Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph
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 med...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2025.1546850/full |
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| author | Houdie Tu Lei Li Lei Li Zhenchao Tao Zhenchao Tao Zan Zhang Zan Zhang |
| author_facet | Houdie Tu Lei Li Lei Li Zhenchao Tao Zhenchao Tao Zan Zhang Zan Zhang |
| author_sort | Houdie Tu |
| collection | DOAJ |
| description | 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.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.ResultsThe experiments have verified the effectiveness and efficiency of TEM algorithm.DiscussionThis method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency. |
| format | Article |
| id | doaj-art-38c529326ba24a3da52d3e6f29ea45d0 |
| institution | Kabale University |
| issn | 2624-909X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj-art-38c529326ba24a3da52d3e6f29ea45d02025-02-10T06:48:37ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2025-02-01810.3389/fdata.2024.15468501546850Edge-level multi-constranint graph pattern matching with lung cancer knowledge graphHoudie Tu0Lei Li1Lei Li2Zhenchao Tao3Zhenchao Tao4Zan Zhang5Zan Zhang6School of Artificial Intelligence, Hefei University of Technology, Hefei, ChinaKey Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaDepartment of Radiation Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, ChinaDepartment of Radiation Oncology, Anhui Provincial Cancer Hospital, Hefei, ChinaKey Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei, ChinaIntroductionTraditional 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.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.ResultsThe experiments have verified the effectiveness and efficiency of TEM algorithm.DiscussionThis method effectively addresses the complexity of uncertainty in lung cancer knowledge graph, and is significantly better than the existing algorithms on efficiency.https://www.frontiersin.org/articles/10.3389/fdata.2025.1546850/fullgraph pattern matchingprobability graphlung cancer knowledge graphMonte Carlo methodmulti-constranint |
| spellingShingle | Houdie Tu Lei Li Lei Li Zhenchao Tao Zhenchao Tao Zan Zhang Zan Zhang Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph Frontiers in Big Data graph pattern matching probability graph lung cancer knowledge graph Monte Carlo method multi-constranint |
| title | Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph |
| title_full | Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph |
| title_fullStr | Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph |
| title_full_unstemmed | Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph |
| title_short | Edge-level multi-constranint graph pattern matching with lung cancer knowledge graph |
| title_sort | edge level multi constranint graph pattern matching with lung cancer knowledge graph |
| topic | graph pattern matching probability graph lung cancer knowledge graph Monte Carlo method multi-constranint |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2025.1546850/full |
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