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This work explore the use of visualization as a cognitive aid for managing ontologies and knowledge representations.The continuing need for more effective information retrieval has lead to the creation of the notions of the semantic web and personalized information management, areas of study that very often employ ontologies to represent the semantic context of a domain.
The Ontology are machine understandable and thus need some means of graphical visualization for humans to comprehend. Consequently, the need for effective ontology visualization for design, management and browsing has arisen. Visualization tools makes it easy to understand large and complex ontology Important for easy representation of selected parts. There are several ontology visualizations available through the existing ontology management tools. This work presents the preliminary results of an evaluation of three visualization methods in Protege.
4.1 Domain ontology 9
4.2 Upper ontology 9
4.3 Task ontology 9
4.4 Heavy-weight ontology and light-weight ontology 10
5.1 Knowledge Interchange Format 11
5.2 Resource Description Framework 11
5.3 Web Ontology Language(OWL) 12
6.1 Good visualization tool characteristics 12
7. Protege class browser 13
8. OntoViz 16
9. Jambalaya 18
The need for more effective information retrieval has lead to the creation of the semantic web and personalized information management notions, areas of study that take advantage of the semantic context of documents to facilitate their management. In many of the proposed solutions in this field, it is common to take advantage of an ontology. A term initially borrowed from philosophy, it is now used to denote a set of concepts and their interrelations in a specific domain. Consequently, the need for effective ontology visualization for design, management, and browsing has arisen.
Visualization of ontologies is not an easy task. An ontology is something more than a hierarchy of concepts. It is enriched with role relations among concepts and each concept has various attributes related to it. Furthermore, each concept most probably has instances attached to it, which could range from one or two to thousands. Therefore, it is not simple to create a visualization that will effectively display all this information and at the same time allow the user to easily perform various operations on the ontology.
Understanding and maintaining the structure of large knowledge bases has been a problem since the first knowledge based systems, such as rule-based expert systems, were created. Many ontologies contain very complex structures that need to be viewed at various levels of abstraction and in different contexts to be clearly understood. Consequently, knowledge modeling can be a cognitively challenging task, as the user has to clearly understand the ontology at a high level as well as manage and understand detailed information. To enable the reuse of ontologies, analysts will often have to explore and understand ontologies they did not create, to decide how or when to use the existing ontology. Clearly, for large, complex ontologies, this is not a trivial task. Support is also needed to enable the evolution of ontologies.The following sections provide an ontology definition, a description of the techniques, followed by a discussion of their characteristics, and the conclusions.
Ontology is a term initially borrowed from philosophy, where an ontology is a systematic account of existence, that is trying to answer the question 'what properties can explain the existance'. In computer science according to Gruber [1993], an ontology is an explicit specification of a conceptualization. The term "conceptual lization" is defined as an abstract, simplified view of the world, which needs to be represented for some purpose. It contains the objects, concepts, and other entities that are presumed to exist in some area of interest, and the relations that hold among them. That is Ontology describes basic concepts in a domain and defines relations among them .
The basic building blocks include,
Classes - concepts that are also called type, sort, category, and kind - are abstract groups, sets, or collections of objects. They may contain individuals, other classes, or a combination of both. For examples in the world of vehicle we can have the classes
¢ Vehicle, the class of all vehicles
¢ Car, the class of all cars
¢ Class, representing the class of all classes
¢ Thing, representing the class of all things
Attributes - properties of the object in the Ontology.Each attribute has at least a name and a value and is used to store values that is specific to that object it is attached to. For example in vehicle world the Ford Explorer object has attributes such as:
¢ Name: Ford Explorer
¢ Number-of-doors: 4
Relationship - attribute whose value is another object in the ontology. An important use of attributes is to describe the relationships (also known as relations) between objects in the ontology. For example in the ontology that contains the Ford Explorer and the Ford Bronco, the Ford Bronco object might have the following attribute:
¢ Successor: Ford Explorer
This tells us that the Explorer is the model that replaced the Bronco.
The most important type of relation is the is-a relation which explains the hierarchical taxonomy. For example we have already seen that the Ford Explorer is-a 4-wheel drive, which in turn is-a Car:
Another common type of relations is the meronymy relation, written as part-of, that represents how objects combine together to form composite objects. For example, if we extended our example ontology to include objects like Steering Wheel, we would say that "Steering Wheel is-part-of Ford Explorer" since a steering wheel is one of the components of a Ford Explorer.
Facets-these are restriction on slots or role restrictions which state conditions that must always hold to guarantee the semantic integrity of the ontology.For example each slot has a type value or the restricted number of allowed values.Allowed classes for slots of type instances are often called a range of slots.
Instances - are the basic, "ground level" components of an ontology. The individuals in an ontology may include concrete objects such as people, animals, tables, automobile s, molecules, and planets, as well as abstract individuals such as numbers and words. Strictly speaking, an ontology need not include any individuals, but one of the general purposes of an ontology is to provide a means of classifying individuals, even if those individuals are not explicitly part of the ontology.
An ontology provides a common vocabulary for researchers who need to share information in the domain. Some of the reasons to create an ontology are:
¢ To share common understanding of the structure of information among people or software agents
¢ To enable reuse of domain knowledge
¢ To make domain assumptions explicit
¢ To separate domain knowledge from operational knowledge
¢ To analyze domain knowledge
Sharing common understanding of the structure of information among people or software agents is one of the more common goals in developing ontologies (Musen 1992; Gruber 1993). For example, suppose several different Web sites contain medical information or provide medical e-commerce services. If these Web sites share and publish the same underlying ontology of the terms they all use, then computer agents can extract and aggregate information from these different sites. The agents can use this aggregated information to answer user queries or as input data to other applications.
Enabling reuse of domain knowledge was one of the driving forces behind recent surge in ontology research. For example, models for many different domains need to represent the notion of time. This representation includes the notions of time intervals, points in time, relative measures of time, and so on. If one group of researchers develops such an ontology in detail, others can simply reuse it for their domains. Additionally, if we need to build a large ontology, we can integrate several existing ontologies describing portions of the large domain. We can also reuse a general ontology, such as the UNSPSC ontology, and extend it to describe our domain of interest.
Making explicit domain assumptions underlying an implementation makes it possible to change these assumptions easily if our knowledge about the domain changes. Hard-coding assumptions about the world in programming-language code makes these assumptions not only hard to find and understand but also hard to change, in particular for someone without programming expertise. In addition, explicit specifications of domain knowledge are useful for new users who must learn what terms in the domain mean.
Separating the domain knowledge from the operational knowledge is another common use of ontologies. We can describe a task of configuring a product from its components according to a required specification and implement a program that does this configuration independent of the products and components themselves (McGuinness and Wright 1998). We can then develop an ontology of PC-components and characteristics and apply the algorithm to configure made-to-order PCs. We can also use the same algorithm to configure elevators if we "feed" an elevator component ontology to it (Rothenfluh et al. 1996).
Analyzing domain knowledge is possible once a declarative specification of the terms is available. Formal analysis of terms is extremely valuable when both attempting to reuse existing ontologies and extending them (McGuinness et al. 2000).
4.1 Domain ontology
A domain ontology (or domain-specific ontology) models a specific domain, or part of the world. It represents the particular meanings of terms as they apply to that domain. For example the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punch card" and "video card" meanings.
4.2 Upper ontology
An upper ontology (or foundation ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies. It contains a core glossary in whose terms objects in a set of domains can be described. There are several standardized upper ontologies available for use, including Dublin Core, GFO, OpenCyc/ResearchCyc, ,and SUMO.
4.3 Task ontology
Task ontology characterizes the computational architecture of a knowledge-based system which performs a task By a task, we mean a problem solving process like diagnosis, monitoring, scheduling, design, and so on. The idea of task ontology which serves as a system of the vocabulary/concepts used as building blocks for knowledge-based systems might provide us with an effective methodology and vocabulary for both analyzing and synthesizing knowledge-based systems. Task ontology is useful for describing inherent problem solving structure of the existing tasks domain-independently. It is obtained by analyzing task structures of real world problems. Proposal of task ontology has been done in order to overcome the short comings of generic tasks while preserving their basic philosophies. It does not cover the control structure but do components or primitives of unit inferences taking place during performing tasks. The ultimate goal of task ontology research includes to provide a theory of all the vocabulary/concepts necessary for building a model of human problem solving processes.
The determination of the abstraction level of task ontology requires a close consideration on granularity and generality of the unit of problem solving action. These observations suggest task ontology consists of the following four kinds of concepts:
1. Task roles reflecting the roles played by the domain objects in the problem solving process
2. Task actions representing unit activities appearing in the problem solving process,
3. States of the objects, and
4. Other concepts specific to the task. Task ontology for a scheduling task, for example, includes:
Task roles:
"Scheduling recipient", "Scheduling resource", "Due date", "Schedule", "Constraints", "Goal", "Priority", etc. Task actions:
"Assign", "Classify", "Pick up", "Select", "Relax", "Neglect", etc. States:
"Unassigned", "The last", "Idle" etc.
"Strong constraint", "Constraint satisfaction", "Constraint predicates", "Attribute", etc. Actions are defined as a set of procedures representing its operational meaning. So, they collectively serve as a set of reusable components for building a scheduling engine.
Before task ontology has been invented, people tended to understand an ontology is often use-dependent and it has the same shortcoming of the knowledge bases of expert systems, that is, little reusability because of its task-specificity. The idea of task ontology contributes to the resolution of such problems. The reason why an ontology looks task-specific is the ontology mixes up task ontology and domain ontology. Task ontology specifies the roles which are played by the domain objects. Therefore, if a domain ontology is designed after task ontology has been developed, one can succeed to come up with a domain ontology independent at least of the particular task because all the task-specific concepts are detached from the domain concepts to form task-specific roles in the task ontology. A task ontology thus helps develop a use-neutral domain ontology. 4.4 Heavy-weight ontology and light-weight ontology
Another viewpoint suggests us another type of ontology: Light-weight ontology and heavy-weight ontology. The former includes ontologies for web search engines like Yahoo ontology which consists of a topic hierarchy with little consideration of rigorous definition of a concept, principle of concept organization, distinction between word and concept, etc. The main purpose of such a hierarchy is to power up the search engine and hence it is very use-dependent. The latter is different. It includes ontologies developed with much attention paid to rigorous meaning of each concept, organizing principles developed in philosophy, semantically rigorous relations between concepts, etc. Instance models are usually built based on those ontologies to model a target world, which requires careful conceptualization of the world to guarantee of the consistency and fidelity of the model. Upper ontology is a typical ontology of heavy-weight ontology
This section discusses some ontology representation languages. Usually,an ontology development methodology has its own support tool which has a function to generate the ontology and instances in a few ontology representation languages.
5.1 Knowledge Interchange Format
Knowledge Interchange Format (KIF) is a computer-oriented language for
the interchange of knowledge among disparate programs. It has declarative semantics (i.e. the meaning of expressions in the representation can be understood without appeal to an interpreter for manipulating those expressions); it is logically comprehensive (i.e. it provides for the expression of arbitrary sentences in the first-order predicate calculus); it provides for the representation of knowledge about the representation of knowledge; it provides for the representation of nonmonotonic reasoning rules; and it provides for the definition of objects, functions, and relations.
5.2 Resource Description Framework
RDF is a framework for metadata description developed by W3C(WWW Consortium). It employs the triplet model <object, attribute, value>, well-known in AI community, in which object is called resource representing a web page. A triplet itself can be an object and a value. Value can take a string or resource. Object and value are considered as a node and attribute as a link between nodes. Thus, an RDF model forms a semantic network. RDF has an XML-based syntax(called serialization) which makes it resembles a common XML-based mark up language. But, RDF is different from such a language in that it is a data representation model rather than a language and that the XML's data model is the nesting structure of information and
the frame-like model with slots.
5.3 Web Ontology Language(OWL)
Web Ontology Language(OWL) is also a language developed by W3C[OWL]. OWL is designed to make it a common language for ontology representation and is based on DAML+OIL[DAML]. OWL is an extension of RDF Schema and also employs the triple model. Its design principle includes developing a standard language for ontology representation to enable semantic web, and hence extensibility, modifiability and interoperability are given the highest priority. At the same time, it tries to achieve a good trade-off between scalability and expressive power.
Recently, the continuing progress in network technologies and data storage has enabled the digitization and dissemination of huge amounts of documents. The need for more effective information retrieval has lead to the creation of the notions of the semantic web and personalized information management, areas of study that exploit the semantic context of documents to facilitate their management. In many of the proposed solutions in this field, it is common to include the use of an ontology; The Ontology are machine understandable and thus need some means of graphical visualization for humans to comprehend. Visualization tools makes it easy to understand large and complex ontology Important for easy representation of selected parts.That is visualization allows to reduce all possibilities and to show what user wish to view.
6.1 Good visualization tool characteristics
A good visualization tool should show the following features:
¢ Elements of ontology should be displayed.
For a method to be eligible for the visualization of an ontology, it has to support the presentation of ontology ingredients; classes (or entity types), relations, instances, and properties (or slots)
¢ It should be able to load large ontology
¢ Navigational techniques can be used to move to certain region in the large ontology
¢ It should have efficient navigation technique like zoom , browsing option through search etc..
Visualization of ontologies is not an easy task. An ontology is something more than a hierarchy of concepts. It is enriched with role relations among concepts and each concept has various attributes related to it. Furthermore, each concept most probably has instances attached to it, which could range from one or two to thousands. Therefore, it is not simple to create a visualization that will effectively display all this information and at the same time allow the user to easily perform various operations on the ontology.
In the field of ontology visualization, there are several works, mostly in 2D. Apart from the systems that propose visualizations especially tailored for ontologies, there are a number of other techniques used in other contexts such as graph or file system visualization, that could be adapted to display ontologies.
Here we present three visualization tools in Protege [Prot'eg'e Project protege.stanford.edu] in terms of their characteristics and features in relation with a set of requirements compiled for an ontology visualization tool.They are:
¢ Protege class browser
¢ OntoViz
¢ Jambalaya
The visualization of a news paper ontology is done using these tools.
7. Protege class browser
The Class Browser is a simple visualization technique that offers a Windows Explorer - like view of the ontology. In this view, the taxonomy of the ontology (as dictated by the is-a inheritance relationships) is represented as a tree. It displays the class hierarchy with the lower-level nodes presented as a list under their parent and indented to its right. Classes with more that one parents (multiple inheritance) appear under all their parents. The lists of child nodes may be retracted or expanded at will by clicking or double clicking on their parent. A node may be located using the Search feature available, which, however, only locates classes that are already visible.
Advantages and disadvantages
The main advantage is simplicity of implementation and representation and the familiarity to user. It offers a clear view of class names and the hierarchy. In the case of node labels, it has a clear advantage in comparison with almost all the other techniques: there is no label overlap and it is not required to move the mouse over the item in order to view the label.
One problem is that it represents a tree, not a graph. So it only displays is-a relations, not role relations. Role relations are accessible only through slots. Also multiple inheritance cases are not very obvious. Since it being not a graphical representation it places the class under all the parent classes. But it is not always clear to an inexperienced user. Only small portion of ontology is visible at a time. However this performs better than any other visualization tool which is used for hierarchies.
8. OntoViz
OntoViz is another Protege visualization plug-in using a very simple 2D graph visualization method. The OntoViz Tab allows to visualize Protege ontologies with the help of a highly sophisticated graph visualization software called "Graphviz" [graphviz] from AT&T. The types of visualizations are highly configurable and include:
¢ Picking a set of classes or instances to visualize part of an ontology.
¢ Displaying slots and slot edges.
¢ Specifying colors for nodes and edges.
¢ When picking only a few classes or instances, you can apply various closure operators (e.g., subclasses, superclasses) to visualize their vicinity.
The ontology is presented as a 2D graph with the capability for each class to present, apart from the name, its attributes slots and inheritance and role relations. In graph we can see rectangle nodes with different colors. The instances are displayed in different color. Class hierarchy is represented by placing child nodes under parent nodes linked with is-a link and Multiple inheritance by placing child nodes under all parent nodes. It is possible for the user to choose which ontology elements will be displayed from the configuration panel on the left. Right-clicking on the graph allows the user to zoom - in or zoom - out. No keyword search is provided in this tool.
To create a graph, select a class from your ontology in the Classes pane, click the "add class" button in the upper left "Config" area of the tab. To fine tune graph (e.g., for showing only a part of ontology), the following options are available:
¢ sub - subclass closure
¢ sup - superclass closure
¢ slx - slot extension
¢ isx - inverse slot extension
¢ slt - slots
¢ sle - slot edges
¢ ins - instances
¢ sys - system frames
Check several of the options and click the "Create Graph" button. The same process can be used for creating graphs of instances by using the "add instance" instead of the
"add class" button. To remove an entry from the Config table, use the "remove class" button.
Figure 3.OntoViz visualization of newspaper ontology
Advantages and disadvantages
It is most suited to answer structural and trends related questions.But it is making inefficient use of screen space,only less than 1000 nodes can be seen effectively in a screen. OntoViz visualization received very negative reactions in evaluations. It attempts to alleviate the problem of node clutter by allowing the user to select the nodes she/he would like to display, along with their subhierarchies or related nodes, through a configuration panel. However, several interaction issues seemed to lead to a rather bad performance.
All users commented on the lack of interaction and had experienced problems with the navigation, such as having to drag the scrollbars to navigate. Furthermore, the zoom in and out commands and clicking of the item on focus. They found the presentation "poor" and "chaotic" and commented on the lack of a search tool and the fact that some labels are not fully visible, forcing the user to guess their meaning; absence of sorting (instances are not presented in alphabetical or any other deterministic order) was also negatively commented. However, some users commented that the visualization could be effective for smaller ontologies or if the user is very familiar with the ontology, as it seemed to them useful for the presentation of hierarchies.
9. Jambalaya
Jambalaya is a visualization plug-in for the Prot'eg'e ontology tool that uses the SHriMP (Simple Hierarchical Multi-Perspective) 2D visualization technique. SHriMP uses a nested graph view and the concept of nested interchangeable views. It provides a set of tools including several node presentation styles, configuration of display properties and different overview styles.
According to this method, nested nodes are used to express the inheritance relations between the classes, as sub-classes are nested inside parent classes. Instances are also represented as nested nodes in their corresponding class in the graph. Instance nodes are distinguished from the class ones by their color. Role relations between classes or instances are represented in the graph using directed links between the related nodes. Users may navigate in the ontology through this visualization utilizing the selection and zoom tools. When a class or instance is selected by zooming on it,the SHriMP view focuses (using a focus technique with animation) on the selected node of the nested graph.
When the class or instance is double-clicked, the view focuses on the clicked node and opens a form with the node information, embedded in the visualization. The visualization also offers extra navigation buttons like "back" or "home". Jambalaya contains a more advanced keyword search than the other methods, allowing the user to search the whole ontology (classes and instances alike) or limit the search scope by specifying the type of the searched item. Search refinement is also available by searching within the results.
In addition to using arcs to show relationships as connections (arcs) between classes and instances, the user may show a relationship type using containment. For example, rather than drawing arcs between nodes to show the is-a relationship, we can instead nest subclasses within their superclasses. Furthermore, it may also be advantageous to use other user defined slot types for nesting nodes. For example, in an anatomy ontology, the user defined part-of relationship may be a more appropriate relationship for nesting nodes than the is-a relationship.
Advantages and disadvantages
Jambalaya in general got positive reactions. Most users commented positively on the effective search tool and the animated transition when double clicking on an instance or class. They liked "flying together with the visualization to locate the information." Some noted that they would like the animation to be faster ("I lose time waiting") or slower ("not enough time to understand the transition") or to display the steps of the transition to the side. It was interesting that none of the users tried to use the visible relation links and almost all noted as a negative point the appearance of the links and the fact that after browsing some classes there come to be so many relation links that they obstruct the view to the visualization. They also noted that labels overlap in the case of many instances.As in Jambalaya, users had a problem knowing which is the current parent node that had been zoomed in, or if the node had already been visited.
Based on ontology visualization characteristics, this section attempts an analysis of tasks related to ontologies, with the aim of assessing which visualizations best support each task type. Shneiderman [1996], presents seven high-level tasks that an information visualization application should support. These are the following:
1. Overview. Gain an overview of the entire collection.
2. Zoom. Zoom in on items of interest. When zooming, it is important that global context
can be retained.
3. Filter. Filter out uninteresting items.
4. Details-on-demand. Select an item or group and get details when needed.
5. Relate. View relationships among items.
6. i/istory.Keep a history of actions to support undo, replay, and progressive
7. Extract. Allow extraction of subcollections and query parameters. This extraction
refers to saving desired subparts of the collection and is typically supported by the
ontology management tools, not the visualization methods per se.
Not all tasks can be effectively supported through a single visualization. This fact supports the view that more than one visualization method should be made available to ontology designers and users. Furthermore, not all tasks may be supported by visualization, thus supplemental information retrieval aids should be provided. Locating a specific node, for example, may be accomplished by browsing the ontology, using the visualization, but it is much quicker and more effortless to do so using a search tool. This fact was proven in Katifori et al. [2006a]. Cardinality-related tasks, for example, finding the number of class siblings or children, can be performed using the visualization alone, but the user would have to count the nodes; certain tools facilitate these tasks by providing the numbers (by default or on request), but these facilities are strongly tool-dependent, rather than visualization method-dependent.
"Going back to a previously visited node" could be supported by the tool if it provided an elaborate history mechanism, but also by the visualization. If the visualization supports learning of the ontology structure and the creation of a mental image, then the user may easily return to previously visited nodes. Methods that are more effective to this end are the ones that maintain a constant positioning of the nodes and allow quick browsing at the same time. Last, tasks like "Forwards-Back" or "Initial View" are solely tool-related.
Navigation and Interaction Issue
All static hierarchical presentations have liimits as to the quantity of nforma tion they are capable of presenting on a finite display space Babaria [2004]. When these limits are reached, navigational techniques must be used, creating the potential for loss of context. In most visualizations, depending upon the drawing algorithm and the size of the display space, a hundred or so nodes can be adequately represented on screen without the need for panning or zooming. The various visualization techniques differ in the level of interaction they offer to the user. Some of the methods allow the user to only view the presented ontology as a static image. Others allow the retraction and expansion of nodes, the movement and rotation of the presented ontology, zooming or clicking to change hierarchy level or the node on focus. Other, mostly tool-related, features are history functionalities, overview windows, and the use of animated transitions.
All these features are useful for exploring the ontology to find specific nodes, focus on nodes of interest, or examine relations between nodes. Retraction and expansion of nodes, viewpoint movement, and rotation, and zooming, are features that most of the visualizations support, since they are necessary to navigate hierarchies with more than a hundred nodes. In these cases, the interaction techniques used are essential for the success of the visualization as they greatly affect task completion.
Zooming is another important issue. According to Plaisant et al. [2002], semantic zooming is preferred over geometrical scaling; it is important to provide the user the means to focus on specific nodes and be able to view their details, not just scale the visualization as an image. Another issue with zooming is the loss of the sense of where the user is and where she/he came from. As already mentioned, navigational cues such as informing the user of the current level of the hierarchy and the path she/he followed to get there are essential to this end.
Another useful feature is Overview tools and Back and Forward navigation aids. Overview tools are especially effective in zoomable visualizations where the user may easily lose sense of his/her position. "Back" and "Forward," on the other hand, allow the user to retrace his/her steps during browsing. Movement and rotation of the graph is another interaction feature that should be carefully designed. Although it allows the user to manipulate and examine the ontology in order to locate specific nodes or areas of interest, it may disorient the user. Furthermore it does not help the creation of a cognitive model of the ontology as nodes continuously change position. This is also the case of animated transitions. They are used as a means to change the view while zooming, rotating the graph, expanding or retracting, focusing on another part of the ontology and so on, while helping the user to understand the change and retain a clear picture of his/her previous and current locations in the graph. However, the reaction of the users to it is not always positive and it may be conflicting. In the case of its use for moving automatically from one place to the other, the user may find the animation useful because it shows the transition path, or annoying because it is time consuming.
On the whole, interaction and navigation techniques are essential for the success of a visualization method. They form an integral part of the method, as without them the visualization would be a static image. More research and evaluations are needed in order to couple visualization and interaction effectively to create a useful and easy to use tool. Scalability issues
Current systems tend to avoid the problem of scalability by limiting the number of visible items to about 10000. Ontosphere for example reports problems with many nodes (more than 1000) such as occlusion and label overlap. According to Fekete and Plaisant [2002], control panels, labels, margins, waste space, and data structures are not optimized for speed, and the graphics libraries they employ are not sufficient.
Another issue in big ontologies is that of the node labels display, especially important in an ontology, which is basically composed of concepts that the user should be able to read to understand. Fekete and Plaisant [2002] state that text labels are not preattentive but nevertheless important to understand the context in which visualized data appear. Labeling each item cannot be done statically on a dense visualization.
The visualization of relation links is also problematic and the display may become cluttered very quickly. Both TGVizTab and OntoViz became impossible to use when relation links were visible, even for an ontology for less than 300 nodes. In Jambalaya too, users did not exploit the relation links”they even seemed to hinder them. A solution to the problem of relation link clutter is not to display them all on the graph but rather allow the user to select which ones to display. Several visualizations like the 3D Hyperbolic Browser, Jambalaya, OntoViz and TGVizTab, support this.
Techniques based on zooming, which use different node sizes for the representation of the lower levels, also become illegible as the number of nodes increases. The zoomable techniques that do not visualize all the levels at the same time may become difficult to navigate after a point. The reason is that when the number of nodes and hierarchy levels increases, it becomes more and more difficult for the user to keep track of his/her position.
The more efficient techniques for large ontology sizes are most probably the techniques that use distortion or expansion and retraction of the nodes, because they can provide detail, maintaining at the same time the general impression of the context. Van Ham and VanWijk [2002] propose three solutions to the problem of visualization of many nodes:
1. Increase available display space, by either using three dimensional and/or
hyperbolic spaces.
2. Reduce the number of information elements by clustering or hiding nodes.
3. Use the given visualization space more efficiently by using every available pixel. Such solutions have been employed by most of the presented visualizations with varying degrees of effectiveness.
On the whole, as Munzner [1997] also states that information density should not be the only metric in ontology visualization: when taken too far, it becomes a clutter.Drawing for example all the links in a highly connected graph yields a picture that can give a high level overview of the global structure but is useless for examining the details. There is always a trade-off between maximum number of nodes displayed and clarity and details in the visualization. Allowing the user to configure the visualization according to his/her needs and the related task is probably the best solution possible. Reasoning
A very important issue related to ontologies, which are mainly knowledge representations, is that of reasoning. An ontology is more than a simple graph, it is a structure with rich semantics and the ability to use logic operations on it so as to reach conclusions and produce new information. The issue of coupling visualization and reasoning has not yet been sufficiently treated in existing literature and very few methods support it. OntoTrack, for example, has a connection with an external Reasoner in order to detect problems while editing, which are outlined with red on the visualization. OZONE on the other hand, as a visual query tool allows the user to extract information from the ontology. However, this issue should be further investigated in order to create visualizations that will support all the ontology features more effectively.
Much work has been done in the field of graph and hierarchy visualization both in 2D and 3D. The visualization of ontologies is a particular subproblem of this area with many implications due to the various features that an ontology visualization should present. As the results of researches done so far in this area it imply, there is not one specific method that seems to be the most appropriate for all applications and, consequently, a viable solution would be to provide the user with several visualize tions, so as to be able to choose the one that is the most appropriate for his/her current needs.
Furthermore, an important conclusion of most of the evaluations taken into account for this work is that visualizations should be coupled with effective search tools or querying mechanisms. Browsing is not enough for tasks related to locating a specific class or instance, especially for big ontologies. Most users also seem to dislike chaotic and too cluttered overviews, and tend to prefer visualizations that offer the possibility of an orderly and clear browsing of the presented information, even if in some cases it requires focusing on a specific part of the ontology or hierarchy. This fact implies that visualizations should also take advantage of the semantic context of the information and even the user profile, in order to guide and support the hierarchy or ontology exploration.
In some applications it is preferable or more convenient to provide only a single visualization of the ontology. In this case the designer has to make a choice among the available methods, based on certain characteristics of the ontology, the application, the user profile, expertise, and so forth. It is hoped that the current work will be useful in order to make that choice.
[1] Ontology Visualization Methods”A Survey ,Akrivi Katifori and Constantin Halastis University of Athens and Lepauras, CostasVAssilakis, and Eugenia Giannopaulo University of Peloponnese, ACM Computing Surveys,Vol. 39, No. 4,Article 10,Publication date: October 2007
[2] A Comparative Study of Four Ontology Visualization Techniques in Protege: Experiment Setup and Preliminary Results , Akrivi Katifori and Constantin Halastis University of Athens and Lepauras, CostasVAssilakis, and Eugenia Giannopaulo University of Peloponnese
[3] Protege project and implimentation , Stanford University protege.stanford.edu/doc/ users.html#tutorials
[4] en.wikipediawiki/Ontology_(computer_science)
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