Representation learning from multiple heterogeneous graph using scholarly big data
| Project/Area Number |
17K00427
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Web informatics, Service informatics
|
|---|
| Research Institution | The University of Tokyo |
|---|
Principal Investigator |
Mori Junichiro 東京大学, 大学院情報理工学系研究科, 准教授 (30508924)
|
|---|
| Project Period (FY) |
2017-04-01 – 2021-03-31
|
| Project Status |
Completed (Fiscal Year 2020)
|
| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
|---|
| Keywords | 学術文献データ / 引用ネットワーク / 表現学習 |
|---|
| Outline of Final Research Achievements |
In this study, we propose the method for representation learning from multiple heterogeneous network data in order to support the extraction and discovery of useful knowledge from large-scale scholarly data. We develop the system for large-scale scholarly data analysis. In particular, we conducted citation network analysis on a large dataset of academic literature on COVID-19 using the Academic Industry and Technology Overview System, which is one of our research results, and extracted information on scientific evidence and important technologies related to COVID-19. The results of the analysis have been widely available to the public in order to support evidence-based approaches to COVID-19. The results of these studies have been published in several international conferences and journals.
|
| Academic Significance and Societal Importance of the Research Achievements |
本研究では、大規模な論文データから生成される複数の異種ネットワークから適切な分散表現学習をする手法の知見を明らかにした。また、学習されたネットワーク分散表現を論文データ分析における複数のタスクに適用しその有効性を明らかにした。その上で、実際に大規模論文データ分析のシステム構築を行い、政策立案者、研究者、データベースプロバイダなど科学技術の複数のステークホルダの視点から、ネットワークデータを大規模な学術文献情報からの知識発見に利活用するための知見を明らかにした。
|
Report
(5 results)
Research Products
(27 results)
-
-
-
-
-
[Journal Article] Using Sequence Constraints for Modelling Network Interactions2019
Author(s)
Johannes De Smedt, Junichiro Mori, and Masanao Ochi
-
Journal Title
Advances in Artificial Intelligence: Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence (JSAI 2019)
Volume: 1128
Pages: 3-13
DOI
NAID
ISBN
9783030398774, 9783030398781
Related Report
Peer Reviewed / Int'l Joint Research
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
