蔡益坤臺灣大學:資訊管理學研究所劉俊宏Liu, Jun-HongJun-HongLiu2010-05-052018-06-292010-05-052018-06-292008U0001-1707200821235500http://ntur.lib.ntu.edu.tw//handle/246246/179859語意網的核心挑戰之一就是將現有的大量資訊轉變成由知識本體語言所定義的知識本體，這些現有的大量資訊主要是由網路上的資訊內容所構成，因此，為了實現語意網的目的，將這些網路上的資訊內容對映至知識本體是有必要的。既然網路上擁有超過數億的網頁，而這些網頁上的資訊內容大多數是被儲存在關聯式資料庫因而很難被搜尋引擎所找到(俗稱“深網”)。因此，為了讓語意網可以取用這些資訊內容，一個有效的方法就是將深網底下的關聯式資料庫對映至某特定領域中一個已有的知識本體。 在這篇論文中，我們設計出一套半自動化方法直接將一個關聯式資料庫對映到某特定領域中一個已有的知識本體，這個方法採取在資料採礦中的群集分析概念去替每個表格找出它們的匹對類別群組，其中在類別群組中的每個類別都會滿足對映一至性亦即對映的結果不應該違背在關聯式資料庫所表達的事實。我們將此方法分成兩階段。第一階段主要是利用語意上的資訊將關聯式資料庫中的外鍵對映到知識本體中的物件屬性以獲得一些表格的匹對類別群組特徵，有別於其它方法，我們在對映時，不只考量了表格之間的一般化/特殊化關係也考量了隱含表達在關聯式資料庫中的反關係以建構出一個隱含匹對等級。第二階段使用外鍵的對映結果作為特徵線索以找出一些表格的匹對類別群組，然後再利用這些表格的匹對類別群組去找到其它表格的匹對類別群組，最後將每個表格對映至本身的匹對類別群組。一個雛形化系統顯示出利用此方法執行一些現實世界中的樣本對映有良好的績效。One of the core challenges of the Semantic Web is to transform mass existing information to OWL ontologies. This mass existing information is mainly composed of the contents on the Web. Therefore, to realize the Semantic Web, it will be necessary to map the Web contents to OWL ontologies. Manually mapping the Web contents to OWL ontologies is impractical since the Web has well over billions of Web pages and most of the contents of Web pages is stored in the relational databases and hence hard to be found by search engines (so-called the ``emph{deep Web}"). Hence, to make these contents available for the Semantic Web, an effective way is to map the relational databases underlying the deep Web to domain-related OWL ontologies. In this thesis, we propose a semi-automatic approach for directly mapping relational databases to OWL ontologies. This approach takes the concept of cluster analysis in data mining to find the matching classes group (MCG) for every table, where every found class of MCG will satisfy the mapping consistency stating that the mapping results should not violate the fact expressed in the relational database. We divide our approach into two phases. The first phase primarily uses the semantic information to map the foreign keys of a relational database to object properties of OWL ontologies to get the features of MCGs for tables. Different from other approaches, we not only take into consideration the Generalization/Specialization relationship between tables but also take advantage of the inverse relationship between tables which are implicitly expressed in a relational database to construct an implicit matching level between foreigns keys and object properties. The second phase uses the mapping results of the foreign keys as clues of features to find MCGs for some tables and then takes the MCGs of these tables to find MCGs for other tables. Finally, every table is mapped to its MCG. A prototype system demonstrates that our approach performs well on several domain samples from the real world.1 Introduction 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Motivation and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Related Work 5.1 Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Deep Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Mapping between Relational Databases and OWL Ontologies . . . . . . . 9.3.1 Extracting OWL ontologies from Relational Databases . . . . . . 9.3.2 Mapping Relational Databases to Existing OWL Ontologies . . . 11.4 Matching Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.1 String-Based Matching . . . . . . . . . . . . . . . . . . . . . . . . 13.4.2 WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.4.3 Graph Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4.4 Matching Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Preliminaries 20.1 Relational Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.1.1 Relational Data Model . . . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Relational Database Normalization . . . . . . . . . . . . . . . . . 23.1.3 Entity-Relationship model . . . . . . . . . . . . . . . . . . . . . . 24.2 OWL: Web Ontology Language . . . . . . . . . . . . . . . . . . . . . . . 24.2.1 OWL Lite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25.2.2 OWL DL and OWL Full . . . . . . . . . . . . . . . . . . . . . . . 28.2.3 The Mapping Between OWL DL And Description Logic . . . . . 29.3 Di erences Between Database Schema and Ontology . . . . . . . . . . . 30 Mapping Approach 32.1 Overview of the Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 33.2 Preliminary De nitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35.3 Classifying Tables and Columns . . . . . . . . . . . . . . . . . . . . . . . 36.3.1 Classifying Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . 37.3.2 Classifying Foreign Keys . . . . . . . . . . . . . . . . . . . . . . . 39.4 Matchers in the Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 40.4.1 Matchers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40.4.2 Source of Computing the Similarity . . . . . . . . . . . . . . . . . 43.5 Mapping Foreign Keys to Object Properties . . . . . . . . . . . . . . . . 44.5.1 Mapping Base FKs . . . . . . . . . . . . . . . . . . . . . . . . . . 44.5.2 Mapping Part-Of FKs . . . . . . . . . . . . . . . . . . . . . . . . 46.5.3 Mapping FKs of Relationship Tables . . . . . . . . . . . . . . . . 47.6 Mapping Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48.6.1 MCGs of Associated Tables . . . . . . . . . . . . . . . . . . . . . 48.6.2 MCGs of G/S Tables and Other Tables . . . . . . . . . . . . . . . 50.6.3 Mapping Tables and Non-Foreign Keys . . . . . . . . . . . . . . . 52.6.4 Mapping Columns . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Prototype System: Annotator 57.1 Annotator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57.1.1 Matcher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58.1.2 Relational Database . . . . . . . . . . . . . . . . . . . . . . . . . 59.1.3 OWL Ontology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Conclusion 64.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66ibliography. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68application/pdf988206 bytesapplication/pdfen-US語意網知識本體語言深網對映關聯式資料庫群集分析對映一致性Semantic WebOWLDeep WebMappingRelational DatabasesCluster AnalysisMapping Consistencyhttp://ntur.lib.ntu.edu.tw/bitstream/246246/179859/1/ntu-97-R95725040-1.pdf