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基于规则和嵌入的知识图完成系统的细粒度评估
Fine-grained Evaluation of Rule- and Embedding-based Systems for Knowledge Graph Completion |
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| 课程网址: |
http://videolectures.net/iswc2018_fink_fine_grained_completion/
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| 主讲教师: |
Manuel Fink
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| 开课单位: |
曼海姆大学
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| 开课时间: |
2018-10-23
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| 课程语种: |
英语
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| 中文简介: |
近年来，嵌入作为一种完成知识图的方法，吸引了越来越多的研究关注。类似地，基于规则的系统在过去也被研究过。到目前为止，现有工作中缺少的是一种包括多种方法的通用评估。我们通过以常用的评估格式比较两种系统的代表来缩小这一差距。利用基于规则的系统的解释性，我们提出了一个细粒度的评估场景，该场景深入了解了最流行的数据集的特征，并指出了所研究方法的不同优势和缺点。我们的结果表明，TransE、RESCAL或HolE等模型在解决某些类型的完成任务时存在问题，这些任务可以通过基于规则的方法以高精度解决。同时，还有其他基于规则的系统难以完成的任务。受这些见解的启发，我们通过集成学习将这两种方法结合起来。结果支持我们的假设，即两种方法可以以有益的方式相互补充。
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| 课程简介: |
Over the recent years embeddings have attracted increasing research focus as a means for knowledge graph completion. Similarly, rule-based systems have been studied for this task in the past as well. What is missing from existing works so far, is a common evaluation that includes more than one type of method. We close this gap by comparing representatives of both types of systems in a frequently used evaluation format. Leveraging the explanatory qualities of rule-based systems, we present a fine-grained evaluation scenario that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches. Our results show that models such as TransE, RESCAL or HolE have problems in solving certain types of completion tasks that can be solved by a rule-based approach with high precision. At the same time there are other completion tasks that are difficult for rule-based systems. Motivated by these insights we combine both families of approaches via ensemble learning. The results support our assumption that the two methods can complement each other in a beneficial way.
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| 关 键 词: |
网研究方法的不同优势和缺点; 数据集的特征; RESCAL或HolE等模型
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| 课程来源: |
视频讲座网
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| 数据采集: |
2022-12-30：cyh
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| 最后编审: |
2023-05-15：cyh
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| 阅读次数: |
43
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