.png){kind=icon}
返回
A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2021.3073726.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Attribution extraction refers to find the attributes for the instances of a given semantic class, which is essential to enhance the schema of a knowledge graph. To facilitate the attribution extraction from the query log, this article proposes a pattern driven graph ranking approach to jointly employ the pattern and context distribution information. First, a simple pattern on query text is applied to automatically acquire seed attributes. Then, a graph-based weight propagation is designed to rank the patterns by context distribution algorithm information. Experimental results show that, on a Chinese query log collected by Baidu, the automatically acquired seeds are more representative than the classical manually assembled seeds, achieving an improvement of 11.6% in MAP as compared to the baseline approach. And the graph-based ranking algorithm manipulates the two types of evidence more effectively, outperforming both the distributional similarity based baseline and the HITS algorithm by 29.2% and 11.3%, respectively.
Keyword:
Data mining
Semantics
Postal services
Informatics
Manuals
Web search
Urban areas
Distributional similarity
graph-based ranking
pattern driven
semantic class attributes
Web search queries
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.9
论文数:
8.5K
被引数:
6.0W
机构
引用论文
分享收藏Mapping past and recent landscape modifications in the Lagoon of Venice through geophysical surveys and historical maps.png)
Anthropocene
AnthropoceneIF0
PREAI
分享收藏
查看更多内容