图 书 馆
从RDF数据学习关系贝叶斯分类器Learning relational bayesian classifiers from RDF data |
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| 课程网址: | http://videolectures.net/iswc2011_lin_rdfdata/ |
| 主讲教师: | Harris Lin |
| 开课单位: | 爱荷华州立大学 |
| 开课时间: | 2011-11-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The increasing availability of large RDF datasets o ffers an exciting opportunity to use such data to build predictive models using machine learning algorithms. However, the massive size and distributed nature of RDF data calls for approaches to learning from RDF data in a setting where the data can be accessed only through a query interface, e.g., the SPARQL endpoint of the RDF store. In applications where the data are subject to frequent updates, there is a need for algorithms that allow the predictive model to be incrementally updated in response to changes in the data. Furthermore, in some applications, the attributes that are relevant for specifi c prediction tasks are not known a priori and hence need to be discovered by the algorithm. We present an approach to learning Relational Bayesian Classiffi ers (RBCs) from RDF data that addresses such scenarios. Specifi cally, we show how to build RBCs from RDF data using statistical queries through the SPARQL endpoint of the RDF store. We compare the communication complexity of our algorithm with one that requires direct centralized access to the data and hence has to retrieve the entire RDF dataset from the remote location for processing. We establish the conditions under which the RBC models can be incrementally updated in response to addition or deletion of RDF data. We show how our approach can be extended to the setting where the attributes that are relevant for prediction are not known a priori, by selectively crawling the RDF data for attributes of interest. We provide open source implementation and evaluate the proposed approach on several large RDF datasets. |
| 关 键 词: | RDF数据集; 机器学习; 贝叶斯分类器 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-05：lxf |
| 阅读次数: | 42 |