图 书 馆
知识图识别Knowledge Graph Identification |
|
| 课程网址: | http://videolectures.net/iswc2013_pujara_graph_identification/ |
| 主讲教师: | Jay Pujara |
| 开课单位: | 马里兰大学 |
| 开课时间: | 2013-11-28 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Large-scale information processing systems are able to extract massive collections of interrelated facts, but unfortunately transforming these candidate facts into useful knowledge is a formidable challenge. In this paper, we show how uncertain extractions about entities and their relations can be transformed into a knowledge graph. The extractions form an extraction graph and we refer to the task of removing noise, inferring missing information, and determining which candidate facts should be included into a knowledge graph as knowledge graph identification. In order to perform this task, we must reason jointly about candidate facts and their associated extraction confidences, identify co-referent entities, and incorporate ontological constraints. Our proposed approach uses probabilistic soft logic (PSL), a recently introduced probabilistic modeling framework which easily scales to millions of facts. We demonstrate the power of our method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL project containing over 1M extractions and 70K ontological relations. We show that compared to existing methods, our approach is able to achieve improved AUC and F1 with significantly lower running time. |
| 关 键 词: | 概率建模; 图识别 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-18：zyk |
| 最后编审: | 2020-11-18：zyk |
| 阅读次数: | 49 |