Microsoft Academic Graph: When experts are not enough

By Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, Anshul Kanakia. Quantitative Science Studies, 2020

697 citations recorded by the source corpus.

Abstract An ongoing project explores the extent to which artificial intelligence (AI), specifically in the areas of natural language processing and semantic reasoning, can be exploited to facilitate the studies of science by deploying software agents equipped with natural language understanding capabilities to read scholarly publications on the web. The knowledge extracted by these AI agents is organized into a heterogeneous graph, called Microsoft Academic Graph (MAG), where the nodes and the edges represent the entities engaging in scholarly communications and the relationships among them, respectively. The frequently updated data set and a few software tools central to the underlying AI components are distributed under an open data license for research and commercial applications. This paper describes the design, schema, and technical and business motivations behind MAG and elaborates how MAG can be used in analytics, search, and recommendation scenarios. How AI plays an important role in avoiding various biases and human induced errors in other data sets and how the technologies can be further improved in the future are also discussed.

Subfields: Artificial Intelligence, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/ea9a516d5cb0b298f0df50e82b3e0400b72fcdff

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

In Information Systems and Management

Citations in the corpus

Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research (2021, methodology): “To study the distribution of widely-utilized datasets across institutions, we linked all dataset-introducing papers to the Microsoft Academic Graph (MAG) [34].”
Large Language Models on Graphs: A Comprehensive Survey (2023, background): “In the academic domain, graphs [12] are constructed with papers as nodes and their relations ( e.g. , citation, authorship, etc) as edges.”
GPT-GNN: Generative Pre-Training of Graph Neural Networks (2020, background): “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.”
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods (2021, neutral): “We originally downloaded the data from the Open Graph Benchmark [31], and the data is a subset of the Microsoft Academic Graph [70] • snap-patents: The data was originally publically released by NBER in 2001 in a working paper by [27].”
Benchmarking Graph Neural Networks (2023, methodology): “[84] Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia.”
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs (2021, positive): “MAG240M is a heterogeneous academic graph extracted from the Microsoft Academic Graph (MAG) (Wang et al., 2020).”
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision (2022, methodology): “ogbn-arxiv The ogbn-arxiv is a recently proposed large-scale dataset of citation networks (Hu et al., 2020a; Wang et al., 2020).”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, methodology): “…2019) ( cf. Section 5.1), ogbl-collab is an author collaboration network (Wang et al., 2020) ( cf. Section 5.2), ogbl-citation is a paper citation network (Wang et al., 2020) ( cf. Section 5.3), and ogbl-wikikg is a Wikidata knowledge graph (Vrande ˇ ci ´ c & Krötzsch, 2014) ( cf. Section 5.4).”
Training Graph Neural Networks with 1000 Layers (2021, background): “, 2020; 2021) and Microsoft Academic Graph (MAG) (Wang et al., 2020).”
GraphGPT: Graph Instruction Tuning for Large Language Models (2023, methodology): “The OGB-arxiv dataset [13] represents a directed graph that captures the citation network among computer science arXiv papers indexed by MAG [41].”