nghiên cứu phương pháp chuyển đổi giữa mô h̀nh mức khái niệm và ontology TT TIENG ANH

HUE UNIVERSITY UNIVERSITY OF SCIENCES VO HOANG LIEN MINH RESEARCH ON TRANSFORMATION METHODOLOGY BETWEEN CONCEPTUAL MODEL AND ONTOLOGY MAJOR: COMPUTER SCIENCE CODE: 9.48.01.01 SUMMARY OF PHD THESIS HUE, YEAR 2021 The thesis has been completed at Hue University – University of Sciences Supervisors: - Assoc Prof Dr Hoang Quang, Faculty of Information Technology, University of Sciences, Hue University - Assoc Prof Dr Hoang Huu Hanh, Director of International Training Center Posts and Telecommunications Institute of Technology Reviewer 1: Assoc Prof Dr Dang Van Duc – Institute of Information Technology, Viet Nam Academy of Science and Technology …………………………………………………………… …………………………………………………………… Reviewer 2: Assoc Prof Dr Tran Van Lang – Institute of Mechanism and Application Information, Viet Nam Academy of Science and Technology …………………………………………………………… …………………………………………………………… Reviewer 3: Prof Dr Le Xuan Viet – Quy Nhon University …………………………………………………………… …………………………………………………………… The thesis will be defended at Hue University’s thesis committee meeting at: ……………………………………………………………… This thesis can be found at the following library: The library of University of Sciences, Hue University PREFACE Necessity of the topic The software industry has made many important breakthroughs in the design of data models, especially conceptual models Because they reflect the real world well, entity-relationship models (ER models) and its extensions are considered conceptual data models In addition, in order to model information systems with a set of attributes and methods to reflect the data structure of a class, the UML class diagram is also one of the conceptual data models used to describe and reflect the real world of information systems According to the W3C, information provided by websites accounts for nearly 70% of all information exchanged worldwide The web has become a large data system, and it is an indispensable information transmission medium in these days The question is how to effectively exploit information on the Web, specifically how computers can understand and support humans to process such information automatically Therefore, the semantic web was introduced, in which information is clearly defined, so that humans and computers can work together more effectively Ontology is a scientific term used to describe the entities in the real world and the relationships between those entities Ontology provides a way for both humans and machines to perceive information, thereby improving two-way systems of interaction and knowledge sharing Then, humans and computers can work together, and computers "understand" and are able to process information effectively An important benefit of using ontology is semantic search capabilities So, W3C designed OWL as a language that describes classes, properties, and relationships between objects in a way that machines can understand web content However, most of the data has been modeled and stored in traditional databases (relational databases, object oriented databases) Such data is therefore beyond the reach of many applications of the semantic web With the current development of the semantic web, the integration of existing web applications into the semantic web is becoming an urgent issue In addition, current applications are often designed from the conceptual data model, while the semantic web is mainly built on ontologies represented by OWL Therefore, upgrading an information system by transformating conceptual data models to ontology allows the inheritance of data structures of older systems, so reduces design costs Research motivation There are many researches proposing the transformation between conceptual data model and ontology, such as: transforming ER model, UML class diagram to OWL ontology, extracting from OWL ontology to ER model In the published researches, the authors have only proposed to transform the general cases of the conceptual model and the OWL ontology But in reality, many information systems are designed to be sure to reflect the nature of the real world, with many extended components Applying the transformation rules of previous studies will not fully transform the models Therefore, proposing a complete set of rules for transforming the components of the conceptual model and OWL ontology is an important issue in upgrading old information systems Research objectives The purpose of this thesis is to research and develop transformation methods between conceptual data models (such as ER model, EER model, UML class diagram) and OWL Therefore, the thesis implements specific objectives including: (1) transforming the entity-relationship model into OWL ontology; (2) transforming the UML class diagram into OWL ontology; (3) extract conceptual data model from OWL ontology Object and scope of the study - Research objects: OWL1, OWL2 and conceptual data models: entityrelationship model (ER), EER model, UML class diagram - Research scope: It proposes a solution to transform between conceptual data model and ontology Research Methodology - Theoretical research methodology: It aims to find out, collect, and analyze published research works, journals, and articles published by prestigious journals, seminars, or conferences, which helps to facilitate researching, and supplementing transformation methods between conceptual data models and OWL ontology Consequently, it enables to evaluate strengths and weaknesses of preceding publications in comparison with the recommended methods - Experimental methodology: It aims to install the principles of conversion recommended by the thesis in order to prove the applicability of the methods of conversion Thesis structure The thesis includes the introduction, four chapters of content, conclusion and references, as follows: Chapter presents an overview of conceptual data models, including entity–relationship model, UML class diagram, semantic web concepts and OWL ontology; surveys and analyzes several research works related to the conversion between conceptual data model and OWL ontology; analyzes the equivalence between the conceptual data model and the OWL ontology Thereby, the thesis will suggest more transformation rules between a conceptual data model and OWL ontology Chapter summarizes and analyzes the transformation results between ER, EER and OWL ontology of researches, uniformly normalizes the transformation rules according to the thesis's transformations; recommend additional rules for transforming EER model to OWL ontology and presents the transformation experiment results Chapter summarizes and analyzes the transformation results between UML class diagram and OWL ontology of several researches, uniformly normalizes the transformation rules according to the thesis's transformation; proposes additional rules for transforming a UML class diagram to an OWL ontology based on the similarities between them; and presents the transformation experiment results Chapter suggests rules for extracting a conceptual data model from a given OWL ontology The extraction of a conceptual data model from a given OWL is regarded as defining a backward mapping of the transformation from conceptual data model to OWL ontology, allowing "investigation" of a conceptual data model that could have been used to design an ontology Each extraction method is illustrated to verify the results CHAPTER OVERVIEW OF CONCEPTUAL DATA MODEL AND ONTOLOGY 1.1 Introduction 1.2 Introduction of the conceptual data models 1.3 Introduction of the semantic web and ontology 1.4 Overview of ealier researches 1.4.1 The proposed results of entity-relationship model transformation to OWL Since 2005, there have been many studies proposing methods to transform from ER or EER model to OWL ontology such as [1] [2] [3] [4] In order to be able to propose transformation rules, all of these proposals define the corresponding components between the ER model and OWL The earlier researches suggest the following transformation rules: Transformation of entities type; Transformation of single_valued attribute; Transformation of the relationship: Inheritance relationship (Is-A); Binary relationship; n-ary relationship However, these studies have not suggested the transformation rules with the components of Extended Entity Relationship (EER) model, specifically, - Transformation of the weak entity and identifying relationship - Transformation of the nested multi-valued composite attributes - Transformation of recursive relationship - Transformation of temporal aspects of EER When performing the installation of a transformation program, if we not consider all possible cases of an input model, the output model cannot be identified Based on existing researches, the thesis proposes some additional transformation rules from ER model to OWL in order to design ontology as the stated purposes 1.4.2 The proposed results of UML class diagram transformation to OWL There have been many researchs on transforming from UML class diagrams to OWL to reuse old systems [5] [6] [7] [8] [9] [10] [11] [ 12] [13] [14] The authors have analyzed the basic differences, thereby proposing transformation rules: Transform class; Transform attribute; Transform relations between classes: association, association with association classes, aggregation, dependency, generalization/specialization Overall, previous researches have proposed transformation rules for the cases of UML class diagrams, but these researches have not analyzed and proposed the transformation of detailed cases of UML class diagrams, including: - Transformation of the attribute where the datatype is class; - Transformation of the structured attributes; - Transformation of the association with associated classes; - Transformation of the recursive association; - Transformation of the aggregation with a qualify; - Transformation of the dependency 1.4.3 The results of proposed extraction of the conceptual data model from OWL The authors [24] [25] have focused on developing a set of rules for mapping OWL to ER and EER models by defining the basic components of ER and EER models in a given OWL, as follows: mapping entities, mapping attributes, mapping relationships Previous researches have proposed some mapping cases from OWL to EER model However, there are a number of output components that have not been extracted, including: - Composite attribute - Recursive relationship - N-ary relationship 1.5 Summary of Chapter The first chapter of the thesis introduced an overview about the conceptual data models, the semantic web and OWL This chapter outlines the approach of the thesis, an overview of the results of the authors which transform between conceptual data models and OWL The following chapters will present the research results of the thesis on these topics CHAPTER TRANSFORMATION OF ENTITY-RELATIONSHIP MODEL INTO OWL 2.1 Introduction The transformation of a conceptual model to OWL consists of two stages: preprocessing and transformation The preprocessing phase transforms the conceptual data model into an XML/XMI document The transformation phase will apply algorithms to transform XML/XMI documents into OWL ontology Figure 2.1 Conceptual data model into OWL framework architecture 2.2 Previous researches 2.3 Additional transformation rules 2.3.1 Transformation of the weak entity and identifying relationship The author [6] shows that there is no difference when transforming the strong entity type and the weak entity type, except for adding the datatype property to class which represent the weak entity type This datatype property represents the primary key of the strong entity type Semantics is 10 side entity type and some properties representing for the temporal restriction depend on the temporal aspects XX as shown in Table 2.3 2.4.5 Transformation of the temporal attribute of the relationship Rules EER21 Each attribute attR has the temporal aspect XX of the relationship R: Add class C(attR_XX), the attribute attR transformed into the datatype property of class C(attR_XX); Add two inverse object properties that represent the relationship between class C(R) and C(attR_XX): attR_has_XX with domain being class C(R) and range being class C(attR_XX); XX_of_attR with domain being class C(attR_XX) and range being class C(R), and has functional characteristics Minimum cardinality restriction of two properties is set to 1; Key of class C(attR_XX) consists of the properties XX_of_attR and some properties which represent the temporal restrictions depend on the temporal aspect XX as shown in Table 2.3 2.5 Experimental results The thesis has experimented on two sample data models, the CitationEnhanced Bibliographic Database [33] and the Elmasri model [2] The thesis transforms to OWL and calculates the accuracy [34] after transforming between the method proposed and previous proposals, such as S.R Upadhyaya [6], M Fahad [7], I Myroshnichenko [8], Pasapitch Chujai [10] Figure 2.27 Comparison of transformation performance on citationenhanced bibliographic database With the citation-enhanced bibliographic database [15], the proposed methods [6], [7], [8], [10] transform most of the cases, because this model does not contain components indicated in Section 2.3 But with Elmasri model [2] with more general cases, the methods [6], [7], [8], [10] cannot transform From the experimental results of the transformation methods, it has proved the completeness of the proposed method for the problem of transformating the extended ER model to OWL 16 Figure 2.18 Comparison of transformation performance on Elmasri model 2.6 Summary of Chapter In this chapter, the thesis introduces the method of transforming the extended ER model to OWL The thesis has added transformation rules: the weak entity and identifying relationship; the nested multi-valued composite attributes; recursive relationship; temporal aspect on the TimeER model In which, related to the recursive relationship, the thesis has classified and propose transformation for these cases Based on those transformation rules, the thesis also proposed a method to convert TimeER model to OWL [CT1] [CT3] [CT4] 17 CHAPTER TRANSFORMATION OF UML CLASS DIAGRAM INTO OWL ONTOLOGY 3.1 Previous researches 3.2 Additional transformation rules 3.2.1 Transformation of structure attribute The author [22] proposes to transform the structure attribute to a new class and link by an object property However, datatype properties in OWL support hierarchies, so transforming structured attributes to sub datatype properties will represent more context, and it is no need to create new classes or properties Rule UML10 The structure attribute attU of class U which has the set of sub attributes sub_attU is transformed into datatype property DP(attU) in class C(U) The sub attribute sub_attU will transform into sub datatype properties DP(sub_attU) of the datatype property DP(attU) The DP(sub_attU)’s domain is DP(attU) and range is corresponding with data type in OWL 3.2.2 Transformation of the rescursive association The author [22] has proposed a rule to transform the rescursive association, however, the rescursive association has not been classified, so the transformation has not properly reflected the nature of this association Checking the role allows us to classify all of the rescursive association: symmetric or asymmetric Rule UML11 Let consider the symmetric recursive association of class A with role role: Add the object property with its name which is the name of role, domain and range which is class C(A), set ReflexiveProperty for this object property; If the cardinality constraint is 1-1, add the maximum cardinality restriction by 1; With the minimum cardinality constraint being 1-1 add minimum cardinality restriction by to this object property 18 Rule UML12 Considering the asymmetric recursive association of the class A with two roles role1 and role2: Add a pair of inverse object properties with their name being role1 and role2, domain and range being class C(E); The cardinality constraint on the asymmetric recursive association will converse the position between two classes when transforming into OWL For each role with min/max cardinality constraint different and N, add the min/max cardinality restriction corresponding to the object properties 3.2.3 Transformation of shared aggregation The aggregation is a special kind of association between classes that specifies that every instance of a given class (of parts) can be a part of at most one whole With that property, we come across with the following findings: - Aggregation is an association between classes that are not symmetric, so when transformating, you must set up the asymmetric with the AsymmetricProperty axiom - Aggregation is not reflexive, that is, a class has no aggregation to itself, so when transforming, it must set up non-reflexivity with IrReflexiveProperty axiom This prevents instances from being related to itself by an object property that represents an aggregation Rule UML13 Let consider the shared aggregation class A associated with component class F: Add class C(A) and C(F); Add two object properties for representing the relationship between class C(A) and C(F): A_F with domain is C(A), range is C(F); F_A with domain is C(F), range is C(A); Set IrReflexiveProperty and AsymmetricProperty axiom for the object attribute F_A The cardinality constraint on the share aggregation will change the position between two classes when transforming into OWL; 3.2.4 Transformation of the qualified aggregation Rule UML14 Let consider the aggregation with a qualify Q between the whole class A and the component class F: the qualify Q is transformated to a datatype property Q in class C(F), of which domain is class C(F) and the 19 range is the corresponding data type in OWL; Add two inverse object properties which show relationship between class C(A) and C(F): A_F with domain being C(A), range being C(F); F_A with domain being C(F), range being C(E) and maxQualifiedCardinality cardinality restriction being The keys of class C(F) includes the datatype attribute Q and object property F_A; The cardinality constraint on UML will change position between two classes when transforming into OWL; 3.3 Experimental results The thesis evaluates the proposed transformation rules by applying the transformation method on two UML class diagrams, the Purchase Order Application model [35] and Elmasri [2], then determines the output and compare this result with previous methods Figure 3.13 Comparison of transformation performance on UML class diagram Purchase Order Application Figure 3.14 Comparison of transformation performance on UML class diagram Elmasri The thesis uses precision evaluation measure [34] after transforming the thesis’s method proposed and previous proposals, such as S.Brockmans [13], Noreddine Gherabi [16], Imnas Zarembo [18], Jesper Zedlitz [ 19] [20] [21], Oussama [22] The comparative data from Figures 3.13 and 3.14 show that the proposed conversion method is incomplete, but in which the proposed thesis method is higher than that of other methods 3.4 Summary of Chapter Inheriting previous researches, the thesis has analyzed and proposed additional transformation rules, as follow: Transformation of structure attribute; Transformation of the rescursive association; Transformation of shared aggregation; Transformation of the qualified aggregation The completeness of the proposed method is clearly shown through the experimental results presented at the end of chapter [CT2] [CT7] CHAPTER 20 EXTRACTING A CONCEPTUAL DATA MODEL FROM OWL ONTOLOGY Extracting a conceptual data model from a given OWL ontology is regarded as defining a backward mapping of the transformation from conceptual data model to OWL ontology Accordingly, the input and output of this problem are determined as follows: - Input: OWL ontology - Output: a conceptual data model The rules for identifying components of the output are made according to the following general: Construct a condition to identify components so that the "forward transformation" algorithm for this component is satisfied, but not satisfied for the remaining components This allows proving the correctness of the rules by the exclusion method 4.1 Extracting EER model from OWL 4.1.1 Previous researches 4.1.2 Additional extraction rules 4.1.2.1 Extracting composite attribute In OWL, a rdfs:subPropertyOf axiom defines that the property is a subproperty of some other property Rule OWL7 The subproperty sub_dpC is defined with the structure , of which domain is datatype property dpC and the range is the primitive data type, then extracted to the subproperty sub_dpC of the composite attribute dpC 4.1.2.2 Extracting the recursive relationship Rule OWL8 The object property OP with ReflexiveProperty of which domain and scope are class C, then extract to the recursive relationship without properties of the entity type E(C) If object property OP have SymmetricProperty then extract to the symmetric recursive relationship of the entity type E(C) The role name of recursive relationship is the name of the object property, cardinality constraint’s recursive relationship depends on minQualifiedCardinality and maxQualifiedCardinality 21 Rule OWL9 For every inverse object properties role1 and role2 with ReflexiveProperty and AsymmetricProperty of which domain and range are class C, then extract to the asymmetric recursive relationship of the entity type E(C), two roles of recursive relationship are the name of those object properties The cardinality constraint’s recursive relationship depends on minQualifiedCardinality maxQualifiedCardinality of role1 and role2, respectively 4.1.2.3 Extracting n-ary relationship Rule OWL10 For every class C with two inverse object properties OP, domain is class C, set of key attributes of class C includes object properties with domain being class C and minQualifiedCardinality being 1, then extract to 1:1 n-ary relationship Depending on the minQualifiedCardinality and maxQualifiedCardinality of the OP's inverse object properties that set the cardinality constraint’s n-ary relationship In summary, the rules for extracting components of an EER model from an OWL2 can be represented by the diagram as follows Figure 3.2 Cases extracted into EER model 22 4.1.3 Example Figure 3.3 Extracted EER model To illustrate the extraction method, the thesis is experimental on the sample Library ontology [36], the results of the EER model are shown in Figure 3.3 4.2 Extracting UML class diagram from OWL The extraction of an UML class diagram from a given OWL is regarded as defining an inverse mapping of the transformation from UML class diagram to OWL ontology The thesis inherits the analysis of similarities between UML class diagram with OWL to perform extracting OWL to UML class diagram The thesis analyzes on both OWL1 and OWL2 structures to generalize the transformation rules 4.2.1 Extracting class Rule OWL1 A class or subclass C declared by owl:class or rdfs:subClassOf axiom will convert to a class U(C) in UML class diagram 23 4.2.2 Extracting attribute Rule OWL2 The datatype property dpC of which domain is class C and range is the primitive data type in OWL is extracted to attribute U(dpC) of calss U(C), which has the corresponding data type in UML 4.2.2.1 Extracting the key attribute Rule OWL3 The datatype property keyC with owl:hasKey axiom, of which domain is class C and range is the primitive data type in OWL, that is declared by owl:hasKey, is extracted to attribute U(keyC) of class U(C), which has the corresponding data type in UML and label OCL is “unique” 4.2.2.2 Extracting the structure attribute Rule OWL4 For every datatype property sub_dpC that is declared by