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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Published in:	IEEE transactions on industrial informatics Vol. 18; no. 2; pp. 1250 - 1259
Main Authors:	Yang, Muyun, Chen, Kehai, Sun, Shuqi, Han, Zhongyuan, Kong, Leilei, Meng, Qingye
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.02.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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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