图 书 馆
用于数据挖掘的资源描述框架图嵌入RDF2Vec: RDF Graph Embeddings for Data Mining |
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| 课程网址: | http://videolectures.net/iswc2016_ristoski_rdf_graph/ |
| 主讲教师: | Petar Ristoski |
| 开课单位: | 曼海姆大学商业信息学与数学学院 |
| 开课时间: | 2016-11-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks. |
| 关 键 词: | 数据挖掘; 关联数据; 数值特征; 语言建模 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-04-24：chenxin01 |
| 最后编审: | 2023-05-18：chenxin01 |
| 阅读次数: | 24 |