JSON Documents Similarity Measurement Model Based on Tree Structure
WANG Liuping
LU Wenbing
HU Zida
SUN Cheng
ZHANG Jin
Abstract：The similarity measurement of JSON documents is the key to JSON document data mining,text clustering,and in-formation retrieval.The existing research methods used to extract the structure of JSON documents have defects,resulting in low ac-curacy of similarity calculation and unsatisfactory results.Based on the existing tree edit distance algorithm,this paper proposes a similarity measurement model for JSON documents,and optimizes the calculation of edit distance for unordered trees.The experi-mental results show that the model improves the efficiency of JSON document similarity calculation to a certain extent.
Keywords：edit distanceJSON documentstructural similarityinformation extractionsemi-structured data
Publication Date：2025-08-20
Online Publishing Date：2025-12-12(First online date of this platform, not the publication date of the document)
Pages：7( 2133-2139 )