RelaGraph: Improving embedding on small-scale sparse knowledge graphs by neighborhood relations |
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| Institution: | 1. School of Information Management, Nanjing University, Nanjing 210023, China;2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China;1. College of Artificial Intelligence, Beijing Information Technology College, Beijing, 100018, China;2. College of Engineering and IT University of Dubai, UAE;3. Independent Researcher, USA;4. Department of Computer Science, College of Computer and Information Sciences, Majmaah University. Al-Majmaah, 11952, Saudi Arabia;5. Department of Electrical Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia;1. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Department of Economics, University of Reading, Reading RG6 6UD, UK;1. School of Information Management, Nanjing University, Nanjing 210023, PR China;2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210093, PR China;3. Science, Mathematics and Technology, Singapore University of Technology and Design, 487372, Singapore;1. School of Economics and Management, Xidian University, Xian, China;2. School of Computer Science, Shaanxi Normal University, Xi''an, China;3. School of Computer Science, Northwestern Polytechnical University, Xi''an, China |
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| Abstract: | Learning a continuous dense low-dimensional representation of knowledge graphs (KGs), known as knowledge graph embedding (KGE), has been viewed as the key to intelligent reasoning for deep learning (DL) and gained much attention in recent years. To address the problem that the current KGE models are generally ineffective on small-scale sparse datasets, we propose a novel method RelaGraph to improve the representation of entities and relations in KGs by introducing neighborhood relations. RelaGraph extends the neighborhood information during entity encoding, and adds the neighborhood relations to mine deeper level of graph structure information, so as to make up for the shortage of information in the generated subgraph. This method can well represent KG components in a vector space in a way that captures the structure of the graph, avoiding underlearning or overfitting. KGE based on RelaGraph is evaluated on a small-scale sparse graph KMHEO, and the MRR reached 0.49, which is 34 percentage points higher than that of the SOTA methods, as well as it does on several other datasets. Additionally, the vectors learned by RelaGraph is used to introduce DL into several KG-related downstream tasks, which achieved excellent results, verifying the superiority of KGE-based methods. |
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