OAG: Toward Linking Large-scale Heterogeneous Entity Graphs

By Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li and 1 more. 2019

133 citations recorded by the source corpus.

Linking entities from different sources is a fundamental task in building open knowledge graphs. Despite much research conducted in related fields, the challenges of linkinglarge-scale heterogeneous entity graphs are far from resolved. Employing two billion-scale academic entity graphs (Microsoft Academic Graph and AMiner) as sources for our study, we propose a unified framework --- LinKG --- to address the problem of building a large-scale linked entity graph. LinKG is coupled with three linking modules, each of which addresses one category of entities. To link word-sequence-based entities (e.g., venues), we present a long short-term memory network-based method for capturing the dependencies. To link large-scale entities (e.g., papers), we leverage locality-sensitive hashing and convolutional neural networks for scalable and precise linking. To link entities with ambiguity (e.g., authors), we propose heterogeneous graph attention networks to model different types of entities. Our extensive experiments and systematical analysis demonstrate that LinKG can achieve linking accuracy with an F1-score of 0.9510, significantly outperforming the state-of-the-art. LinKG has been deployed to Microsoft Academic Search and AMiner to integrate the two large graphs. We have published the linked results---the Open Academic Graph (OAG)\footnote\urlhttps://www.openacademic.ai/oag/ , making it the largest publicly available heterogeneous academic graph to date.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiyuan Liu
2. Jie Tang
3. Minlie Huang
4. Yuxiao Dong
5. Xipeng Qiu
6. Maosong Sun
7. Wayne Xin Zhao
8. Xiao Liu

In Management Science and Operations Research

1. Minlie Huang
2. Zhiyuan Liu
3. Xipeng Qiu
4. Jun Zhu
5. Ming Ding
6. Ji-rong Wen
7. Qin Jin
8. Zhiwu Lu

Citations in the corpus

• Pre-Trained Models: Past, Present and Future (2021, background): “An interesting attempt is OAG-BERT (Liu et al., 2021a), which integrates heterogeneous structural knowledge in the open academic graph (OAG) (Zhang et al., 2019a), which covers 0.7 billion heterogeneous entities and 2 billion relations.”
• CodeS: Towards Building Open-source Language Models for Text-to-SQL (2024, unknown): “We follow the same procedure as Bank-Financials and obtain a training set with 5,427 samples and a test set with 97 samples.”
• GPT-GNN: Generative Pre-Training of Graph Neural Networks (2020, methodology): “ous graphs, we use the Reddit dataset [12] and the paper citation network extracted from OAG. All datasets are publicly available and the details can be found in Appendix A. Open Academic Graph (OAG) [34, 38, 44] contains more than 178 million nodes and 2.236 billion edges. It is the largest publicl”
• Self-Supervised Representation Learning: Introduction, advances, and challenges (2021, unknown): “Like in other modalities, we have seen increasingly large graphs being used for pretraining, like the Amazon Review Recommendation data [71] with 113 million nodes or the Open Academic Graph [70], which consists of more than 178 million nodes and 2 billion edges.”
• Heterogeneous Graph Transformer (2020, methodology): “For example, the Open Academic Graph (OAG) [28] in Figure 1 contains five types of nodes: papers, authors, institutions, venues (journal, conference, or preprint), and fields, as well as different types of relationships between them.”
• OAG-BERT: Pre-train Heterogeneous Entity-augmented Academic Language Models (2021, background): “For example, OAG [47] is the largest publicly available heterogeneous academic entity graph containing more than 700 million entities, including paper, author, field-of-study, venue and affiliation; and 2 billion relationships.”
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (2020, methodology): “le a subgraph centered at each vertex and apply GCC on it. Then the obtained representation is fed into an output layer to predict the node label. As for datasets, we adopt US-Airport [43] and Hindex [63]. US-Airport consists of the airline activity data among 1,190 airports. The 4 classes indicate ”