called the confidence score, time, and ranking information embedded jointly model (CTRIEJ), which aims to preserve the uncertainty, temporal and structural information of relation facts in the embedding space. To further enhance the precision of the CTRIEJ model, we also introduce a self-adversarial negative sampling technique to generate negative samples. We use the embedding vectors obtained from our model to complete the missing relation facts and predict their corresponding confidence scores. Experiments are conducted on an uncertain temporal KG extracted from Wikidata via three tasks, i.e., confidence prediction, link prediction, and relation fact classification. The CTRIEJ model shows effectiveness in capturing uncertain and temporal knowledge by achieving promising results, and it consistently outperforms baselines on the three downstream experimental tasks.

Keywords: uncertain temporal knowledge graph; temporal knowledge graph; knowledge graph embedding; confidence score (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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