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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3073726