明代地名連結與歷史地圖對應：地理空間實體解析方法框架

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姓名

楊典翰(Dian-Han Yang) 查詢紙本館藏

畢業系所

人工智慧國際碩士學位學程

論文名稱

明代地名連結與歷史地圖對應：地理空間實體解析方法框架
(Linking Ming Dynasty Toponyms with Historical Maps: A Geospatial Entity Resolution Framework)

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至系統瀏覽論文 (2028-1-15以後開放)

摘要(中)

中國數百年前的歷史段落時常蘊含豐富的基層社會流動與聚落資訊，是研究微觀歷史地理的重要史料。然而，傳統歷史地理資訊系統（HGIS）資料庫（如CCTS）多側重於縣級以上之行政地點，缺乏對村落層級的覆蓋，導致研究者難以定位方志中記載的庶民地名。此外，村落名稱高度重複（如「王家村」）所引發的空間歧義性，亦是自動化定位的主要挑戰。

本研究提出一套整合深度學習與空間計算的地名消歧異框架。首先，利用客製化之命名實體識別模型（NER-MS）從明代段落中擷取地名；其次，鑑於明代地圖精度之不足，本研究採用1930年代河北省地圖作為地名資料來源；最後，建構基於行政邊界凸包（Convex Hull）與 5 公里緩衝區（Buffer）的空間過濾機制，有效降低同名異地之歧義問題。

摘要(英)

Historical local records passages serve as invaluable repositories for understanding social mobility and settlement patterns. However, standard Historical Geographic Information System (HGIS) databases, such as the Chinese Civilization in Time and Space (CCTS), primarily index high-level administrative place names, lacking the granularity
required to map village-level settlements. Furthermore, the prevalence of generic place names (e.g., ”Wang Village”) introduces significant spatial ambiguity, hindering auto
mated localization efforts.

This study proposes an integrated framework combining Named Entity Recognition (NER) with computational geometry to address these challenges. The methodology proceeds in three stages: first, extracting toponyms using a custom-trained NER model (NER-MS); second, utilizing 1930s historical maps of Hebei (河北) as a comparative reference materials to bridge the gap between pre-modern texts and modern coordinates; and third, implementing a spatial filtering mechanism based on administrative convex hulls
augmented with a 5 km buffer to resolve homonymy.

關鍵字(中)

★ 命名實體連結
★ 命名實體識別
★ 歷史地理

關鍵字(英)

★ Named Entity Linking
★ Named Entity Recognition
★ historical geography

論文目次

1 Introduction 1
1.1 Background.................................................................... 1
1.2 Objective...................................................................... 1
2 RelatedWork 3
2.1 NamedEntityLinkingforClassicalChinese................................. 3
2.2 GazetteerConstruction....................................................... 3
2.3 ModelingSpatialUncertaintyandBoundaries............................... 4
3 Method 5
3.1 Methodology.................................................................. 5
3.2 ToponymExtractionviaNamedEntityRecognition......................... 7
3.3 TemporalAugmentationvia1930sHistoricalMaps.......................... 7
3.4 SpatialFilteringbyCountyBuffers .......................................... 8
4 ExperimentsandResults 11
4.1 StepwiseFilteringEfficiencyAnalysis........................................ 11
4.1.1 DataPre-processingandCandidateGeneration...................... 11
4.1.2 ExperimentalProcedure............................................... 14
4.2 DataDivergence............................................................... 15
4.2.1 DivergenceinToponymicStrata...................................... 16
4.2.2 DisambiguationEfficiencyacrossToponym........................... 16
4.2.3 TheNecessityofMicro-ScaleGeospatialData........................ 19
xvii
CONTENTS
5 AnalysisandDiscussion 21
5.1 TheShiftfromMacro-toMicro-Geographies................................ 21
5.1.1 ComplementaryGeographicStrata................................... 21
5.2 SpatialConstraintsasaNecessityforVernacularHGIS..................... 22
5.2.1 Solvingthe“WangVillage”Problem................................. 22
5.3 AddressingtheTemporalGap:MingTextsvs. 1930sMaps................. 24
6 Conclusion 25
7 Limitations 27
8 FutureWork 29
Bibliography 31
AAppendix:ExperimentalDetails 33
A.1 A.1GeospatialAnalysisParameters.......................................... 33
A.2 A.3DataProcessingSample.................................................. 33

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指導教授

蔡宗翰(Tzong-Han Tsai)

審核日期

2026-1-26

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