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使用MapReduce的大规模模糊物pD*推理
Large scale fuzzy pD* Reasonig using map reduce |
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| 课程网址: |
http://videolectures.net/iswc2011_liu_map/
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| 主讲教师: |
Chang Liu
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| 开课单位: |
上海交通大学
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| 开课时间: |
2011-11-25
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| 课程语种: |
英语
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| 中文简介: |
mapreduce 框架已被证明对数据密集型任务非常有效。早期的工作试图使用 mapreduce 进行大规模的 pd 推理 ∗ 语义, 并显示出很有希望的结果。本文向前迈出了一步, 考虑模糊 pd ∗ 语义下语义数据的可扩展推理 (即基于模糊的 owl ∗ 语义的扩展)。据我们所知, 这是首次研究 mapreduce 如何帮助解决模糊 owl 推理的可伸缩性问题的工作。虽然现有的 mapreduce 框架使用的 pd ∗ 语义的大多数优化也适用于模糊 pd ∗ 语义, 但当我们处理模糊信息时, 会出现独特的挑战。我们确定这些关键挑战, 并提出应对其中每一个挑战的解决方案。此外, 我们还实现了一个用于评估目的的原型系统。实验结果表明, 我们系统的运行时间与 webpie 相当, webpie 是 pd ∗ 语义中最先进的可扩展推理引擎。
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| 课程简介: |
The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD∗ semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic data under fuzzy pD∗ semantics (i.e., an extension of OWL pD∗ semantics with fuzzy vagueness). To the best of our knowledge, this is the first work to investigate how MapReduce can help to solve the scalability issue of fuzzy OWL reasoning. While most of the optimizations used by the existing MapReduce framework for pD∗ semantics are also applicable for fuzzy pD∗ semantics, unique challenges arise when we handle the fuzzy information. We identify these key challenges, and propose a solution for tackling each of them. Furthermore, we implement a prototype system for the evaluation purpose. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD∗ semantics.
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| 关 键 词: |
计算机科学; 模糊逻辑; 数据密集型
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-13：邬启凡(课程编辑志愿者)
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| 阅读次数: |
48
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