A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph

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 emplo...

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Vydáno v:IEEE transactions on industrial informatics Ročník 18; číslo 2; s. 1250 - 1259
Hlavní autoři: Yang, Muyun, Chen, Kehai, Sun, Shuqi, Han, Zhongyuan, Kong, Leilei, Meng, Qingye
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Context
Data mining
Distributional similarity
graph-based ranking
Informatics
Knowledge representation
Manuals
pattern driven
Postal services
Queries
Ranking
Seeds
semantic class attributes
Semantics
Urban areas
Web search
Web search queries
ISSN:1551-3203, 1941-0050
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Abstract 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.
AbstractList 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.
Author Kong, Leilei
Chen, Kehai
Han, Zhongyuan
Yang, Muyun
Meng, Qingye
Sun, Shuqi
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SubjectTerms Algorithms
Context
Data mining
Distributional similarity
graph-based ranking
Informatics
Knowledge representation
Manuals
pattern driven
Postal services
Queries
Ranking
Seeds
semantic class attributes
Semantics
Urban areas
Web search
Web search queries
Title A Pattern Driven Graph Ranking Approach to Attribute Extraction for Knowledge Graph
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