Generating networked knowledge based on statistical modeling and data mining techniques and its applications
| Project/Area Number |
20300038
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Media informatics/Database
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| Research Institution | Kobe University |
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Principal Investigator |
EGUCHI Koji 神戸大学, 大学院・システム情報学研究科, 准教授 (50321576)
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| Co-Investigator(Kenkyū-buntansha) |
TAKASU Atsuhiro 国立情報学研究所, コンテンツ科学研究系, 教授 (90216648)
OHKAWA Takenao 神戸大学, 大学院・システム情報学研究科, 教授 (30223738)
尾崎 知伸 神戸大学, 自然科学系先端融合研究環重点研究部, 助教 (40365458)
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| Co-Investigator(Renkei-kenkyūsha) |
OZAKI Tomonobu 大阪大学, サイバーメディアセンター, 特任講師 (40365458)
UNO Takeaki 国立情報学研究所, 情報学プリンシプル研究系, 准教授 (00302977)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥14,430,000 (Direct Cost: ¥11,100,000、Indirect Cost: ¥3,330,000)
Fiscal Year 2010: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2009: ¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2008: ¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
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| Keywords | 統計モデリング / データマイニング / トピックモデル / ネットワーク分析 / グラフマイニング / 情報検索 / 確率的トピックモデル / 確率的言語モデル / 適合モデル / 複雑ネットワーク分析 / 確率的生成モデル |
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| Research Abstract |
We aim to construct networked knowledge from scattered pieces of information using techniques of statistical modeling and data mining, and apply that knowledge to solve problems in human intellectual activities. To achieve these objectives, we mainly carried out the following research tasks:
(1) Research on extracting relational structures from text data using topic models : We statistically estimated latent topics from some sorts of structured text data, and used the latent topics in some applications, such as extracting relational structures between entities or between data.
(2) Research on inferring node clusters from network data and its applications : We statistically estimated latent communities from a network, and applied the estimated communities to real-world problems, such as link prediction.
(3) Research on discovering patterns from network data : We developed algorithms to discover characteristic patterns from complex structured data, such as a single network in which nodes or edges are associated with a set of numerical attributes.
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Report
(5 results)
Research Products
(73 results)
