Thesis
Reference
Analysis and specification of scientific knowledge visualization techniques
DAPONTE, Vincenzo
Abstract
Scientific knowledge embraces all the notions of the scientific subjects, such as mathematics, physics, chemistry and more. This knowledge requires to be visualized to various users and for different tasks. The purpose of this work is to improve the design of scientific knowledge visualization systems through the creation of User interface (UI) prototypes from the scientific knowledge to visualize. To achieve this purpose a methodology is proposed. The formal representation and visualization of this knowledge are the first research questions addressed by two reference models: Scientific Knowledge Model and Visual Model. A mapping language designed to declare abstract representations of the desired UI prototypes associates these two models. Algorithms to transform these abstract representations into concrete UI prototypes have been also developed. The methodology has been tested using various sources and the results, including the real use case of the CERN CMS High Level Trigger configuration management system, are presented.
DAPONTE, Vincenzo. Analysis and specification of scientific knowledge visualization techniques. Thèse de doctorat : Univ. Genève, 2019, no. GSEM 71
URN : urn:nbn:ch:unige-1258167
DOI : 10.13097/archive-ouverte/unige:125816
Available at:
http://archive-ouverte.unige.ch/unige:125816
Disclaimer: layout of this document may differ from the published version.
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(Analyse et spécification des techniques de visualisation de connaissances scientifiques »)
THESIS
submitted to the
Geneva School of Economics and Management, University of Geneva, Switzerland,
by
Monsieur Vincenzo DAPONTE
Under the direction of
Prof. Gilles FALQUET, co-supervisor, University of Geneva Dr. Andrea BOCCI, co-supervisor, Cern, Switzerland
in fulfillment of the requirements for the degree of
Docteur ès économie et management mention information systems
Jury members:
Prof. Dimitri KONSTANTAS, Chair, University of Geneva Prof. Gilles FALQUET, co-supervisor, University of Geneva
Dr. Andrea BOCCI, co-supervisor, Cern, Switzerland Dr. Claudine METRAL, University of Geneva Dr. lr. Rob LEMMENS, University of Twente, Pays-Bas
Thesis no 71 Geneva, September 2019
La Faculté d’économie et de management, sur préavis du jury, a autorisé l’impression de la présente thèse, sans entendre, par-là, émettre aucune opinion sur les propositions qui s’y trouvent énoncées et qui n’engagent que la responsabilité de leur auteur.
Geneva, le 23 septembre 2019
Dean
Marcelo OLARREAGA
Impression d’après le manuscrit de l’auteur
LE DOYEN
Uni Mail - 40 bd du Pont d’Arve - CH-1211 Genève 4 www.unige.ch
A Q U I D E D R O I T
I M P R I M A T U R
Je, soussigné, Professeur Marcelo OLARREAGA, Doyen de la Faculté d’Economie et de Management, confirme que Monsieur Vincenzo DAPONTE obtient l’imprimatur pour sa thèse N°71, suite à sa soutenance publique du 13 septembre 2019, pour le grade de docteur en systèmes d’information.
Prof. Marcelo OLARREAGA Doyen
Genève, le 23 septembre 2019
MO/GK/kra
I thank my Family.
I thank Gilles, my University supervisor.
I thank Andrea, my Cern supervisor.
I thank the Commission.
I thank Giuseppe, precious office and work mate.
I thank Marta and Maria for the invaluable smiles.
I thank Roberto and Raluca, my never too far friends.
I thank Leo and Luna, for giving me joy everyday.
I thank Claudia, my friend, my partner and the love of my life.
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Scientific knowledge embraces all the notions of the scientific subjects, such as mathe- matics, physics, chemistry and more. This knowledge whether recent or well established requires to be visualized to various users and for different tasks. The purpose of this work is to improve the design and development process of scientific knowledge visualization sys- tems by enabling the creation of User interface (UI) prototypes from the scientific knowl- edge to visualize. To achieve this purpose a methodology is proposed that is punctuated in formal steps and employs conceptual and practical tools. How to formally represent and to visualize this knowledge are the first research questions addressed by two reference models, the Scientific Knowledge Model (SKM) and the Visual Model (VM). The SKM exposes the formalism, the concepts and the relations among them regarding the knowledge represented; while the VM groups the visual elements with their properties used to present the knowledge to the user. The association of these two models is achieved by a mapping language designed to declare abstract representations of the desired UI prototypes. Algo- rithms to transform these abstract representations into concrete UI prototypes have been also designed and developed. The methodology and the tools employed have been tested using various scientific knowledge sources and the results, including the real use case of the CERN CMS High Level Trigger configuration management system, are presented.
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La connaissances scientifiques englobe toutes les notions des matières scientifiques, dont les mathématiques, la physique, la chimie et plus encore. Cette connaissance, qu’elle soit récente ou bien établie, nécessite d’être visualisée auprès de différents utilisateurs et pour différentes tâches. Le but de ce travail est d’améliorer le processus de conception et de développement des systèmes de visualisation des connaissances scientifiques en permet- tant la création de prototypes d’interface utilisateur (IU) à partir des connaissances scien- tifiques à visualiser. Pour atteindre cet objectif, une méthodologie est proposée, ponctuée d’étapes formelles et utilisant des outils conceptuels et pratiques. Comment représenter formellement et visualiser ces connaissances sont les premières questions de recherche abordées par deux modèles de référence, le modèle des connaissances scientifiques (Scien- tific Knowledge Model - SKM) et le modèle visuel (Visual Model - VM). Le SKM expose le formalisme, les concepts et les relations entre eux concernant les connaissances repré- sentées, tandis que le VM regroupe les éléments visuels avec leurs propriétés utilisées pour présenter les connaissances à l’utilisateur. L’association de ces deux modèles est réalisée par un langage de mapping conçu pour déclarer des représentations abstraites des proto- types d’interface utilisateur souhaités. Des algorithmes pour transformer ces représenta- tions abstraites en prototypes d’interface utilisateur concrets ont également été conçus et développés. La méthodologie et les outils utilisés ont été testés en utilisant diverses sources de connaissances scientifiques et les résultats, y compris le cas d’utilisation réel du sys- tème de gestion de configuration du High Level Trigger de l’expérience CMS au CERN, sont présentés.
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Acknowledgement v
Abstract vii
Résumé ix
1 Introduction and background 1
1.1 Motivations . . . 3
1.2 Background . . . 3
1.3 Research questions . . . 4
2 State of the Art 9 2.1 Models of (knowledge) visualization processes . . . 9
2.1.1 Model-driven User Interfaces . . . 10
2.1.2 Scientific data visualization . . . 13
2.1.3 Visualization models summary . . . 15
2.2 Scientific knowledge visualization systems . . . 16
2.2.1 Visualization techniques . . . 16
2.2.2 Hypertext based systems . . . 17
2.2.3 Multi-dimensional Plotsbased systems . . . 20
2.2.4 Graphbased systems . . . 25
2.2.5 Ontologyvisualization systems . . . 28
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3 Methodology proposed 39
3.1 Knowledge visualization model . . . 39
3.2 A methodology for creating knowledge visualization prototypes . . . 41
3.2.1 Objectives . . . 42
3.2.2 Methodology main components . . . 42
3.2.3 Basic operational scenario . . . 44
3.3 Requirements analysis for the Scientific Knowledge model . . . 45
3.3.1 SKM requirements . . . 46
3.3.2 State of the art on SKM . . . 46
3.3.3 Summary . . . 48
3.4 Requirements analysis for the Visual model . . . 48
3.4.1 VM requirements . . . 49
3.4.2 State of the art on VM . . . 49
3.4.3 Summary . . . 50
3.5 Requirements analysis for the Mapping model . . . 50
3.5.1 Mapping requirements . . . 51
3.5.2 Survey on non procedural graph mapping languages . . . 52
3.5.3 Summary . . . 53
4 Scientific knowledge model 55 4.1 Conception of the reference SKM . . . 55
4.2 Model formalization: the SKOO ontology . . . 57
4.2.1 SKOO Top level . . . 57
4.2.2 SKOOScientific Knowledge itemtop level class . . . 59
4.2.3 SKOOScientific activitytop level class . . . 60
4.2.4 SKOOScientific information objecttop level class . . . 61
4.2.5 The validation of SKOO . . . 62
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5.2 Model implementation: VIDOO . . . 67
5.2.1 VIDOO ontology Top level . . . 67
5.2.2 Data structure . . . 68
5.2.3 Visual object . . . 70
6 Mapping language 71 6.1 Conceptual problem . . . 71
6.2 Approach . . . 72
6.3 Mapping node details . . . 74
6.3.1 DataStructuresubclass content information . . . 74
6.3.2 Nodes recursive composition . . . 77
6.4 Concrete visualization definition . . . 78
6.4.1 Interfaces synchronization and binding . . . 79
6.5 Summary . . . 83
7 Prototype viewer 85 7.1 Conceptual design . . . 85
7.2 Architecture . . . 88
8 Methodology validation 95 8.1 Validation process . . . 95
8.2 Validation outcomes . . . 96
8.2.1 An accelerator physics textbook . . . 96
8.2.2 TheHigh Level Triggerof theCMSexperiment at CERN . . . 100
9 Conclusions 109 9.1 Contributions . . . 109
9.2 Outlook . . . 112
A First Appendix 113 A.1 The task analysis for the CMS HLT managment system . . . 113
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A.1.3 9 1.3 Sequence related tasks . . . 120
A.1.4 Module related tasks . . . 123
A.1.5 Stream related tasks . . . 126
A.1.6 Dataset related tasks . . . 130
A.1.7 ParameterSets related tasks . . . 133
A.1.8 General purpose tasks . . . 137
B Mapping instances 141 B.1 Accelerator physics textbook prototypes . . . 141
B.1.1 Task1 UI prototype . . . 141
B.1.2 Task2 UI prototypes . . . 144
Bibliography 155
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2.1 Comparative table of models for representation of visualization systems. . . 15 2.2 Comparative table of the analyzed visualization systems. . . 35 3.1 Transformation Operators for the data states if the Data State Model pre-
sented in Chi (2000) . . . 40 3.2 TheKnowledge State Model (KSM)states and their descriptions. . . 40 3.3 The transformation operations for theKSMstates and their descriptions. . . 41 4.1 Correspondences between the classes of SKOO and OMDoc, DOLCE,
WordNet . . . 63 6.1 The node specific information to provide through specific attributes for the
main subclasses of VIDOODataStructure. . . 76
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1.1 A semantic network example of tourism-related terms by Bergeret al.(2003). 2 1.2 The periodic table of the elements. . . 2 1.3 A visual representation of the placement of an optimal solution in terms
complexity and usability. . . 6 2.1 The graphic representation in the form of UML Class diagram of the KM
(a) and VM (b) representing theWikipediasystem. . . 18 2.2 The graphic representation in the form of UML Class diagram of the KM
(a) and VM (b) representing theWolfram Alphasystem. . . 19 2.3 The graphic representation in the form of UML Class diagram of the KM
(a) and VM (b) identified for theKMSsystem. . . 20 2.4 The graphic representation in the form of UML Class diagram of the KM
(a) and VM (b) representing theNanoPortsystem. . . 21 2.5 The graphic representation in the form of UML Class diagram of the KM
(a) and VM (b) representing theVxInsightsystem. . . 22 2.6 (a) The KM and (b) VM of theStarlightsystem represented through UML
Class diagrams. . . 23 2.7 (a) The KM and (b) VM of the Vista system represented through UML
Class diagrams. . . 24 2.8 (a) The KM and (b) VM of theData Explorersystem represented by UML
Class diagrams. . . 25 2.9 The KM (a) and the VM (b) of theGeneWayssystem represented as UML
Class diagrams. . . 26
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2.11 A view ofProtégédisplaying several widgets as the graph based view in the main panel, the class hierarchy tree in the bottom panel and the instances relationships and list respectively at the top and at the bottom of the right
side panel. . . 29
2.12 The individuals graph view ofGraphDBsystem. . . 31
3.1 The main conceptual components of the proposed methodology. . . 43
3.2 A representation of the basic operational scenario through a Petri net. The transitions can be matched with the operations from the KSM.Step 1tran- sition corresponds to the Knowledge transformation, Step 2 depends on the DE design choices whileStep 3transition corresponds to theMapping definition and Step 4 corresponds to the Generation. Finally the step 5 represents theevaluationof the generated prototypes. . . 45
4.1 The representation of the conceptual model of the proposed methodology. In red is highlighted the SKOO ontology presented in this chapter. . . 56
4.2 The classes and the relations in the SKOO top level, aligned with the higher level concepts of DOLCE ontology and WordNet. . . 58
4.3 The hierarchy within theScientific Knowledge itemclass. . . 60
4.4 The hierarchy within theScientific activityclass. . . 61
4.5 The hierarchy within theScientific information objectclass. . . 62
4.6 Examples of concepts included and identified is the sections of chapter 3 of Wille (2000) expressed as instances of the SKOO ontology. . . 64
5.1 The representation of the conceptual model of the proposed methodology. In green is highlighted the VIDOO ontology presented in this chapter. . . . 66
5.2 The classes and the relations in the VIDOO top level. . . 68
5.3 The Hierarchy of the subclasses of theData_Structureclass. . . 69
5.4 The Hierarchy of the subclasses of theVisual_Objectclass. . . 70
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VIDOO ones. The mapping association can relate several instances of a model to several of the other one. . . 72 6.2 The basic structure of theNode, the elementary part of a mapping. . . 73 6.3 The conceptual representation of the selector: the mapping tool to pull
SKOO instances. . . 75 6.4 MultipleDataStructurespecific attribute instance declaration, the example
of a bipartite Graph. . . 77 6.5 The classes and the relations in the VIDOO top level. . . 78 6.6 Theinclude-nodeattribute enables the nesting ofguestnodes by specifying
theguestnodeidattribute in thehostnode selector. . . 78 6.7 Thestyleattribute in the mapping structure. . . 79 6.8 An example of avidoo:Graphmapping node. . . 80 6.9 Thebindattribute in the mapping structure, used to synchronize two inde-
pendent nodes. . . 80 6.10 An example of mapping nodes with nested node inclusion and node syn-
chronization through binding. . . 82 7.1 The basic operational scenario presented in chapter 3 described through a
Petri net syntax. . . 86 7.2 The sequence diagram of the interactions among the user, the SKOO end-
point and the Prototype viewer. . . 86 7.3 The main components on which the implementation of the proposed
methodology is based. . . 88 7.4 The modules that compose the Prototype viewer and their interactions with
the user and the SKOO endpoint. . . 89 7.5 In the same panel an example of the main parameters editing feature
(through the text area on the left side of the panel), and an example of synchronizationwith thebindingUI prototype at the center and thebound one flanked on the right side. . . 93
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physics textbook Wille (2000). . . 98
8.3 The user interface of theLiving Textbooksystem. . . 98
8.4 The first prototype option for Task2. . . 99
8.5 The second prototype option for Task2 based on the Living Textbook ap- proach. . . 101
8.7 The classes of the CMS HLT configuration domain aligned with the SKOO ontology. . . 102
8.6 The main concepts of the CMS HLT configuration domain. . . 102
8.8 The mockup of a possible UI produced to accomplish Task1. . . 103
8.9 The mockup of a possible UI produced to accomplish Task2. . . 104
8.11 The second UI prototype option produced for Task1. . . 105
8.10 The first UI prototype option produced for Task1. . . 105
8.12 The UI prototype produced for Task2. It includes the two independent nodes for subtask Task2.1 and Task2.2 synchronized. . . 106
8.13 The final UI implementation for Task1 together with the task of displaying the selected HLTModuleparameters. . . 107
8.14 The final UI implementation for Task2. . . 107
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Introduction and background
The purpose of this work is to propose a methodology to improve the design and develop- ment process of scientific knowledge visualization (SKV) systems.
In defining the purpose, before telling how it is meant to be achieved, it should be defined what is the object of this work, the visualization of course, but of what? Scientific knowledge is not a well-defined field (and it will not be at the end of this work), but it is important to indicate where it is located with respect to fields that may seem similar and sometimes overlapping. In the context of scientific research there are many fields that have always been particularly relevant, they sometimes blend together and seem to overlap.
The Data visualization is certainly among them, and especially in such a context it is well studied [Tufte (2001)]. On the other hand, when referring to Knowledge visualization, a similarly important field is referred to, even if less investigated than Data visualization.
One of the reasons may be the form, or rather the forms through which knowledge is presented which are multiple. The semantic networks [Sowa (1987)], for example, can be considered instances of Knowledge visualizations where concepts are represented as nodes with labeled links (Fig. 1.1) and the knowledge is a combination of information about concepts and how those concepts relate to each other. Another famous example assimilable to scientific knowledge is the periodic table of elements (Fig. 1.2).
This visualization system is based on a model of knowledge constituted at the base by the single element, represented by a symbol, the atomic number (Z), the element name, el- ements with no stable isotopes have the atomic masses of their most stable isotopes, where
1
Figure 1.1: A semantic network example of tourism-related terms by Bergeret al.(2003).
Figure 1.2: The periodic table of the elements.
such masses are shown, listed in parentheses [Greenwood & Earnshaw (2012)]. Element properties determine the groups subdivision and they are translated visually (graphically) in the position assumed by the element on the table and eventually in the color-code asso- ciated to it.
1.1 Motivations
The motivations that drove the realization of this work are many, among them certainly does not figure the intention to create a new visualization technique. There are many well known, assessed and widely used ones that can be exploited to visualize scientific knowl- edge. Surely the visualization, although it is a highly researched topic, still has many open challenges [Johnson et al.(2006), Liuet al.(2014)]. Specifically the aim to improve the design and development process of visualization systems, particularly for scientific knowl- edge, thus operating in the field of HCI engineering and not strictly in design field. HCI engineering focuses on the tasks of people using the system, on the information that the users need to perform their tasks and on the environment in which they work[Buie & Val- lone (1997)], in this case the users are the experts in charge of the development of the visualization system. In particular, to facilitate the access to those development phases frequently dropped due to lack of time, such as the creation and evaluation of prototypes.
Another challenge that has been taken up is the one ofmulti-view: to provide different vi- sualizations - therefore different points of view - on the same set of knowledge at the same time, therefore potentially satisfying more users (and more tasks).
1.2 Background
In the area of knowledge visualization, in particular in the scientific field, it is necessary to establish properly the main concepts encountered and to identify the domain of interest.
The difference between Data visualization and Knowledge visualization, in particular, is the first notion to point out, because often in the collective perception they may appear super- imposed or even synonymous. Therefore, Data visualization may be defined asthe science of visual representation of data, defined as information which has been abstracted in some
schematic form, including attributes or variables for the units of information [Friendly &
Denis (2001)]. Knowledge visualization on the other handis a field of study that investi- gates the power of visual formats to represent knowledge and aims at assisting the users in learning and problem solving [Terganet al.(2006)]. Going further in details, the con- cept of Scientific knowledge has been introduced, a concept that in this work is intended as descriptive of the knowledge produced by scientific results. It differs from Scientific Visu- alization [Nielsonet al.(1997)] since this latter concept is intended as the visualization of the scientific results themselves.
1.3 Research questions
The definition of the research scope inevitably leads to the question of where a significant contribution can be offered in this area. There are many open questions in this domain, but in order to try to address the purposes stated above, it is certainly necessary to answer more precise research questions.
The first question this work should answer should be about the development of scientific knowledge visualization systems, in particular
• how the process of HCI engineering in the field of scientific knowledge can be for- malized?
This question raises other consequent issues, these issues can be synthesized by the fol- lowing questions. Discussing the domain of scientific knowledge within this hypothetical process model, an answer should be given to
• according to the previously introduced interpretation, how the scientific knowledge domain can be effectively formalized?
Having pointed out that this work does not intend to present a new visualization technique at all, another question would certainly be
• how should be formalized the different visualization techniques (at least the most important ones) and the elements contributing to the presentation of such knowledge?
Once these research questions have been answered, the subsequent follow up question would be
• how the elements of the scientific knowledge domain can be associated to the ele- ments of the visual object domain?
Last but not least, there is the question that the author considers to be the most important as it marks the difference between a correct methodology and a useful methodology:
• would the proposed solution and the eventual formalisms describing the domains of interests be usable for a domain expert designing a SKV?
Considering indeed the aforementioned research questions namely the formal models for scientific knowledge and visualization techniques, they could be directly addressed by graphs and to create associations between them a graph mapping technique could be used such as graph grammars [Grzegorz (1999)]. However, an extreme level of complexity is re- quired to specify all the necessary information through graphs, which would consequently be overly complex. As for the associations between graphs, although graph grammars are a very useful tool it involves a heavy and complex formalism even to describe simple asso- ciations. It is therefore clear that the formal tools that should serve the purposes indicated should be usable. To visually represent the impact usability should have, let the two classic approaches to this type of problems such as pure procedural (i.e a code library) and pure declarative (i.e a graph grammar) be placed on a horizontal axis. Being these approaches completely opposite they would tend to the opposite extremes of our axis (Fig. 1.3).
Between these extremes there are the intermediate solutions and for each of them it can be hypothesized an estimation of usability, as showed in Fig. 1.3 where the usability is indicated through the vertical blue axis, while the green axis states the specification level of the approach adopted. A library in fact, although well programmed will always be too specific for a certain task or domain, like for instanced3js[?] is specific for Data Visual- ization, and will always require a learning curve that can only be justified by a worthy final result, which is very difficult to guarantee considering the extent of the domain of interest.
On the other hand, the graph grammars, as already mentioned, are a very complex and not a very ductile formalism, even in this case it is clear that despite their potentiality they
Figure 1.3: A visual representation of the placement of an optimal solution in terms com- plexity and usability.
do not represent the most usable among the tools. An intermediate solution that is suffi- ciently formal and enables a large operability without involving procedural and imperative programming would be ideal to ensure the usability of the whole methodology hence try to improve significantly an HCI engineering process. As a consequence this solution may include a declarative system to define the association of formal models in an effective and usable manner.
Structure of the thesis Following this introduction, a state of the art on visualization models and visualization systems analyzed through a visualization abstract model is pre- sented. In the following chapter a survey on the methodology proposed in this work is outlined. The individual formal components (models) included in the proposed method- ology are detailed in the following three chapters. In particular, in the fourth chapter the formalized scientific knowledge model is described, in the fifth chapter the model of visual objects is presented (and visualization techniques) and in the sixth chapter the mapping language created to formalize the associations between these models is described. In the seventh chapter the system designed and developed to implement and test the proposed methodology is outlined, while in the eighth chapter the results of the tests carried out on
real cases are shown. Finally, conclusions are drawn and in the appendix are listed the in- structions for the use of the system developed with some examples of the mapping language instances.
State of the Art
In order to effectively analyze the state of the art of the Scientific Knowledge Visualization (SKV) domain, it is important to take into account the models of knowledge visualization that can inspire the visualization systems as well as the visualization techniques that can be employed in these systems. Through the insights from the visualization models analysis it is possible to describe further formally the SKV systems and the understanding of the visu- alization techniques allows a wider comprehension of the characteristics of these systems designed to visualize this kind of knowledge. Furthermore, for a deeper understanding of the systems that populate this domain also the tasks that these systems aims to perform are taken into consideration in order to look the aspect of the design driven by them.
Therefore, in the following section certain significant models describing abstract knowl- edge visualization process is presented, while the in the succeeding section is outlined some knowledge visualization techniques. The visualization system analysis will follow after these sections and finally a summary based on the review of these systems, models and techniques is discussed in the last section.
2.1 Models of (knowledge) visualization processes
This section focuses on methodologies and models that, through different approaches, al- low to guide the process of design and development of an UI in a formal and structured way. The first part is dedicated to systems responding to the field of Model-driven User
9
Interfaces (MD-UI), while in the following part presents comprehensive systems for the visualization of structured data.
2.1.1 Model-driven User Interfaces
The scope of Model-driven User Interfaces (MD-UI) systems is teeming with ideas of pro- cesses that allow the formal development UI, in order to clearly analyze these systems it can be useful to start from the analysis of the principles that govern this type of devel- opment processes, these principles are collected and formalized in Vanderdonckt (2008).
This work provides a description of the methodology for developing systems structured according to a Model-Driven Architecture and the conceptual models contained therein, this methodology is based on three main requirements: a) a genuine UI model or set of related models well defined by semantics, syntax, stylistics, hence with the support of a User Interface Description Language (UIDL); b) a development method that is explicitly based on the previously introduced models and that provides explicit guidance to designers;
and c) a tool that support explicitly a development method: a tool explicitly developed for a development method, not adapted to. In details, this methodology is articulated in four main stages and uses a series of conceptual models across all the steps.
• Stage 1. Modeling the tasks and domain: a model is built to represent the end user’s task, the domain of operation and the context of use.
• Stage 2. Building Abstract User Interface (AUI): potential user interfaces are de- scribed independently of any interaction modality and any implementation technol- ogy, where abstract containers and individual components are defined.
• Stage 3. Building Concrete User Interface (CUI): at this step a potential user in- terface is described according to the choice of a particular interaction modality(e.g., graphical, vocal, multimodal). It represents the concrete expression of an abstract UI for a given context of use.
• Stage 4. ProducingFinal User Interface(FUI): the code of a user interface is pro- duced from the previous stages, this code can be either interpreted or compiled.
To process the information across all the stages, in well formed representations suitable for mapping and transformations between each other, several model are being defined, whose main ones are:
Ask model: it describes the interactive task as viewed by the end user interacting with the system. Domain model: is a description of the domain classes of objects manipulated by a user. Mapping model: is a contains a series of related mappings between models or elements of models.Transformation model: Graph Transformation (GT) techniques used to formalize explicit transformations between any pair of models (except from the FUI level).
Context model: is a model describing the three aspects of a context of use: a user model, a platform model, and an environment model. AUI Model: is the model describing the UI at theabstractlevel.CUI Model: is the model describing the UI at theconcretelevel.Process model: organizes tasks in time and space to form high-level business processes. Workflow model: structures business processes into a workflow information system.Resource model:
specifies the resources that can be consumed by the tasks. Below will be discussed two more specific methodologies created from the methodology just described and using the above mentioned models.
An example of a methodology that implements the concepts presented in the work pre- viously analyzed is presented in Martinez-Ruizet al.(2006) where a model-driven method to design user interfaces for Rich Internet Applications (RIA) is proposed. This method de- clines in four steps the transformation from an abstract modeled interface (AUI) into afinal user interface(FUI) coded in a specific platform, usingUsiXML[Limbourget al.(2004)]
to model all the intermediate levels. In the first step, the user (or the domain expert) defines the task and domain modelthrough a CTT diagram W3C (2012) used to define tasks and activities. The diagram type is the only specification provided to define the task and domain model and the absence of specification on the type of information (data or knowledge) to visualize suggests that this method is highly task oriented. The second step produces an Abstract UI definition (AUI) from the task and domain model through a clustering pro- cess using IDEALXML [Monteroet al. (2005)], without any interaction specification. In the third step, the AUI definitions are translated into Concrete Individual Objects(CIOs) mapping popular native widgets sets including ergonomic criteria to fit into the Final User
Interface (FUI). Finally, in the last step are produced operational UIs platform-specific through a toolkit compiler.
Remaining within the scope of the MD-UI, in Sottetet al.(2007) there is the second example of what is defined in Vanderdonckt (2008) aslateral engineering. The approach proposed with the so calledUI plasticityforesee the integration within a unified framework of Model-Driven Engineering (MDE) and Service Oriented Approach (SOA) to cover the development ofinteractive systemsas well as the run-time phase. To accomplish the desired goal this approach relies on five principles.
1. An interactive system is a graph of models (e.g. task model, a concept model, a workspace model) linked bymappingsintended as a rule of correspondence between two sets that associates each member of the first set with a single member of the second [Mifflin (2000) (p. 797)]. This graph, expressing multiple perspectives on the system, is available at design-time as well as at run-time.
2. Thetransformations, intended as the production of a set of target models from a set of source models according to a set of transformation rules is considered to be a model itself.
3. The choice of usability frameworks is left open for eliciting the properties that must, should or may be satisfied bytransformations.
4. All actors are kept in the development and adaptation loop by supporting a mix of automated, semi- automated, and manually performed transformations.
5. Mix open and closeadaptiveness. Aclose-adaptivesystem supports only the adjust- ments planned at the design stage while anopen-adaptivesystem may check whether new adaptation plans can be introduced at run-time.
This system still uses the models introduced previously as AUI, CUI and FUI, and the transformations, also considered models, in this case, instead of going only from the most abstract meta-model to the most concrete one, it is introduced the idea of an evolution at runtime even of the abstract models, the so-called Open Adaptiveness. In practice, this evolution is controlled by a specific software module by means of rules that evaluate differ- ent conditions, predicting each time an outcome, then a consequent evolution. The system
certainly presents the classic elements of the MDA methodology pushing further on a task oriented policy enriched by the possible evolution given by the user experience at run-time.
This last aspect inevitably leaves the knowledge model (in these systems defined asdomain model) to be visualized uncovered. It is significant from this point of view that in the ex- ample proposed, more than knowledge, the focus falls on services (and therefore on tasks) and all references to the domain are linked to the latter.
2.1.2 Scientific data visualization
The first model presented is a visualization model based on the geometry of fiber bundles aiming to define a process to visualize scientific data [Butler & Pendley (1989a)], the model is also applied in the visualization system (ViMS) [Butler & Pendley (1989b)]. The model is based on two major abstract representation models: thegeometric representationand the graphic representation. Ageometric representationis the result of the first stage of this process, which involves the transformation by the user of the application data into geo- metric objects. Geometric objects are the constituent elements of fiber bundles: thebase, thefiber, thebundleand thesectionassociated with each other through Cartesian products (base andfiber) and mapping (frombase tobundle). This process is strongly oriented to the visualization of variables with one or more dimensions making this model particularly suited for this kind of visualization, on the other hand the type of representation involved may overcomplicate the visualization process when concepts rather than data variables have to be visualized. In the next step the visualizations are produced on the basis of ageomet- ric representation to which is associated one (or more)graphic representation. Agraphic representationis a mapping between one or severalsectionsof a vectorbundleand a visual display. The graphic representation model provides two levels of graphic composition:
thegraph leveland thevisualization level. The former provides graphic representations of individual geometric structures. The latter integrates these individual graphic objects into a complete visualization.
Remaining within the context of Data Visualization, in Chi (2000) an extended taxon- omy of the visualization techniques that can be used according to the input data is proposed.
This work defines a visualization model that is structured in different phases, starting from
the identification of the input data up to the definition of an abstract data model - a concept that is implemented in several of the systems being considered in this work. This model also includes the definition of an abstract visualization model, placed just before the fi- nal visualization. In particular, this work proposes two main contributions: theDate State Modeland theTaxonomyof the visualization techniques.
The Date State Model consists of two merging parts: theData stagesand theTransfor- mation Operators, where the operations allow to transition from one stage to the next. The Data Stagesare in order:
1. Value, which represents the raw data,
2. Analytical Abstraction, where an abstract data model is built, essentially the data is complemented by meta-data,
3. Visualization Abstraction, at this stage, from anAnalytical Abstraction, it is defined the information visualizable on the screen using a visualization technique.
4. View, the last stage. It represents the visualization end-product presented to the user.
In order to be able to proceed from one stage to the next, the model in question provides a series of transformations that intersect between every two stages, respectively:
1. TheData Transformationis the processing step that generates ananalytical abstrac- tionfrom thevalue(from data stage1to2).
2. The Visualization Transformation indicates the process that reduces an analytical abstractioninto avisualization abstraction(from data stage2to3).
3. TheVisual Mapping Transformationpresents the visualizable format information in a graphicalview(from data stage3to4).
On the other hand, the taxonomy makes explicit the analysis of visualization systems through the data state model, which allows to isolate the different techniques and even- tually reuse them in design and subsequent developments. This model, compared to those previously analyzed, appears much simpler, since it proposes a lower number of stages (states) reducing to a minimum the abstract levels. This aspect, associated with the amount
Model Source Source
formalization Visualization formalization Model-Driven Architecture
[Vanderdonckt (2008)] Any Not specified
(custom domain model)
AUI - CUI
Model-Drivenlateral engineering[Sottetet al.
(2007)]
Any Custom domain
model and graph of models
AUI - CUI
Model-Driven method to design RIA UI [Martinez-Ruizet al.
(2006)]
Any Custom domain
model AUI - CUI
ViMS [Butler & Pendley
(1989b)] Scientific Data Geometric
representation Graphic representation Date State Model [Chi (2000)] Data Analytical
abstraction Visual abstraction Table 2.1: Comparative table of models for representation of visualization systems.
of information to be carried along together with the rules of transformation that must be applied each time, to transition from one state to the other, make this model lighter and easier to apply. Moreover, it can be extended from the data to the scientific knowledge, being more comprehensive (because it includes both abstract and concrete models of both data and visualization). As for the construction of the taxonomy, it is possible to realize a characterization of the systems of visualization of scientific knowledge - appropriately adjusting the stages of the Data State Model- such as to standardize the analysis of these latter and to have more homogeneous results.
2.1.3 Visualization models summary
This subsection highlights the models identified in the state of the art for the representations of visualization systems. The analysis of these systems has raised different features and emphasized the characteristics of each of them. Comparing these models through these features is useful to draw useful information for a possible choice of use of one of these or for the definition of a new model.
In the Tab. 2.1 all the models have been compared highlighting the characteristics as- similable to the research questions. The characteristics highlighted refer first of all to the domain of interest and its formalization, starting from the type of information the consid- ered model deals with. Then it is identified the way in which this information is abstracted (source formalization). Finally, since these are models for visualization systems, it is im- portant to evidence how visualization techniques and visual objects are modeled, referring to the research question on the formalization of visual objects. The features highlighted in Tab. 2.1 reveal first of all that among the cited models just one that deals with the visual- ization of scientific knowledge. The models that adapt to each type of source, however, do not provide any specific reference for the abstraction of the source of knowledge to be visu- alized; while the models that explicitly indicate how to abstract the source data are indeed oriented to data, at most scientific data, but not to knowledge. Regarding the abstraction of visualization objects all the models offer various ways of formalization applicable to several types of UI.
2.2 Scientific knowledge visualization systems
In this section will be presented different systems of visualization of scientific knowledge, these systems will be analyzed with the help of the Date State Model introduced in the pre- vious section and classified according to the technique of visualization used by the system under review. A list of the visualization techniques will be also presented.
2.2.1 Visualization techniques
The taxonomy of the visualization systems discussed in Chi (2000) analyses different visu- alization systems and identifying for each of them the components according to the stages indicated by the Date State Model. In addition to this characterization, the taxonomy di- vides the visualization systems into sets according to the visualization technique used. The following visualization techniques were identified for the analyzed systems:Geographical- based Info Visualization, Multi-dimensional Plots, Information Landscapes and Spaces, Trees, Network (Graphs are included in this set), Text, Web Visualization, Visualization
Spreadsheets. These categories set can be extended with other techniques such asHyper- textandList(Vector). All these techniques are used in the following analysis of visualiza- tion systems for scientific knowledge. In order to apply this model more effectively to the analysis of scientific knowledge systems, it may be useful to adapt this model, originally oriented to data, to knowledge. For this reason the Knowledge State Model(KSM) is de- fined, which is a knowledge oriented instance of the Date State Model previously presented.
In particular:
• The Value, which in the original model represents the raw data, in this new model represents the knowledge to be visualized.
• The Analytical Abstraction, that constitutes the abstract data model (raw data ex- tended with meta-data), in the new model is replaced by theKnowledge Model(KM) that represents the abstraction the knowledge to visualize.
• TheVisualization Abstraction, where the information that, using a visualization tech- nique, visualizable on the screen is defined. In the new model it is substituted by the Visual Model (VM) that represents the abstraction of the proposed future interface, unattached from any implementation detail, always including the visualization tech- nique.
• The View represents the visualization end-product presented to the user, that in the new model keeps the same meaning.
2.2.2 Hypertext based systems
Among the techniques mentioned in the previous section, theHypertext is widely used to visualize structured concepts through resources linked together through hyperlinks within them [Falquetet al.(2000)].
One of the most popular scientific knowledge visualization systems based onHypertext isWikipedia[Foundation (2001)]. This system combines a simple standard visualization, based on a well-defined layout with hypertexts to navigate through the contents. Its struc- ture allows a fruition of every kind of knowledge, even more so of scientific knowledge.
Through its graphical interface, which resembles an article by structure, it allows many
Figure 2.1: The graphic representation in the form of UML Class diagram of the KM (a) and VM (b) representing theWikipediasystem.
types of users (experts, less experienced, simple onlookers) to easily learn what is pro- posed on a given content. It is possible to state that the main task that such a system allows to accomplish is the divulgation of knowledge to the widest possible public and for this reason it proposes only one type of visualization, which does not change with the content.
Looking at this system through the KSM, while in Fig. 2.1 is shown the identified Knowledge Model (Fig. 2.1(a)) and the Visual Model (Fig. 2.1(b)) for the system under consideration,
• theValueis represented by all the notions of general knowledge treated byWikipedia,
• themappingassociates eachArticlein the KM to anA. Windowin VM.
• TheView, the concretization of the VM, is represented for each article by a window with text, images, references and links to other articles.
Another system very similar in terms of main purpose and structure to the one just de- scribed isWolfram Alpha[Wolfram (2009)], but unlikeWikipediait deals only with scien- tific knowledge: physics, logic, chemistry, computer science, etc.. In addition to the knowl- edge domain displayedWolfram Alphaoffers a richer interface compared to Wikipedia, it includes various widgets that allow a more flexible navigation through the sub-contents, even though the task it addresses remains the same as the divulgation.
Figure 2.2: The graphic representation in the form of UML Class diagram of the KM (a) and VM (b) representing theWolfram Alphasystem.
The review of Wolfram Alpha through the KSM is very similar to that of Wikipedia, although it highlights the differences dictated primarily by the type of knowledge displayed.
• TheValuein fact is constituted by general scientific knowledge notions,
• while the similarities are clearly visible in the diagrams shown in Fig. 2.2 that rep- resent respectively the KM (Fig. 2.2 (a)) and the VM (Fig. 2.2 (b)). The classes identified in these models reflect the more specific nature of Wolfram Alpha com- pared to Wikipedia.
• the mapping, similarly to theWikipediasystem, associates each Article in the KM to anA. Windowin VM.
• The resultingViewis a window for each topic that includes text, formulas, plots and links related to the topics mentioned on the page.
An more specific system that exploits the Hypertext technique is KMS(Knowledge Man- agement System) [Akscynet al.(1987)], an example of system developed not recently but still relevant for its conceptual structure. A distributed hypermedia system for worksta- tions designed to help organizations manage their knowledge. It relies on its conceptual data model, and aKMS database consists of screen-sized WYSIWYG workspaces called frames which contain text, graphics and image items. Individual items can be linked to other frames or used to invoke programs. A combined browser/editor is used to traverse the database and manipulate its contents.
Figure 2.3: The graphic representation in the form of UML Class diagram of the KM (a) and VM (b) identified for theKMSsystem.
The review of this system through theKSMidentifies the following indications:
• The Value of KMS is constituted by several contributions, among these there are:
electronic publishing, on-line manuals, electronic mail and bulletin boards, project management, issue analysis, financial modeling and accounting, user interface to videodisk-based materials, user interface to other programs, software engineering, computer-assisted foreign language translation.
• Fig. 2.3 shows the KM (Fig. 2.3(a)) and VM (Fig. 2.3(b)), while the mapping associating them can by considered a one-to-one relationship since the system is de- veloped in the form of WYSIWYG. An exception to this association is constituted by the tree items hyperlink that represent the links to the frame hierarchically imme- diately subordinate to the current one that are displayed as a list (bullet or numbered) of links.
• The View presents two frames per page and exceptionally one in case this is very dense, i.e. it contains many elements.
2.2.3 Multi-dimensional Plots based systems
The first system considered isNanoPort [Chauet al. (2006)] which performs a literature search and content meta-search based on user keywords and displays the content retrieved
through topic maps profiled on the user’s filtering criteria. Although the purpose may be similar to the one this research work is trying to serve, there are remarkable differences like the premise that the domain knowledge this system refers to has to be published somehow, either as article or patent or equivalent, whereas in the scenario considered in this work no.
Figure 2.4: The graphic representation in the form of UML Class diagram of the KM (a) and VM (b) representing theNanoPortsystem.
The review ofNanoPortsystem through theKSMhighlights the following observations:
• TheValueconsists of bibliographic objects, documents and patents,
• themappingassociates eachDocument cluster(KM, Fig. 2.4 (a)) to aTopic(VM, Fig. 2.4 (b)). TheTopicdimensions are proportional to the cluster dimensions while the distance between topics is inversely proportional to the cluster similarities.
• The Viewdisplays a topic list on the left side (a list of document clusters) and the Topic mapat full page.
A system that aims to manage scientific knowledge within enterprises is the VxInsight [Boyack et al. (2002)] knowledge visualization tool. VxInsight uses data mining from sources of bibliographic information to define subsets of information relevant to a technol- ogy domain and it identifies relationships between the individual resources (e.g., articles) using citations, tags, or textual similarities. The identified objects and their relationships are then clustered using a force-directed placement algorithm to produce a terrain view. The system allows exploration and manipulation of the landscapes and gives detail on demand, to enable analysis of the resulting landscapes.
Figure 2.5: The graphic representation in the form of UML Class diagram of the KM (a) and VM (b) representing theVxInsightsystem.
The review of VxInsight system through theKSM brings to the following considera- tions:
• Value: Bibliographic objects, documents, citations.
• Themappingbetween theKM(Fig. 2.5 (a)) andVM(Fig. 2.5 (b)) defines theDis- tancebetween two nodes as proportional to theSimilarityof the corresponding items in theSimilarity matrix, while eachdisplay objects is associated to oneNode. The Mountainsin theMapare dimensioned according to the amount of similar resources to the to the object represented at the top of the mountain, and the distance between each other is proportional to the nodeDistance.
• View - In the main view the resulting map is displayed. The nodes are shown as dots assembled in a scatter plot, this plot can be displayed alone or on a 3D-map.
The mountain shapes appear in this3D-map underneath the corresponding dot that is labeled with the corresponding inferred topic name.
The Starlight system [Rischet al. (1999)] aims to use multiple, concurrent visualization techniques to support comparison of content and interrelationship information at several levels of abstraction simultaneously. Beyond the visualization techniques, it is interesting to find once again the creation of a data model - not knowledge - to be visualized in more ways.
Figure 2.6: (a) The KM and (b) VM of the Starlight system represented through UML Class diagrams.
Revising theStarlightsystem with theKSMmodel gives the following outcomes:
• TheValueincludes test data, relational databases and spatial information.
• Themappingassociates a computedData structure(KM, Fig. 2.6 (a)) to the chosen Map(VM, Fig. 2.6 (b)), according to the data dimensions.
• TheViewconsists of a topic list on the left side (list of items) and the chosen map at full page. The main topics are highlighted by labels.
Browsing further in the scientific visualization domain the Visualization Tool Assistant (Vista) [Senay & Ignatius (1994)] system stands out for its use of rules, mostly heuris- tic, acquired through literature surveys and investigations. Vista’s primary function is to automatically generate a visualization technique design for a given dataset, it also allows users to modify this design and renders the resulting image using rendering algorithms. It is indeed this possibility to adjust the resulting interface the interesting aspect identified in this system along with the set of rules used to generate the visualization design.
Figure 2.7: (a) The KM and (b) VM of theVistasystem represented through UML Class diagrams.
By overhauling the Vista system using the KSM model, the following outcomes are obtained:
• theValueconsists of scientific datasets,
• in themappingeachVariable(KM, Fig. 2.7 (a)) either single or multiple is encoded in a mark, that represents any graphical symbol visible on a View. These marks are then arranged to form a Render (VM, Fig. 2.7 (b)) according to the chosen Visualization techniqueand following aComposition technique(VM, Fig. 2.7 (b)).
• TheViewdisplays at full page the resulting multi-dimensional plot.
A system that responds to a research question close to the one addressed isSciVi[Ryabinin
& Chuprina (2015)] that proposes an implementation approach similar to the one proposed but applied to the visualization of scientific data. This system proposes methods and tools for adaptive visualization systems’ development allowing the specification of visualization requirements for different fields of science. The difference with the solution proposed in this research work is increasingly evident once it is considered the methods of specification of the data model - hence not knowledge - to be visualized. The following outcomes are obtained by revising this system with theKSMmodel:
• theValueis constituted by scientific data.
• TheKMis represented by theSyntax ontology,
• while the Object Ontology represents theVMcollecting all the individual displayable objects.
• Themappingassociating the two models is encored in theDescription scene.
• TheViewdisplays at full page the multi-dimensional plot resulting from the visual- ization process.
2.2.4 Graph based systems
The first system considered among those that use graphs as a visualization technique is Data Explorer [Lucas et al. (1992)]. It presents an interesting approach that distinguish between the interface model and the data model. It has a set of visualization modules used to visualize a variety of data structured through a data model, but it deals with data visualization, in particular scientific visualization.
Figure 2.8: (a) The KM and (b) VM of theData Explorersystem represented by UML Class diagrams.
TheKSMstages of this systems are discussed as follows:
• TheValue: is constituted by scientific data.
• Themappingassociates eachFunctionin theKM(Fig. 2.8 (a)) to a boxnode(VM, Fig. 2.8 (b)) in thedataflowgraph, while edges linking a box to another represent the output flow coming out from aFunctionand entering into another.
• TheViewis implemented as a comprehensive window including more panels within it. Thedataflowgraph resides in the main panel, where for each box (node) repre- senting aFunction the input boxes are at the top and the output ones at the bottom of it. The output data plot view is shown after the executions of thedataflowgraph modules.
Visualization systems may not only display vast domains of scientific knowledge, but may also be designed for circumscribed subdomains of scientific knowledge. Visualizing a limited set of scientific knowledge is a common need for many scientists from various disciplines and it often happens that each of them produce in-house tools to meet this need. This is the case of many systems created in the field of Biology such asGeneWays [Rzhetsky et al. (2004)] whose purpose is to analyze molecular pathway data and it is addressed to molecular pathway experts who can use this system to perform analysis and study new interactions.
Figure 2.9: The KM (a) and the VM (b) of theGeneWayssystem represented as UML Class diagrams.
TheKSMstages that represent this system are described as follows:
• TheValueincludes text, articles and gene databases,
• themappingprojects on nodes from theVM(Fig. 2.9 (b)) the main entities treated by the system such asgenes, substances, processes (KM, Fig. 2.9 (a)), while the
edges connecting them represent the interactions inferred by the system and encoded in thesimplified gene statements.
• The view presents at full page a directed graph resulting from the user query with different color codes according to type of the entity displayed. A click on an edge shows the original sentence the edge is derived from, as well as the article where the sentence belongs.
Another example within the same domain (Genetics) is theBlast2GOsystem [Conesaet al.
(2005)], whose main purpose is to enable genetic scientists to perform annotations and analysis process onGene Ontology (GO)[Ashburneret al.(2000)] sequences for which no GOannotation is yet available.
Figure 2.10: The identified KM (a) and VM (b) for theBlast2GOsystem expressed in the form of UML Class diagrams.
For this system the identifiedKSMstages are the following:
• The Value is constituted by Gene-ontology data and the data from public gene databases.
• In themappingeachNode(VM, Fig. 2.10 (b)) represents anAnnotation(KM, Fig.
2.10 (a)) on genes while theEdgesshow theparent/childrelationships among them.
• The Viewdisplays at full page a directed acyclic graph (DAG) whose nodes’ color intensity is modulated to highlight the most relevant nodes.
2.2.5 Ontology visualization systems
Ontology are widely used to represent conceptual knowledge (conceptualization) in dif- ferent domains [Gruber (2009)]. It is important to analyze which are the techniques and methods that are used to visualize this knowledge, in particular scientific knowledge, and which task they address. Although several systems that have an ontology as their basis have already been analyzed, in this section are analyzed in full those systems that have as their main purpose specifically the visualization of an ontology or part of it. These systems, pro- viding different visualizations, but addressing the same purpose, create a myriad of systems of scientific knowledge visualization, for this reason they will be analyzed by referring to the peculiarity of each. Within this group systems more oriented to show different parts of an ontology can distinguished, such as systems oriented to the visualization of classes and their relations, or systems that visualize instances and even systems that try to visualize all the elements within an ontology.
In terms ofKSMsome features, in particular the Value and the KM, are equivalent for most of the ontology visualization systems:
• theValueis constituted by any kind of ontology representation data:rdf files,turtle files,owlfiles, etc.
• TheKMis represented by the ontology to visualize,
while theView, theVMand consequently themappingvary for each system.
A large part of the systems belonging to this group are all those tools and plugins as- sociated toProtégé[Gennariet al.(2003)], among the most popular software for the man- agement and visualization of ontologies. Protégé, in itself, offers different visualizations on the various parts of an ontology: the classic hierarchical view that displays the hierarchy of classes, the view showing object properties, the list of all individuals and individuals for each class, the graph-based view for classes and individuals (Fig. 2.11), etc.. This type of visualization is oriented both to the management and the editing that constitute the main task ofProtégé; however, even if this system tries to offer a suitable view for every com- ponent of an ontology. It does not change according to the type of knowledge contained in it and not even in according to the type of user. It remains indeed the same whether the
Figure 2.11: A view ofProtégédisplaying several widgets as the graph based view in the main panel, the class hierarchy tree in the bottom panel and the instances relationships and list respectively at the top and at the bottom of the right side panel.
content of the ontology is displayed by the domain expert or by a non-expert user who is looking for information for research or learning.
• The VM, in the cases of the graph based visualization shown in Fig. 2.11, is con- stituted by nodes and edges. Hence themappingrelates each node to a class or an instance while the edges map the hierarchy and the relationships among them.
• TheViewpresents itself as a multi-widget interface with the class hierarchy tree on the left side or middle panel, surrounded by widgets to filter and insert new instances or additional information in the knowledge base. The graph view of selected classes can be displayed in the middle panel.
An alternative visualization is the one proposed by OntoSphere[Bosca et al. (2005)]
using a 3D view-port with visual cues. The purpose of this system remains the ontology modeling. The reference user type is again the domain expert even though an additional purpose is to extend the system for creating ontology editors for users with low ICT skills.
For this system
• theVMincludes nodes and edges which are part of multi-dimensional maps,
• while themappingassociates to each node a class or an instance and the edges map the hierarchy and the relationships among these elements.
• TheView includes a main panel showing the computed multi-dimensional map in- cluding nodes and edges along the shape of the selected map. Nodes and edges are decorated with the associated instance’s name and the corresponding color code.
Another platform that offers several visualization options for ontologies isGraphDB[On- totext (2015)], a system that allows to import and manage an ontology through a web application. This application proposes different views for different tasks within the on- tology visualization, wherein it is distinguished the visualization in form of graph of the instances and the relations between them (Fig. 2.12), the interface that maps the relations between classes through aChord diagramand the view in styleCircle packingthat shows the classes present in the ontology grouped according to thesubClassrelationship and di- mensioned according to the quantity of instances related to each class. This system has a more attractive interface than the classic ones present for example in Protégé that shows more clearly the various components of an ontology, however there are views related only to the ontology as such and do not adapt to the content or to a different task that is not part of the context of the ontology visualization.
Also in this case, regarding the KSM stages
• theVMis mainly constituted by nodes and edges.
• Themappingis straightforward with the nodes representing the classes and the in- stances while the edges map the hierarchy and the relationships among them.
• TheViewpresents a multi-tab UI displaying several types of graphs and diagrams.
All the graphs are related to classes or instances, while the main diagram (Chord diagram) is related only to relationships in the current KB. There is also a widget dedicated to the editing of the class and their instances, and another to launchSparQL queries on the KB.
These tools are often analyzed and revised to better describe their characteristics and their purpose. Therefore, it is possible to refer to a large part of literature reviews on this subject,
which provide an exhaustive summary for the purposes of drafting this state of the art. From these reviews we can obtain the information relevant to this analysis, such as the reference task of these tools and the types of potential users.
Figure 2.12: The individuals graph view ofGraphDBsystem.
Many other techniques/tools have been revised [Lanzenbergeret al.(2010), Sivakumar et al.(2011)] and propose solutions similar to those mentioned above while always sharing their main purpose. In support of the analysis of these systems, it is necessary to specify that the ontology visualization is part of the problem for which this research work proposes a solution, since the scientific knowledge to be visualized does not necessarily have to be expressed through an ontology, just as it does not have to be necessarily contained in a published resource.
Visualization systems may not only display vast domains of scientific knowledge, but may also be designed for circumscribed subdomains of scientific knowledge. Visualizing a limited set of scientific knowledge is a common need for many scientists from various disciplines and it often happens that each of them produce in-house tools to meet this need.
This is the case of the systems managed by the National center for biomedical ontology [Rubinet al.(2006)] that visualize each time with a specific task, a well defined portion of scientific knowledge - in this case within the domain of genetics - and as such are domain
specific and special purpose systems. Regarding theKSMstages given the specific sector to which this system is addressed, it is worth specifying all the stages:
• theValueconsists of Open Biomedical data.
• TheKMis represented by the considered Biomedical Ontologies,
• while theVMincludesnodesandedges,
• hence themappingspecifies that the nodes represent the classes and the edges map the hierarchy among them.
• TheViewincludes a multi-panel page displaying statistics on the ontologies, an on- tology list and in the main panel and the graph (or tree) visualizing the selected ontology.
Another different approach is proposed by theWSMOViz[Kerrigan (2006)] system, an inte- grated ontology engineering and ontology visualization tool for theWeb Service Modeling Ontology(WSMO). WSMO is a conceptual model for creating semantic descriptions that can be used to resolve interoperability issues between web services.WSMOVizuses a graph based approach providing filtering functionality for removing nodes from the graph, using adhoc layout strategies, it incorporates some of these techniques with some new mech- anisms and produces a visualization tool with editing support. In the description of this system the characteristics of interest for this research work come up as the only possible visualization offered that is based only on graphs and the tasks that the application aims to address that are those related to ontology editing. Once again, given the specific sector to which this system is addressed, it is worth specifying all theKSMstages:
• theValueincludes web service information and vocabulary,
• theKMis represented by theWSMO, the Web Service Modeling Ontology.
• TheVMessentially depict a directed graph, hence it includes nodes and edges.
• Consequently themappingprovides that the nodes represent the classes and individ- uals while the edges map the hierarchy and the relationships among them.
