基於語意網技術與WordNet促進地理網路資源之探索

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姓名

楊易宸(YANG, I-CHEN) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

基於語意網技術與WordNet促進地理網路資源之探索
(Facilitating Geospatial Web Resource Discovery based on the Semantic Web Technology and WordNet)

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至系統瀏覽論文 (2026-10-14以後開放)

摘要(中)

地理網路 (GeoWeb) 指的是任何包含地理資訊的網際網路(World-Wide Web, WWW)資源，其中資源(resource)可以是個別資料(data)或網路服務(web service)。透過網際網路，使用者可便利地取得地理網路資源並在許多領域應用，然而因為資源由不同的個別用戶或機構，基於各自的資料模型或編碼，地理網路有著嚴重的異質性問題(heterogeneity issues)：包括地理網路缺乏一致的模型，以及無法顯示資源間隱含的語意(semantic)關係，所以用戶需要了解不同的資料模型和各個資料提供者的表達架構，以探索並識別他們感興趣的地理網路資源；此外，地理網路資源之間的隱含的語意關係，導致簡單的關鍵字匹配不足以讓用戶尋找到其需的地理網路資源。因此，本研究旨在透過完整且易於理解的知識本體（ontology），同時根據不同標準自動描述各種類型的地理網路資源來解決上述問題。而除了考慮了不同的資料模型，本研究亦考慮了地理網路資源間關於主題的隱含語意關係：我們透過WordNet計算語意相似度(semantic similarity)，解析詮釋資料(metadata)，並據此自動化統一以資源描述框架 (Resource Description Framework, RDF)儲存地理網路資源，最後透過SPARQL協定與RDF查詢語言(SPARQL Protocol and RDF Query Language, SPARQL Query)搜索資源，即可透過此研究所提出的知識本體，發現地理網路資源間的主題隱含之語意關係。從結果我們可以看到，知識本體配合語意相似度地計算，可以自動地整合地理網路資源的異質性問題，促進地理網路資源的發現及應用。

摘要(英)

The Geospatial Web (GeoWeb) represents the collection of World-Wide Web resources containing geographic information, where resources could be individual data or web services. With the help of the Internet, GeoWeb resources are easily accessible and have been applied in many fields. However, GeoWeb faces serious heterogeneity issues as resources are provided by various individuals or organizations and are usually based on different data models or encodings. Among the heterogeneity issues, the semantic heterogeneity issue includes lack of a coherent conceptual model for GeoWeb resources and hidden relationships between resources. Hence, users need to understand different data models and individual ontologies to identify and discover the GeoWeb resources of their interest. Furthermore, implicit relationships between GeoWeb resources cause problems on simple keyword matching as users may not search with exact keywords. Therefore, this study aims at addressing the aforementioned issues by describing the GeoWeb resources based on a complete and accessible ontology. Additionally, automatic matching various types of geospatial resources according to each standard. This research considers not only dissimilar data models but also the implicit semantic relationships between their thematic metadata. With WordNet, we parsed metadata of resources and calculated the semantic similarity between proposed ontology and metadata to discover their implicit relationships. With resources represented in the Resource Description Framework (RDF) format, SPARQL queries can be issued to search for resources based on their semantic relationships. The result shows that the proposed solution can integrate heterogenous GeoWeb resources and facilitate the GeoWeb resource discovery.

關鍵字(中)

★ 地理網路
★ 語意網
★ 知識本體
★ SWEET
★ WordNet
★ 語意相似度計算

關鍵字(英)

★ GeoWeb
★ semantic web
★ ontology
★ SWEET
★ WordNet
★ semantic similarity calculation

論文目次

基於語意網技術與 WordNet 促進地理網路資源之探索 ..................................................................... ii
摘要 ......................................................................................................................................................... ii
ABSTRACT ........................................................................................................................................... iv
致謝 ........................................................................................................................................................ vi
TABLE OF CONTENTS..................................................................................................................... vii
LIST OF FIGURES AND ILLUSTRATIONS .................................................................................. viii
LIST OF TABLES ................................................................................................................................. ix
1. INTRODUCTION ........................................................................................................................... 1
1.1. GeoWeb Resource .................................................................................................................... 1
1.2. Semantic Interoperability Issues ............................................................................................. 4
1.3. Semantic web technology ......................................................................................................... 7
1.4. Research Objective ................................................................................................................... 9
1.5. Content Structure of this Thesis ........................................................................................... 10
2 RELATED WORK ...................................................................................................................... 12
2.1. Existing Ontologies ................................................................................................................. 12
2.2. WordNet as a Semantic Resource ......................................................................................... 13
3. METHODOLOGY .......................................................................................................................... 17
3.1 Workflow ................................................................................................................................ 18
3.2. Ontlolgy Design....................................................................................................................... 22
3.3. Synonymy Matching with WordNet ..................................................................................... 28
3.4. Semantic Similarity Calculation ........................................................................................... 28
4. RESULT............................................................................................................................................ 35
5. CONCLUSION AND FUTURE WORK ...................................................................................... 50
6. REFERENCES ................................................................................................................................. 52

參考文獻

1. Kraak, M. J., & Ormeling, F., Cartography: visualization of geospatial data., Pearson Education, Boca Raton, 2003.
2. Crossland, M.D., Wynne, B.E., & Perkins, W.C., “Spatial decision support systems: An overview of technology and a test of efficacy”, Decision support systems, 14, pp. 219-235, 1995.
3. Burrough, P. A., McDonnell, R. A., McDonnell, R., & Lloyd, C. D., Principles of Geographical Information Systems., OUP Oxford., Nova York, 2015.
4. Rao, M., Fan, G., Thomas, J., Cherian, G., Chudiwale, V., & Awawdeh, M., “A web-based gis decision support system for managing and planning usda′s conservation reserve program (crp)”, Environmental Modelling & Software, 22, pp. 1270-1280, 2007.
5. Haklay, M., Singleton, A., & Parker, C., “Web mapping 2.0: The neogeography of the GeoWeb”, Geography Compass, 2 (6), pp. 2011–39, 2008.
6. Goodchild,M. F., “Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0”, International Journal of Spatial Data Infrastructures Research, 2, pp. 24–32, 2007a.
7. Laney,D., “3D data management: Controlling data volume, velocity and variety”, META Group Research Note, 6, 2001.
8. Liang,S. H., & Huang, C. Y., “GeoCENS: A Geospatial Cyber infrastructure for the World-Wide Sensor Web”, Sensors, 13(10), pp. 13402-13424, 2013.
9. Kuhn, W., “Introduction to spatial data infrastructures”, Presentation held on March 2005.
10. Bai, Y., Yang, C., Guo, L., & Cheng, Q., “OpenGIS wms-based prototype system of spatial information search engine”, pp. 3558-3560, IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France, 2003.
11. Li, W., “In Polarhub: A global hub for polar data discovery”, AGU Fall Meeting Abstracts, pp. 3665, 2014.
12. Huang, C. Y., & Chang, H., “GeoWeb Crawler: An Extensible and Scalable Web Crawling Framework for Discovering Geospatial Web Resources”, ISPRS International Journal of Geo-Information, 5(8), pp. 136, 2016.
13. Cahyani, R., “GeoWeb Search Engine Personalization based on Ontology and User Interest”, National Central University, master′s thesis, 2019.
14. Kauppinen, T., Väätäinen, J., & Hyvönen, E., “Creating and using geospatial ontology time series in a semantic cultural heritage portal”, pp. 110-123, In European Semantic Web, Springer, Berlin, Heidelberg, 2008, June.
15. Knoechel, B., Huang, C.Y., Liang, S., “A bottom-up approach for automatically grouping sensor data layers by their observed property”, ISPRS Int. J. Geo-Inform, 2, pp. 1–26, 2013.
16. Hsu, Y. W., “Distribution and Application of Cross-domain Statistical Data with Resource Description Framework”, National Cheng Kung University, master′s thesis, 2019.
17. Hendler, J., “Web 3.0 Emerging”, Computer, 42, pp. 111-113, 2009.
18. Berners-Lee, T., Hendler, J., & Lassila, O., “The semantic web”, Scientific american,284(5), pp. 28-37, 2001.
19. Guarino, N., Oberle, D., & Staab, S., Handbook on Ontologies., (Staab S., Studer R.), Springer, Berlin, Heidelberg, 2009.
20. Miller, G. A., “WordNet: A Lexical Database for English”, Communications of the ACM, Vol. 38, No. 11, pp. 39-41. 1995
21. Finlayson, M. A., “Java Libraries for Accessing the Princeton Wordnet: Comparison and Evaluation”, In H. Orav, C. Fellbaum, & P. Vossen (Eds.), Proceedings of the 7th International Global WordNet Conference (GWC 2014), pp. 78-85, Tartu, Estonia, 2014.
22. Budanitsky, A., Hirst, G., “Evaluating wordnet-based measures of lexical semantic relatedness”, J. Comput. Ling., Vol 32, pp. 13–47, 2006.
23. Pedersen, T., Patwardhan, S., Michelizzi, J., “WordNet::Similarity: Measuring the Relatedness of Concepts”, HLT-NAACL 2004, HLT-NAACL—Demonstrations 04 Demonstration Papers, pp. 38–41, Stroudsburg, PA, USA, 2004.
24. Resnik, P., “Using Information Content to Evaluate Semantic Similarity in a Taxonomy”, Proceeding IJCAI’95 Proceedings of the 14th International Joint Conference on Artificial Intelligence, Volume 1, pp. 448–453, San Francisco, CA, USA, 1995.
25. Lin, D., “An Information-Theoretic Definition of Similarity”, Proceedings of the 15th International Conference on Machine Learning, Volume 1, pp. 296–304, Madison, WI, USA, 24–27 July 1998.
26. Jiang, J.J. & Conrath, D.W., “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy”, The Computing Research Repository, cmp-lg/9709008, 1997.
27. Hirst, G., St-Onge, E., “Lexical Chains as Representations of Context for the Detection and Correction of Malapropisms”, In WordNet: An Electronic Lexical Database, pp. 305–332, Fellbaum, C., (Ed.), The MIT Press: Cambridge, MA, USA, 1995.
28. Banerjee, S., & Pedersen, T., “Extended Gloss Overlaps as a Measure of Semantic Relatedness”, Proceeding IJCAI’03 Proceedings of the 18th International Joint Conference on Artificial Intelligence, Volume 18, pp. 805–810, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2003.
29. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E., & Milios, E., “Semantic Similarity Methods in WordNet and their Application to Information Retrieval on the Web”, In: 7th ACM Intern, Workshop on Web Information and Data Management (WIDM 2005), pp.10–16, Bremen, Germany, 2005.
30. Kim, S.N., Baldwin, T., “Automatic Interpretation of Noun Compounds Using WordNet Similarity”, In: Dale R., Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science, vol 3651, Wong KF., Su J., Kwong O.Y. (eds) Springer, Berlin, Heidelberg, 2005.
31. Batet, M., Sánchez, D., & Valls, A., “An ontology-based measure to compute semantic similarity in biomedicine”, J Biomed Inform, 44, pp. 118-125, 2010.
32. Sheth, A., Henson, C., & Sahoo, S., Semantic Sensor Web. IEEE Internet Comput, 2008(12), pp.78–83.
33. Yue, P., Gong, J., & Di, L., “Augmenting geospatial data provenance through metadata tracking in geospatial service chaining”, Computers & Geosciences, 36, pp. 270–281, 2010.
34. Liu, K., Yang, C., Li, W., Gui, Z., Xu C., & Xia, J., “Using Semantic Search and Knowledge Reasoning to Improve the Discovery of Earth Science Records: An Example with the ESIP Semantic Testbed”, International Journal of Applied Geospatial Research, 5(2), pp. 44-58, 2014.
35. Battle, R., & Kolas, D., “GeoSPARQL: Enabling a Geospatial Semantic Web”, Semantic Web, 3 (4), pp. 355–370, 2012.
36. Perry, M., & Herring, J., OGC GeoSPARQL-A geographic query language for RDF data, OGC implementation standard, 2012.
37. Pedersen, T., Patwardhan, S., Michelizzi, J., “WordNet::Similarity: Measuring the Relatedness of Concepts”, HLT-NAACL Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 38–41, 2004;
38. Na, A., & Priest, M., “Sensor Observation Service Version 1.0.0”, Doc. Nr. OGC 06-009r6, OpenGeospatial Consortium Inc., Wayland, MA, USA, 2007.
39. Raskin, R., & Pan, M., “Semantic Web for Earth and Environmental Terminology (SWEET)”. Semantic Web Technologies for Searching and Retrieving Scientific Data (SCISW), Sanibel Island, Florida, 2003.
40. Tanimoto, T.T., “An elementary mathematical theory of classification and prediction”, IBM Report, November, 1958.

指導教授

黃智遠(Huang, Chih-Yuan)

審核日期

2021-10-19

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