Edge-level multi-constraint graph pattern matching with lung cancer knowledge graph
Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data re...
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| Published in | Frontiers in big data Vol. 8; p. 1546850 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
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Frontiers Media S.A
10.02.2025
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| ISSN | 2624-909X 2624-909X |
| DOI | 10.3389/fdata.2025.1546850 |
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| Abstract | Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data research.
In order to overcome this limitation, based on the existing research on GPM with the lung cancer knowledge graph, this paper introduces the Monte Carlo method and proposes an edge-level multi-constraint graph pattern matching algorithm TEM with lung cancer knowledge graph. Furthermore, we apply Monte Carlo method to both nodes and edges, and propose a multi-constraint hologram pattern matching algorithm THM with lung cancer knowledge graph.
The experiments have verified the effectiveness and efficiency of TEM algorithm.
This method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency. |
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| AbstractList | Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data research.
In order to overcome this limitation, based on the existing research on GPM with the lung cancer knowledge graph, this paper introduces the Monte Carlo method and proposes an edge-level multi-constraint graph pattern matching algorithm TEM with lung cancer knowledge graph. Furthermore, we apply Monte Carlo method to both nodes and edges, and propose a multi-constraint hologram pattern matching algorithm THM with lung cancer knowledge graph.
The experiments have verified the effectiveness and efficiency of TEM algorithm.
This method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency. Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data research.IntroductionTraditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability to return subgraphs quickly and accurately, these methods are limited to their applications without medical data research.In order to overcome this limitation, based on the existing research on GPM with the lung cancer knowledge graph, this paper introduces the Monte Carlo method and proposes an edge-level multi-constraint graph pattern matching algorithm TEM with lung cancer knowledge graph. Furthermore, we apply Monte Carlo method to both nodes and edges, and propose a multi-constraint hologram pattern matching algorithm THM with lung cancer knowledge graph.MethodsIn order to overcome this limitation, based on the existing research on GPM with the lung cancer knowledge graph, this paper introduces the Monte Carlo method and proposes an edge-level multi-constraint graph pattern matching algorithm TEM with lung cancer knowledge graph. Furthermore, we apply Monte Carlo method to both nodes and edges, and propose a multi-constraint hologram pattern matching algorithm THM with lung cancer knowledge graph.The experiments have verified the effectiveness and efficiency of TEM algorithm.ResultsThe experiments have verified the effectiveness and efficiency of TEM algorithm.This method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency.DiscussionThis method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency. |
| Author | Zhang, Zan Tao, Zhenchao Tu, Houdie Li, Lei |
| AuthorAffiliation | 3 School of Computer Science and Information Engineering, Hefei University of Technology , Hefei , China 2 Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology , Hefei , China 4 Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China , Hefei , China 1 School of Artificial Intelligence, Hefei University of Technology , Hefei , China 5 Department of Radiation Oncology, Anhui Provincial Cancer Hospital , Hefei , China |
| AuthorAffiliation_xml | – name: 4 Department of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China , Hefei , China – name: 1 School of Artificial Intelligence, Hefei University of Technology , Hefei , China – name: 2 Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology , Hefei , China – name: 3 School of Computer Science and Information Engineering, Hefei University of Technology , Hefei , China – name: 5 Department of Radiation Oncology, Anhui Provincial Cancer Hospital , Hefei , China |
| Author_xml | – sequence: 1 givenname: Houdie surname: Tu fullname: Tu, Houdie – sequence: 2 givenname: Lei surname: Li fullname: Li, Lei – sequence: 3 givenname: Zhenchao surname: Tao fullname: Tao, Zhenchao – sequence: 4 givenname: Zan surname: Zhang fullname: Zhang, Zan |
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| Cites_doi | 10.1145/2508020.2489791 10.1145/3481640 10.14778/1920841.1920878 10.14778/2732977.2732992 10.1109/ICDE.2008.4497505 10.1145/1281192.1281271 10.3156/jsoft.28.920 10.3233/IDA-160824 10.1145/3685054 10.1016/j.ipm.2019.102054 10.1109/TNNLS.2021.3137396 10.3233/IDA-194653 10.1109/TPAMI.2017.2696940 10.1109/MIS.2016.6 |
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| Copyright | Copyright © 2025 Tu, Li, Tao and Zhang. Copyright © 2025 Tu, Li, Tao and Zhang. 2025 Tu, Li, Tao and Zhang |
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| Keywords | graph pattern matching Monte Carlo method probability graph multi-constranint lung cancer knowledge graph |
| Language | English |
| License | Copyright © 2025 Tu, Li, Tao and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. cc-by |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Yi Zhu, Yangzhou University, China Qixing Qu, University of International Business and Economics, China Edited by: Guanfeng Liu, Macquarie University, Australia Reviewed by: Peng Shi, University of Science and Technology Beijing, China |
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| Title | Edge-level multi-constraint graph pattern matching with lung cancer knowledge graph |
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