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An Overview of Microsoft Academic Service (MAS) and Applications
Abstract
In this paper we describe a new release of a Web scale entity graph that serves as the backbone of Microsoft Academic Service (MAS), a major production effort with a broadened scope to the namesake vertical search engine that has been publicly available since 2008 as a research prototype. At the core of MAS is a heterogeneous entity graph comprised of six types of entities that model the scholarly activities: field of study, author, institution, paper, venue, and event. In addition to obtaining these entities from the publisher feeds as in the previous effort, we in this version include data mining results from the Web index and an in-house knowledge base from Bing, a major commercial search engine. As a result of the Bing integration, the new MAS graph sees significant increase in size, with fresh information streaming in automatically following their discoveries by the search engine. In addition, the rich entity relations included in the knowledge base provide additional signals to disambiguate and enrich the entities within and beyond the academic domain. The number of papers indexed by MAS, for instance, has grown from low tens of millions to 83 million while maintaining an above 95% accuracy based on test data sets derived from academic activities at Microsoft Research. Based on the data set, we demonstrate two scenarios in this work: a knowledge driven, highly interactive dialog that seamlessly combines reactive search and proactive suggestion experience, and a proactiv…
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In the citing paper
“The Paper Field dataset is built from the Microsoft Academic Graph (Sinha et al., 2015) foot_2 and maps paper titles to one of 7 fields of study.”
From SciBERT: A Pretrained Language Model for Scientific Text (2019) · cites this paper as Sinha et al., 2015
Citations
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No references could be resolved yet.
Research works printed but not linked: 13
- Microsoft, 2013, http://datamarket.azure.com/dataset/mrc/microsoftacademic
- Acharya A., 2014, CoRR
- 10.1007/978-3-540-30192-9_58 · DOI
- 10.1145/2623330.2623623 · DOI · Paper page
- 10.1007/11871637_53 · DOI
- 10.1145/281250.281253 · DOI
- Larivière V., 2014, Are elite journals declining?, JASIST, 65, 649
- 10.1145/2623330.2623630 · DOI
- Shum H., 2014, Microsoft Research Faculty Summit
- Shum H., 2010, Microsoft Research Faculty Summit
- 10.1145/1255175.1255243 · DOI
- 10.1145/1277741.1277868 · DOI
- Wang K., 2014, ACM SIGIR
Cited by
Sorted by citations
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218
Heterogeneous Graph Transformer
Hu, Z., Dong, Y., Wang, K., & Sun, Y. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380027 · Cited by 1,646
Self-citationVisser, M., van Eck, N. J., & Waltman, L. (2021) · Quantitative Science Studies · doi:10.1162/qss_a_00112 · Cited by 865
Over-optimization of academic publishing metrics: observing Goodhart's Law in action
Fire, M., & Guestrin, C. (2019) · GigaScience · doi:10.1093/gigascience/giz053 · Cited by 351
Dimensions: A competitor to Scopus and the Web of Science?
Thelwall, M. (2018) · Journal of Informetrics · doi:10.1016/j.joi.2018.03.006 · Cited by 294
Metadata
- Authors
- Sinha, A., Shen, Z., Song, Y., Ma, H., Eide, D., Hsu, B.-J. (., & Wang, K.
- Affiliations
- Microsoft Research, Redmond, WA, USA
- Venue
- Proceedings of the 24th International Conference on World Wide Web
- Publisher
- ACM
- Published
- 2015-05-18
- Pages
- 243-246
- Cited by
- 658
- References
- 0
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