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題名 語意式之旅遊推薦系統以台北市為例之研究
A study of ontological travel planning recommendation systems for Taipei City
作者 黃少華
Huang, Shao Hua
貢獻者 楊建民
Yang, Jiann Min
黃少華
Huang, Shao Hua
關鍵詞 旅遊
推薦系統
行程規劃
語意網
本體論
Tourism
Recommendation System
Travel Planning
Semantic Web
Ontology
日期 2010
上傳時間 9-五月-2016 16:29:11 (UTC+8)
摘要 近來，旅遊資訊廣被旅遊者在網路上使用。雖然網路上的資訊十分豐富，但是使用者仍常常難以找尋到精準的資訊。而旅遊商品的特性為無形的，所以使用者不能實際地來評估這個服務直到他實際地體驗之後。也就是因為此種特性，所以如何讓使用者在真正體驗到之前能夠取得可信與真實的旅遊資訊變得十分重要。為了解決此問題，語意網絡的概念即出現來解決人與電腦間溝通的問題。而一個本體即是由一個正式化的、某一具有精確規格概念的領域來提供之可實行的平台來發展可信的旅遊資訊服務。
      在本論文中，我們探討了旅遊推薦系統的發展、其遭遇的問題、語意網相關之技術包含了：網路本體語言、資源描述架構、和一些目前現有的旅遊本體發展的情況。此外，為了要能提供更智慧化的旅遊行程規劃推薦服務，我們將語意的想法帶入了此領域。我們會提出一個方法讓智慧型旅遊行程推薦服務能在本體論的基礎上實現。所以，一系列的旅遊本體會被建構發展，來讓我們的芻形系統能夠做出行程推薦的服務。此提出的系統能夠驗證語意網的概念在旅遊推薦領域的可行性。它亦能利用屬性與之間的關係來推薦出更智慧型的資訊，找出個人化的景點、活動與行程給旅行者。
Nowadays, travel information is increasing to appeal the tourists on the web. Al-
     though there are numerous information provided on the web, the user gets puzzled in
     nding accurate information. The tourism product has an intangible nature in that cus-
     tomers cannot physically evaluate the services on o er until practically experienced. This
     makes access to credible and authentic information about tourism products before the
     actual experience very valuable. In order to solve these problems, the concept of seman-
     tic web comes into existence to have communication between human and computer. An
     Ontology being a formal, explicit specication of concepts of a domain provides a viable
     platform for the development of credible tourism information services.
     In this paper, we discuss the development of travel recommendation system, the
     problems it encounters, the related technology about semantic web including OWL, RD-
     F/RDFS, and some current circumstances of the existing tourism ontologies as well.
     Futhermore, in order to make more intelligent travel planning recommendation services,
     we bring the idea of semantic into tourism domain. We will present an approach aimed
     at enabling intelligent recommendation services in tourism support systems using ontolo-
     gies. A suite of tourism ontologies was developed and engaged to enable a prototypical
     tourism system with recommednation capabilities. The proposed system can verify the
     feasibility and concept of taking semantic web technology into tourism recommendation
     systems domain. It also can recommend more intelligent information using properties,
     relationships of travel ontology, and is responsible for nding personalized attractions,
     activities and a trip itinerary for travelers.
參考文獻 [1] World travel and tourism council. http://www.wttc.org/.
     [2] G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender sys-
     tems:a survey of the state-of-the-art and possible extensions. IEEE Transactions on
     Knowledge and Data Engineering, (6):734{749, 2005.
     [3] A. Adomavicius G., Tuzhilin. Toward the next generation of recommender systems:
     A survey of the state-of-the-art and possible extensions. IEEE Transactions on
     Knowledge and Data Engineering, (6):734{749, 2005.
     [4] D. Bachlechner. Ontology collection in view of an e-tourism portal. In Technical
     report. Technical Report, Digital Enterprise Research Institute, 2004.
     [5] Hirsh H. Basu, C. and W. Cohen. Recommendation as classication: using social and
     content-based information in recommendation. Proceedings of the 1998 workshop on
     recommender systems, page 11Ė15, 1998. Menlo Park, CA: AAAI Press.
     [6] Hendler J. Berners-Lee, T. and O. Lassila. The semantic web. Scientic American,
     (5):34Ė43, 2001.
     [7] D. Billsus and M. J. Pazzani. Learning collaborative information lters. Machine
     learning proceedings of the fteenth international conference, 1998.
     [8] Heckerman D. Breese, J. S. and C. Kadie. Empirical analysis of predictive algo-
     rithms for collaborative ltering. Proceedings of the 14th conference on uncertainty
     in articial intelligence (UAI-98), page 43Ė52, 1998.
     [9] Dan Brickley and R.V. Guha. Rdf vocabulary description language 1.0: Rdf schema.
     In Technical report. www.w3c.org, 2002.
     [10] Jing-Yun Chen. The establishment of tourism narration databases by semantic
     systems{a case study in the physical geography of yang-ming mountains. pages
     1{5, 2004.
     [11] Fodor O. Ricci F. Werthner H. DellńErba, M. Harmonise: a solution for data in-
     teroperability. Proceedings of Second IFIP Conference on E-Commerce, E-Business,
     E-Government, pages 433{445, 2002. Kluwer, Boston.
     [12] Wober K. Fesenmaier, D.R. and H.Werthner. Introduction: recommendation systems
     in tourism. Destination Recommendation Systems: Behavioral Foundations and
     Applications, CAB International,London, 2006.
     [13] Johannes Klein Frank Leymann Dieter Roller Francisco Curbera, Yaron Goland and
     Sanjiva Weerawarana. Business process execution language for web services, version
     1.0. In Technical report, www.ibm.com, 2002.
     [14] T. Franke. Enhancing an online regional tourism consulting system with extended
     personalized services. Information Technology and Tourism, (3):135{150, 2003.
     [15] N. Fuhr and C. Buckley. A probabilistic learning approach for document indexing.
     Transactions on Information Systems, (3):223Ė248, 1991.
     [16] J. Gaschnig. Performance measurement and analysis of certain search algorithms.
     1979.
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     289{296, Holanda, 1995. IOS Press.
     [18] Thomas R. Gruber. A translation approach to portable ontology specications. pages
     199{220, 1993.
     [19] Konstan J. A. Herlocker, J. L. and J. Riedl. Explaining collaborative ltering rec-
     ommendations. Proceedings on the ACM 2000 conference on computer supported
     cooperative work, page 241Ė250, 2000. Philadelphia.
     [20] D. Kanellopoulos, S. Kotsiantis, and P. Pintelas. Intelligent knowledge manage-
     ment for the travel domain. GESTS Int. Transactions on Computer Science and
     Engineering, (1):95{106, 2006.
     [21] Musen M. Noy N. Knublauch, H. Tutorial: Creating semantic web (owl)ontologies
     with protege. 2nd International Semantic Web Conference (ISWC2003), 2003. Sani-
     bel, Island, Florida, U.S.A.
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     computational intelligence for modeling, control and automation. CIMCA, 1999.
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     guage semantics and abstract syntax. In Technical report. www.w3c.org, 2004.
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     tering. Articial Intelligence Review, page 393Ė408, 1999.
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     semantics and abstract syntax. In Technical report, www.w3c.org,, 2004.
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     Information Technology and Tourism, (3-4):215{226, 2001.
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     gorithms for e-commerce. Proceedings of ACM E-Commerce 2000 conference, page
     158Ė167, 2000.
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     Electronic commerce recommender applications, (1/2):115Ė153, 2001.
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     cations. Data Mining and Knowledge Discovery, (1/2):115Ė15, 3 2001.
     [40] James Hendler Tim Berners-Lee and Ora Lassila. The semantic web. In Scientic
     American, 2001.
     [41] C. M. Sperberg-McQueen Tim Bray, Jean Paoli and Eve Maler. Extensible markup
     language (xml) 1.0 (second edition). In Technical report, www.w3c.org, 2000.
     [42] Mike Uschold and Michael Gruninger. Ontologies principles,methods and applica-
     tions. Knowledge Engineering Review, (2):14{20, 2 1996.
描述 碩士
國立政治大學
資訊管理學系
97356030
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097356030
資料類型 thesis
dc.contributor.advisor 楊建民zh_TW
dc.contributor.advisor Yang, Jiann Minen_US
dc.contributor.author (作者) 黃少華zh_TW
dc.contributor.author (作者) Huang, Shao Huaen_US
dc.creator (作者) 黃少華zh_TW
dc.creator (作者) Huang, Shao Huaen_US
dc.date (日期) 2010en_US
dc.date.accessioned 9-五月-2016 16:29:11 (UTC+8)-
dc.date.available 9-五月-2016 16:29:11 (UTC+8)-
dc.date.issued (上傳時間) 9-五月-2016 16:29:11 (UTC+8)-
dc.identifier (其他 識別碼) G0097356030en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/95544-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 97356030zh_TW
dc.description.abstract (摘要) 近來，旅遊資訊廣被旅遊者在網路上使用。雖然網路上的資訊十分豐富，但是使用者仍常常難以找尋到精準的資訊。而旅遊商品的特性為無形的，所以使用者不能實際地來評估這個服務直到他實際地體驗之後。也就是因為此種特性，所以如何讓使用者在真正體驗到之前能夠取得可信與真實的旅遊資訊變得十分重要。為了解決此問題，語意網絡的概念即出現來解決人與電腦間溝通的問題。而一個本體即是由一個正式化的、某一具有精確規格概念的領域來提供之可實行的平台來發展可信的旅遊資訊服務。
      在本論文中，我們探討了旅遊推薦系統的發展、其遭遇的問題、語意網相關之技術包含了：網路本體語言、資源描述架構、和一些目前現有的旅遊本體發展的情況。此外，為了要能提供更智慧化的旅遊行程規劃推薦服務，我們將語意的想法帶入了此領域。我們會提出一個方法讓智慧型旅遊行程推薦服務能在本體論的基礎上實現。所以，一系列的旅遊本體會被建構發展，來讓我們的芻形系統能夠做出行程推薦的服務。此提出的系統能夠驗證語意網的概念在旅遊推薦領域的可行性。它亦能利用屬性與之間的關係來推薦出更智慧型的資訊，找出個人化的景點、活動與行程給旅行者。		zh_TW
dc.description.abstract (摘要) Nowadays, travel information is increasing to appeal the tourists on the web. Al-
     though there are numerous information provided on the web, the user gets puzzled in
     nding accurate information. The tourism product has an intangible nature in that cus-
     tomers cannot physically evaluate the services on o er until practically experienced. This
     makes access to credible and authentic information about tourism products before the
     actual experience very valuable. In order to solve these problems, the concept of seman-
     tic web comes into existence to have communication between human and computer. An
     Ontology being a formal, explicit specication of concepts of a domain provides a viable
     platform for the development of credible tourism information services.
     In this paper, we discuss the development of travel recommendation system, the
     problems it encounters, the related technology about semantic web including OWL, RD-
     F/RDFS, and some current circumstances of the existing tourism ontologies as well.
     Futhermore, in order to make more intelligent travel planning recommendation services,
     we bring the idea of semantic into tourism domain. We will present an approach aimed
     at enabling intelligent recommendation services in tourism support systems using ontolo-
     gies. A suite of tourism ontologies was developed and engaged to enable a prototypical
     tourism system with recommednation capabilities. The proposed system can verify the
     feasibility and concept of taking semantic web technology into tourism recommendation
     systems domain. It also can recommend more intelligent information using properties,
     relationships of travel ontology, and is responsible for nding personalized attractions,
     activities and a trip itinerary for travelers.		en_US
dc.description.tableofcontents 1.1 The Semantic Web Layer Stack (Source: James et al., 2001) . . . . . . . 2
     2.1 The Comparison Between CF and CB (Source: James et al., 2001) . . . . 8
     2.2 The RDF Graph (Source: Lassila et al., 1999) . . . . . . . . . . . . . . . 12
     3.1 Structure of Recommendation System based on Semantic Web (Source:
     From our thesis) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
     3.2 Recommendation Mechanism (Source: From our thesis) . . . . . . . . . . 22
     3.3 Comparison Between Travel Ways and Speed (Source: From our thesis) . 23
     3.4 Illustration of Computing Recommendation Circle (Source: From our thesis) 24
     3.5 Planning and Scheduling Stage Example 1 (Source: From our thesis) . . 25
     3.6 Planning and Scheduling Stage Example 2 (Source: From our thesis) . . 25
     3.7 Structure of Attraction Ontology (Source: From our thesis) . . . . . . . . 28
     3.8 Overview of Attraction domain (Source: From our thesis) . . . . . . . . . 28
     3.9 Instance example of Mackay Tomb (Source: From our thesis) . . . . . . . 29
     3.10 Structure of Accommodation domain (Source: From our thesis) . . . . . 29
     3.11 Overview of Accommodation domain (Source: From our thesis) . . . . . 30
     3.12 Instance example of AquaBella Resort Hotel (Source: From our thesis) . 30
     3.13 Structure of Food domain (Source: From our thesis) . . . . . . . . . . . . 31
     3.14 Overview of Food domain (Source: From our thesis) . . . . . . . . . . . . 32
     3.15 Instance example of DinTaiFung Chinese Restaurant (Source: From our
     thesis) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
     3.16 Overview of User domain (Source: From our thesis) . . . . . . . . . . . . 33
     3.17 Instance example of User (Source: From our thesis) . . . . . . . . . . . . 34
     4.1 Searching Temple Type Attraction from Taipei City (Source: From our
     thesis) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
     4.2 Result of Temple Type Attraction in Taipei City (Source: From our thesis) 37
     4.3 Detail Information About Tianhou Temple (Source: From our thesis) . . 37
     4.4 Searching Main Type Food from Taipei City (Source: From our thesis) . 38
     4.5 Result of Food Information in Taipei City (Source: From our thesis) . . . 38
     4.6 Detail Information About DinTaiFung (Source: From our thesis) . . . . . 38
     4.7 Searching All Accommodation from Taipei City (Source: From our thesis) 39
     4.8 Result of Accommodation Information in Taipei City (Source: From our
     thesis) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
     4.9 Detail Information About Ambassador Hotel-Taipei (Source: From our
     thesis) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
     4.10 Enter Attraction Requirement (Source: From our thesis) . . . . . . . . . 40
     4.11 Enter Food Requirement (Source: From our thesis) . . . . . . . . . . . . 41
     4.12 Enter Accommodation Requirement (Source: From our thesis) . . . . . . 41
     4.13 Final Recommendation Route (Source: From our thesis) . . . . . . . . . 41		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097356030en_US
dc.subject (關鍵詞) 旅遊zh_TW
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 行程規劃zh_TW
dc.subject (關鍵詞) 語意網zh_TW
dc.subject (關鍵詞) 本體論zh_TW
dc.subject (關鍵詞) Tourismen_US
dc.subject (關鍵詞) Recommendation Systemen_US
dc.subject (關鍵詞) Travel Planningen_US
dc.subject (關鍵詞) Semantic Weben_US
dc.subject (關鍵詞) Ontologyen_US
dc.title (題名) 語意式之旅遊推薦系統以台北市為例之研究zh_TW
dc.title (題名) A study of ontological travel planning recommendation systems for Taipei Cityen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] World travel and tourism council. http://www.wttc.org/.
     [2] G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender sys-
     tems:a survey of the state-of-the-art and possible extensions. IEEE Transactions on
     Knowledge and Data Engineering, (6):734{749, 2005.
     [3] A. Adomavicius G., Tuzhilin. Toward the next generation of recommender systems:
     A survey of the state-of-the-art and possible extensions. IEEE Transactions on
     Knowledge and Data Engineering, (6):734{749, 2005.
     [4] D. Bachlechner. Ontology collection in view of an e-tourism portal. In Technical
     report. Technical Report, Digital Enterprise Research Institute, 2004.
     [5] Hirsh H. Basu, C. and W. Cohen. Recommendation as classication: using social and
     content-based information in recommendation. Proceedings of the 1998 workshop on
     recommender systems, page 11Ė15, 1998. Menlo Park, CA: AAAI Press.
     [6] Hendler J. Berners-Lee, T. and O. Lassila. The semantic web. Scientic American,
     (5):34Ė43, 2001.
     [7] D. Billsus and M. J. Pazzani. Learning collaborative information lters. Machine
     learning proceedings of the fteenth international conference, 1998.
     [8] Heckerman D. Breese, J. S. and C. Kadie. Empirical analysis of predictive algo-
     rithms for collaborative ltering. Proceedings of the 14th conference on uncertainty
     in articial intelligence (UAI-98), page 43Ė52, 1998.
     [9] Dan Brickley and R.V. Guha. Rdf vocabulary description language 1.0: Rdf schema.
     In Technical report. www.w3c.org, 2002.
     [10] Jing-Yun Chen. The establishment of tourism narration databases by semantic
     systems{a case study in the physical geography of yang-ming mountains. pages
     1{5, 2004.
     [11] Fodor O. Ricci F. Werthner H. DellńErba, M. Harmonise: a solution for data in-
     teroperability. Proceedings of Second IFIP Conference on E-Commerce, E-Business,
     E-Government, pages 433{445, 2002. Kluwer, Boston.
     [12] Wober K. Fesenmaier, D.R. and H.Werthner. Introduction: recommendation systems
     in tourism. Destination Recommendation Systems: Behavioral Foundations and
     Applications, CAB International,London, 2006.
     [13] Johannes Klein Frank Leymann Dieter Roller Francisco Curbera, Yaron Goland and
     Sanjiva Weerawarana. Business process execution language for web services, version
     1.0. In Technical report, www.ibm.com, 2002.
     [14] T. Franke. Enhancing an online regional tourism consulting system with extended
     personalized services. Information Technology and Tourism, (3):135{150, 2003.
     [15] N. Fuhr and C. Buckley. A probabilistic learning approach for document indexing.
     Transactions on Information Systems, (3):223Ė248, 1991.
     [16] J. Gaschnig. Performance measurement and analysis of certain search algorithms.
     1979.
     [17] Asuncion Gomez-Perez, Natalia Juristo, and Juan Pazos. Evaluation and assessment
     of the knowledge sharing technology. In Towards very large knowledge bases, pages
     289{296, Holanda, 1995. IOS Press.
     [18] Thomas R. Gruber. A translation approach to portable ontology specications. pages
     199{220, 1993.
     [19] Konstan J. A. Herlocker, J. L. and J. Riedl. Explaining collaborative ltering rec-
     ommendations. Proceedings on the ACM 2000 conference on computer supported
     cooperative work, page 241Ė250, 2000. Philadelphia.
     [20] D. Kanellopoulos, S. Kotsiantis, and P. Pintelas. Intelligent knowledge manage-
     ment for the travel domain. GESTS Int. Transactions on Computer Science and
     Engineering, (1):95{106, 2006.
     [21] Musen M. Noy N. Knublauch, H. Tutorial: Creating semantic web (owl)ontologies
     with protege. 2nd International Semantic Web Conference (ISWC2003), 2003. Sani-
     bel, Island, Florida, U.S.A.
     [22] A. Kohrs and B. Merialdo. Clustering for collaborative ltering applications in
     computational intelligence for modeling, control and automation. CIMCA, 1999.
     Vienna. IOS Press.
     [23] Miller B. N. Maltz D. Herlocker J. L. Gordon L. R. Konstan, J. A. and J. Riedl.
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