A path-based relation networks model for knowledge graph completion. (15th November 2021)

• Record Type: Journal Article
• Authors: Lee, Wan-Kon; Shin, Won-Chul; Jagvaral, Batselem; Roh, Jae-Seung; Kim, Min-Sung; Lee, Min-Ho; Park, Hyun-Kyu; Park, Young-Tack
• Is Part Of: Expert systems with applications. Volume 182(2021)
• Publication Date: 2021-11-15
• Subjects: Knowledge graph completion -- Relation network -- Link prediction -- Triple classification; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115273
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: To solve the incompleteness of the knowledge graph, the completion of the knowledge graph is required. The various paths of the knowledge graph are valuable information that can infer missing knowledge. It is possible to generate training data that can learning a model based on extracted paths. The relation network enables relational learning between paths and shows competitive performance. Abstract: We consider the problem of learning and inference in a large-scale knowledge graph containing incomplete knowledge. We show that a simple neural network module for relational reasoning through the path extracted from the knowledge base can be used to reliably infer new facts for the missing link. In our work, we used path ranking algorithm to extract the relation path from knowledge graph and use it to build train data. In order to learn the characteristics of relation, a detour path between nodes was created as training data using the extracted relation path. Using this, we trained a model that can predict whether a given triple (Head entity, relation, tail entity) is valid or not. Experiments show that our model obtains better link prediction, relation prediction and triple classification results than previous state-of-the-art models on benchmark datasets WN18RR, FB15k-237, WN11 and FB13.