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A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph
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DOI：10.1109/TII.2021.3073726.png)
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Abstract
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.
Keywords:
Data mining
Semantics
Postal services
Informatics
Manuals
Web search
Urban areas
Distributional similarity
graph-based ranking
pattern driven
semantic class attributes
Web search queries
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