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研究生:林佳宏
研究生(外文):Chia-Hung Lin
論文名稱:以CIDOCCRM為基礎的推論平台–整合異質性影像資料庫的詮釋性與概念性知識
論文名稱(外文):An Inference Platform Based on CIDOC CRM for Integrating Administrative and Conceptual Knowledge Among Heterogeneous Image Archives
指導教授:洪政欣洪政欣引用關係
指導教授(外文):Jen-Shin Hong
學位類別:博士
校院名稱:國立暨南國際大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:138
中文關鍵詞:語意網本體論語意角色標註影像擷取推論平台
外文關鍵詞:OntologyCIDOC CRMImage RetrievalSemantic Role LabelingInference PlatformSemantic Web
相關次數:
  • 被引用被引用:1
  • 點閱點閱:233
  • 評分評分:
  • 下載下載:49
  • 收藏至我的研究室書目清單書目收藏:1
近年來,隨著資料儲存技術的進步,使用者也開始大量產生數位影像,致使有效率地去儲存、搜尋和擷取所需的影像,也成為一個非常重要的課題。一般來說影像擷取技術可分為兩種類型:傳統關鍵字比對方法與以影像內容為基礎的查詢方法,但這兩種方式都有準確率不高且缺乏語意的問題,使用者無法直覺地利用這兩種方式去搜尋影像。之前的研究指出非專業的使用者比較偏好以基於事件的概念描述去尋找圖片,例如以「女人戴帽子」的概念性描述去做影像的擷取。在許多數位博物館中,有關這些概念性描述都存在影像非結構性文字描述的欄位裡,本研究以自然語言處理技術-語意角色標註,自動化地將這些非結構性的文字描述,轉化成為具有結構性的語意角色標註,使得利用基於事件的概念描述去做影像擷取變為可能,結果顯示採用這種方式,會比純粹將該事件以關鍵字去做比對的準確率會來得高。

本研究提供一個以本體論為基礎的方法,去整合影像後設資料的詮釋性知識與影像非結構性文字描述的詮釋性知識與概念性知識。這個目的是為了能夠自動化的產生使用者有興趣的隱性知識,例如在各異質性影像資料庫中,找出具有相同概念的事件且作者風格也相同的所有畫作。事實上,使用者要自行找出這樣的答案,他必須非常繁瑣地在各個不同的影像資料庫做相關性的查詢才有可能找得到。

因此,本論文利用一個在文化遺產領域已成為國際標準的CIDOC CRM做為本體論,以及結合語意網常用的工具軟體,例如Protégé, SWRL 與Jess推論引擎來建構一個推論平台,本研究想要探討的是在真實生活的異質性影像資料庫的詮釋性知識與概念性知識,該如何整合到以CIDOC CRM為基礎的推論平台,並藉此自動地推論出使用者有興趣的隱性知識,系統實作的結果顯示,本研究所提出的推論平台架構是可實際應用在真實生活異質性影像資料庫的整合。
Recently, advances in storage techniques have conducted to an increase in a lot amount of digital images all around the world. These developments have heightened the need for effective image retrieval techniques. Roughly speaking, image retrieval techniques can be divided into two areas: the traditional keyword-based approach and content-based image retrieval. But they still fail on low precision and lack semantic that will cause semantic gap between image features and the user. Previous studies have demonstrated that non-professional users prefer using event-based conceptual descriptions, such as “a woman wearing a hat”, to describe and search images. In many art image archives, these conceptual descriptions are manually annotated in free-text fields. We propose a novel approach for extracting event-based semantic knowledge automatically from free-text image descriptions. The semantic role labeling techniques appear to be a promising technology for transforming the unrestricted natural language texts into structured records that is easy to index/retrieve using relational database technologies. The precision of such SRL-assisted event-based image retrieval is much higher than that of the conventional keyword-based approach.


This thesis proposes an ontology-based approach for integrating metadata-based image annotations (administrative knowledge) with event-based knowledge (administrative and conceptual knowledge), including subject, verb, object, location and temporal information from free-text image descriptions. The goal is to automatically derive certain interesting knowledge, such as, all the paintings with a same painting style and a same conceptual event in the image content that implicitly dispersed around different image archives. In practice, an answer to these questions requires a series of field-based queries, across different digital archives.

This thesis investigated the possibility to utilize standardized ontology, CIDOC-CRM, and semantic web tools, such as Protégé, SWRL and Jess inference engine in order to model an inference platform. We discuss the need for a robust inference platform for real-life knowledge discovery and integration among heterogeneous image archives. Experimental results indicate the approach can be implemented in real-life applications.
Chapter 1 Introduction 1
1.1 The issue of text-based image retrieval 1
1.2 Users desire interesting knowledge in multiple sources 4
1.3 Developing Inference platform 8
1.4 The objectives of this thesis 11
1.5 The outline of this thesis 11
Chapter 2 Paper Review 13
2.1 Image Retrieval Method 13
2.2. Conceptual-level image descriptions 16
2.3 CIDOC Conceptual Reference Model 20
2.4 Semantic Role Labeling 23
2.5 Coreference Resolution 25
2.6 Commonsense Knowledge 26
2.7 Text Mining Technique 28
2.8 Deriving Implicit CRM Relationships in the Semantic Web Architecture 33
2.9 Inferences in Current Semantic Web Infrastructure 35
Chapter 3 Event-based Image Retrieval 42
3.1 System Framework 44
3.2 Evaluations 45
3.3 Discussion 49
3.4 Conceptual knowledge mapping to CIDOC CRM 50
Chapter 4 Mapping administrative knowledge to CIDOC CRM 54
Chapter 5 Relation-Centric Semantic Annotation for Administrative Knowledge 60
5.1 The issues of Semantic Annotation 61
5.2 Methodology 63
5.3 Primitive Evaluations 71
5.4 Discussion 77
Chapter 6 Inference Platform for Generating Deductive Knowledge 78
6.1 SWRL with CIDOC CRM 80
6.2 Inference Platform for Administrative Knowledge 81
6.3 Test runs for the inference platform 91
Chapter 7 Conclusion and Future work 108
References 118
Appendix- Inference Rule List 134
1. Explicit Rules 134
2. Implicit Rules 135
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