图 书 馆
使用图卷积网络建模关系数据Modeling Relational Data with Graph Convolutional Networks |
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| 课程网址: | http://videolectures.net/eswc2018_kipf_convolutional_networks/ |
| 主讲教师: | Thomas Kipf |
| 开课单位: | 阿姆斯特丹大学 |
| 开课时间: | 2018-07-10 |
| 课程语种: | 英语 |
| 中文简介: |  知识图支持各种应用，包括问题解答和信息检索。尽管在创建和维护上投入了大量精力，但即使是最大的游戏（例如Yago，DBPedia或Wikidata）也仍然不完整。我们介绍了关系图卷积网络（R GCN），并将其应用于两个标准的知识库完成任务：链接预测（丢失事实的恢复，即主题谓词对象三元组）和实体分类（丢失实体属性的恢复）。 R GCN与最近一类在图形上运行的神经网络有关，并且专门为处理现实知识库的高度多关系数据特性而开发。我们证明了R GCN作为实体分类的独立模型的有效性。我们进一步表明，通过使用R GCN编码器模型在图中的多个推断步骤中积累证据，可以显着改善链路预测的因数分解模型（例如DistMult），这表明FB15k 237的解码器仅比解码器大29.8％。基线。 |
| 课程简介: | Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We introduce Relational Graph Convolutional Networks (R-GCNs) and apply them to two standard knowledge base completion tasks: Link prediction (recovery of missing facts, i.e. subject-predicate-object triples) and entity classification (recovery of missing entity attributes). R-GCNs are related to a recent class of neural networks operating on graphs, and are developed specifically to handle the highly multi-relational data characteristic of realistic knowledge bases. We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification. We further show that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline. |
| 关 键 词: | 图卷积网络; 链接预测; 建模 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-12-09：cjy |
| 最后编审: | 2020-12-09：cjy |
| 阅读次数: | 20 |