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高精度,高召回率的获取可扩展知识Scalable Knowledge Harvesting with High Precision and High Recall |
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| 课程网址: | http://videolectures.net/wsdm2011_nakashole_skh/ |
| 主讲教师: | Ndapandula Nakashole |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2011-08-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Harvesting relational facts from Web sources has received great attention for automatically constructing large knowledge bases. Stateof-the-art approaches combine pattern-based gathering of fact candidates with constraint-based reasoning. However, they still face major challenges regarding the trade-offs between precision, recall, and scalability. Techniques that scale well are susceptible to noisy patterns that degrade precision, while techniques that employ deep reasoning for high precision cannot cope with Web-scale data. This paper presents a scalable system, called PROSPERA, for high-quality knowledge harvesting. We propose a new notion of ngram-itemsets for richer patterns, and use MaxSat-based constraint reasoning on both the quality of patterns and the validity of fact candidates.We compute pattern-occurrence statistics for two benefits: they serve to prune the hypotheses space and to derive informative weights of clauses for the reasoner. The paper shows how to incorporate these building blocks into a scalable architecture that can parallelize all phases on a Hadoop-based distributed platform. Our experiments with the ClueWeb09 corpus include comparisons to the recent ReadTheWeb experiment. We substantially outperform these prior results in terms of recall, with the same precision, while having low run-times. |
| 关 键 词: | 知识库; 资源; 计算机科学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-09-24：dingaq |
| 阅读次数: | 93 |