Virtual Reality Ontology Object Manipulation (VROOM)

Margarita Vinnikov

, Daniel Vergilis

, Uras Oran

and James Geller

Department of Informatics, New Jersey Institute of Technology, Newark, U.S.A.

Department of Data Science, New Jersey Institute of Technology, Newark, U.S.A.

Keywords:

Virtual Reality, 3D, Medical Ontology.

Abstract:

Biomedical ontologies are considered important repositories of knowledge about the medical domain and

related ﬁelds. They are best thought of as node-link networks, where each node represents one single (medical)

concept and links express binary relationships between pairs of concepts. The most important relationship

(“IS-A”) is used to form a generalization hierarchy among the concepts. Visualizing and manipulating such a

network when it includes more than a few hundred nodes can be challenging. This paper presents a new system

called VROOM (Virtual Reality Ontology Object Manipulation) that supports browsing and interaction with a

biomedical ontology in a virtual 3-D space, enabling more natural and realistic interaction with and navigation

through the ontology network.

1 INTRODUCTION

Biomedical ontologies are considered important

repositories of knowledge about the medical domain

and many related ﬁelds. The BioPortal web repository

(Stanford NCBO, 2021) contains 891 such ontolo-

gies and terminologies. Medical ontologies are best

thought of as node-link networks where each node

represents one single (medical) concept and links ex-

press binary relationships between pairs of concepts.

Ontologies may be represented as text or as diagrams.

Text is often inadequate because it hides important

structural features of the concept networks, e.g., when

a concept has several parent concepts. However, on-

tologies are usually entered into a computer by us-

ing a text-oriented editor. The most popular ontology

editor in the US is Protege, also created at Stanford

(Stanford Biomedical Informatics Research, 2021).

Diagrams are preferable for small ontologies, as

they show structural features better than text. How-

ever, ontologies are most useful when they represent a

large slice of a domain of the real world. Furthermore,

they have to follow certain structural rules. Thus, real

world ontology diagrams are large in size and com-

plicated in structure. This makes the diagrams over-

whelming and the ontologies difﬁcult to understand.

As text is an even worse option, we are looking for

methods to make the diagram representation useful

even for large and complex ontologies.

In order to carry over the advantages of graphical

network representations to large, realistic ontologies,

we propose to make use of the power of adding a third

dimension and Virtual Reality (VR) to the graphical

display. We have designed and implemented a new

system that supports ontology exploration and manip-

ulation in interactive virtual space. Section 2 reviews

related work. In Section 3, we introduce the Virtual

Reality Ontology Object Manipulation (VROOM) ap-

plication that we have developed for Oculus headset

technology. Section 4 mentions limitations and Sec-

tion 5 contains our conclusions.

2 RELATED WORK

The idea of adding a third dimension to the visualiza-

tion and manipulation of network diagrams has been

investigated before, e.g., by (Robertson et al., 1991),

who created rotating cone trees, which added the pos-

sibility of dynamic manipulation to the display. How-

ever, the resultant image was still projected onto a 2-D

plane and was not optimized for medical ontology dis-

play, which requires the layout of many concepts with

multiple parents. Furthermore, manipulation of the

cone tree was accomplished with standard computer

hardware (mouse and keyboard) and the viewing an-

gle of the user was limited to the size of her screen.

(Morrey et al., 2009) created a hybrid display method

for medical ontologies. By dividing the screen into a

focus concept and its neighborhood, with easy navi-

Vinnikov, M., Vergilis, D., Oran, U. and Geller, J.

Virtual Reality Ontology Object Manipulation (VROOM).

DOI: 10.5220/0010787700003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 361-368

ISBN: 978-989-758-552-4; ISSN: 2184-4305

361

gation to other focus concepts, they implemented the

Neighborhood Auditing Tool (NAT). NAT was suc-

cessful at viewing localized areas of an ontology, but

it did not provide the “big picture view” of the whole

ontology structure. A different approach to ontology

visualization was taken by (Zheng et al., 2017a; Ochs

et al., 2016; Zheng et al., 2017b). They developed

several summarization methods for medical ontolo-

gies, including a multi-granularity display approach.

While this created much better visualizations, the dis-

plays were not intuitive without studying the theory

of abstraction networks (Wang et al., 2012; Halper

et al., 2015), which cannot be expected from casual

users. There is no evidence that these methods have

been adopted by the community.

To overcome these issues in our project, we nav-

igate the ontology structure in 3-D using a VR ap-

proach. Thus, the viewer is not limited to a ﬂat sur-

face with a graph layout on a screen, but can liter-

ally ”walk through” the knowledge structure. Modern

VR goggles and powerful driver computers can cre-

ate a good approximation of this navigation in 3-D

space. Furthermore, users have ambidextrous access

with two hand-held controllers to virtual objects (con-

cept nodes, relationships) as opposed to using one

mouse, making it easier to manipulate the objects in

a natural manner. Controllers also have more buttons

than a standard three-button mouse, adding degrees of

liberty to the manipulation.

2.1 Virtual Reality (VR)

In recent years, VR has become a rapidly develop-

ing technology that has been used in many industries

and domains (Chuah, 2018). Partially this can be at-

tributed to the fact that the head-worn devices have

become lighter and by a magnitude cheaper than the

traditional VR technology. Both VR and Augmented

Reality (AR) solutions have been widely used in the

medical ﬁeld (Roberts et al., 1986; Pensieri and Pen-

nacchini, 2016), e.g., for medical diagnosis, medical

procedures, education, etc. Thus it is natural that VR

technology could be extended to medical ontology ex-

ploration and management. There are numerous ben-

eﬁts of the VR paradigm, such as the utilization of

the space around the user and natural models of inter-

actions that are not available in standard PC settings.

Yet moving a data visualization from a 2-D paradigm

into VR can be challenging, as this is a novel and

emerging technology. Furthermore, there are no es-

tablished solutions for such applications. Nonethe-

less, we view our work as part of the Situated An-

alytics (SA) (ElSayed et al., 2015) framework, that

utilizes the idea of data representation in relevance to

people, objects, and places for “understanding, sense-

making, and decision-making” (Thomas et al., 2018).

Such approaches have proven to be beneﬁcial for spa-

tial memory tasks (Gao et al., 2018) and analytical

tasks (Ens and Irani, 2016). Yet, in order for such an

application to be successful, it has to use an appro-

priate metaphor that supports natural and intuitive in-

teractions. For example, the curved “virtual cockpit”

(Ens et al., 2014) supports different types of content

to be spaced out around the user, such that she can

easily interact with different content objects utilizing

the 3-D space around her. Similarly, Liu et al. (Liu

et al., 2020) have explored an adaptation of 2-D visu-

alizations into 3-D VR. They incorporated a “shelves”

metaphor to organize data into various conﬁgurations.

Their user studies revealed that for small sets of data

visualization items, a ﬂat layout is preferable. On the

other hand, with an increasing number of visualiza-

tions, the user’s preference changes to a semi-circular

layout, as this better utilizes the properties of the vi-

sual ﬁeld and minimizes the distance between the user

and each displayed visualization.

The design of a visualization method needs to

keep in mind the task(s) expected of its users. Dif-

ferent affordances are required when a pure browsing

task is provided for, as opposed to an environment

that requires extensive manipulation of the VR ob-

jects. Visualizations of ontologies are often created

for purposes that go beyond comprehension and in-

clude error detection, error correction, and periodic

scheduled extension with new content (e.g., concepts

for new diseases, such as COVID). For this purpose,

a VR environment has the additional advantage that

objects in the virtual world can be manipulated with

two hands using appropriate multi-button manipula-

tors, as opposed to the single-handed use of a com-

puter mouse. To achieve these ambidextrous 3-D af-

fordances, we have implemented a software system

that allows for navigation through ontologies and di-

rect manipulation of its nodes and links with realistic

virtual tools such as scissors and glue sticks.

3 IMPLEMENTATION

VROOM provides a convenient process of displaying

and manipulating large data structures in a VR en-

vironment. The system can be used to visualize ex-

tensive biomedical ontologies and allows users to ex-

amine and interact with the ontology objects through

real-world metaphors.The system provides the best

user experience when a user wears an Oculus Rift or

a Quest headset. The software utilizes Unity’s XR

Interaction Toolkit Package for VR support (Unity

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362

Technologies, 2020). The following subsections will

review the implementation speciﬁcs of the key fea-

tures of the system.

3.1 Graph Structure

In most ontologies, the entities are referred to as con-

cepts or classes. The most important kind of relation-

ship is called the IS-A relationship. It is drawn as an

arrow that expresses that the concept at its tail end

is more speciﬁc than the concept at its head. Thus

“Lung” is more speciﬁc than “Organ,” and we say that

“Lung IS-A Organ” when an IS-A relationship exists

between these two concepts. Concepts and IS-A re-

lationships together either form a tree or a Directed

Acyclic Graph (DAG).

In a tree, every concept has exactly one parent,

except for a concept called the root, which has no

parents. Tree-shaped ontologies are computationally

easier to deal with, but they do not reﬂect the world

well. In a DAG, every concept may have one or more

than one parent, except for the root(s). To express the

fact that Viral Pneumonia is both a Viral Disease and

a Lung Disease, it is necessary to assign two parents

to Viral Pneumonia (Figure 1). Even in most DAG

ontologies, there is a single root.

Figure 1: The ontology concept Viral Pneumonia with two

parents.

Besides IS-A relationships, an ontology may have

hierarchical part-of relationships and other so-called

“lateral” relationships (location-of, causes, treats,...).

As these greatly complicate processing, we will omit

them in this paper.

3.1.1 Graph Design

To provide easy and clean implementation of the net-

work visualization, we divided the implementation

into two parts. The back end supports reading in on-

tologies and saving them to a ﬁle. It handles node

storage, manipulation, and search. The front end

modules are responsible for navigation, user inter-

face (UI), and interaction with Node Objects and the

connecting edges. This approach supports easy code

maintenance and readability and minimizes the num-

ber of bugs. Figure 2 shows that both nodes and links

(or as they are formally known in graph theory, edges)

are implemented as classes and are inheriting from a

generic graph class that is responsible for basic mod-

iﬁcation and enumeration of the data structure. The

nodes and edges are responsible for maintaining in-

dividual connections. Speciﬁcally, the node objects

store the properties of the ontology data and keep a

list of edges connected to them in an adjacency list,

which is more efﬁcient than the adjacency matrix ap-

proach. This is the case because most ontologies are

sparse, i.e., they do not nearly have as many as the

possible n

relationships. Since the edges are also

deﬁned as objects, they can store information about

the relationship, including the start and end nodes,

weight, and (bi)directionality, and store custom prop-

erties pertaining to the speciﬁc ontology, when nec-

essary. For this version of the VROOM system im-

plementation, a Tree class was deﬁned as a subset of

the generic graph to facilitate certain layout manipu-

lations described below. With a tree structure, data is

guaranteed to be composed of nodes with a single par-

ent (except for the root) and n-children with unidirec-

tional relationships, which can be taken advantage of

with features such as sub-tree collapsing/expanding.

Yet, by using a generic graph data structure under-

neath a VR-focused visualizer, we have a platform to

build additional features into future versions.

A proper layout must be used to provide the best

visualization experience while taking advantage of

the 3-D space. An optimal layout would utilize as

much space around the user as possible to minimize

any movement required in VR. Hence, a radial lay-

out is best to satisfy these requirements. Thus, we

follow the previously mentioned cone tree paradigm,

but in VR. Speciﬁcally, to create the radial layout,

the root node is ﬁrst placed at the center of the vi-

sual space (Figure 3). The area around the root node

is allocated in sectors to each of its children based

on their numbers of descendants, similar to the al-

gorithms described in the Related Work Section on

the hyperbolic browser (Lamping and Rao, 1996). In

polar coordinates, each child of the root is placed in

the middle of its sector between (θ

, θ

) and at a dis-

tance r away from the root, where r increases at each

depth. This algorithm is applied recursively through

each sub-tree, where the sector bounds of the sub-

tree lie within the bounds deﬁned by its parent. Each

level of the radial layout is farther away from the root

plane to bring the layers visually closer to the user. A

feature of this layout algorithm is that descendants of

Virtual Reality Ontology Object Manipulation (VROOM)

363

Figure 2: Graph Class Diagram.

sub-trees spread out more as the depth increases since

the radius of each level naturally increases. We can

also curve this layout around the user to maximize the

data perceptible in the user’s ﬁeld of view.

Figure 3: An ontology in a radial layout tree form as seen

from afar.

3.2 VR Features

In order to make the user experience as naturalistic

and intuitive as possible, we have implemented the

following features, describe in the following subsec-

tions: The virtual platform 3.2.1 includes the cart

3.2.2, the scope 3.2.3, the scissors 3.2.4, the glue tool

3.2.5, the ﬁle reader 3.2.6 and the garbage bin 3.2.7.

The system implements interaction features describe

in Subsection 3.3.

3.2.1 Virtual Platform

The virtual platform serves the user as a working

space. It includes many virtual items that she can uti-

lize while working with an ontology, such as a cart

(section 3.2.2) that can contain nodes and other in-

teraction tools, and a garbage bin (section 3.2.7) that

has all the nodes that (temporarily) do not connect to

any edges. The second purpose of the virtual platform

is to move/ﬂy through the ontology for browsing and

exploration or for advancing from node to node when

speciﬁc paths or nodes are of interest. Movements in

VR space can be performed by using the two hand-

held Oculus controllers.

3.2.2 Cart

The cart metaphor is one of the key ideas in the

VROOM system, and most of the user interactions

revolve around the cart. The ﬁrst functionality of the

cart is to collect/store nodes that have been detached

from a tree and need to be moved to a different lo-

cation. The second function associated with the cart

is that it anchors all other direct manipulation tools,

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364

as shown in Figure 4. These will be discussed in the

following subsections. Furthermore, there are buttons

on the cart that “teleport” any lost tools back to their

initial positions. Finally, several additional buttons on

the cart allow opening a ﬁle dialog to load ontology

ﬁles from disk and save modiﬁed ontologies back to

permanent storage.

Figure 4: The cart that holds the user’s tools and can carry

any nodes of interest. Nodes that are grabbed from the en-

vironment and placed into the cart are carried with the user.

Tools from left to right: scope, glue, and scissors.

3.2.3 Scope

The scope tool (Figure 5) is used to examine nodes at

a distance, so the user does not need to move closer

in VR space to determine whether they are of interest.

This is useful when the user needs to decide which

sub-tree she needs to dig deeper into, and it is more

convenient to make this decision from one spot than

to travel to multiple locations while searching for the

correct node(s). The scope has a dynamic variable

zoom feature that allows for the examination of ob-

jects at varying distances. This feature uses the UI

slider to change the ﬁeld of view of the camera. When

adjusting the slider, the scope will zoom in and out

(codemeariver, 2017).

A special texture, called a render texture, allows

the camera to project its vision onto an entity mod-

eled with the texture. The texture from a renderer is

set as the target texture for the camera in the scene to

vary the size of the ﬁeld of you of the scope. Hence,

by varying the distance of a secondary camera, one

can change the size of a distant object that one is in-

terested in examining.

3.2.4 Scissors

The scissors tool is an object that can be accessed on

the cart by the grab function on one VR controller.

The scissors script is responsible for cutting edges be-

tween nodes. The cutting action is displayed as an an-

imation when the user pushes the appropriate button

on the VR controller. Cutting an IS-A link is done

Figure 5: The scope zoomed in on a speciﬁc subset of the

ontology.

with the following steps involving the scissors tool:

1. Pick up the scissors with the grab button on the

VR controller (back trigger is used for this).

2. Hover over an edge (IS-A link), causing the edge

to be highlighted in red.

3. Perform the trigger selection by pressing the trig-

ger to cut the highlighted edge.

3.2.5 Glue

The glue tool is an interactable object that can be ac-

cessed on the cart by the grab function on a VR con-

troller. The tool functions as the inverse of the scissors

and is responsible for the creation of a new edge be-

tween two nodes when performing the gluing action

with the trigger on the VR controller.

Creating a relationship between two nodes by in-

serting an edge between them can be done using the

glue tool as follows:

1. Pick up glue tool with the grab button on a VR

controller.

2. Hover with the glue over a node, causing it to be

highlighted in white.

3. Perform the trigger selection by pressing and

holding the trigger to create the parent end of the

edge.

4. While still holding the trigger, move to the desired

child node of the relation and release the trigger

button while hovering over the second node (the

selected child node).

5. The two nodes should now have a connecting IS-

A link from the child to the parent.

3.2.6 File Reader/Ontology Loader

Loading an ontology is done by opening the ﬁle ex-

plorer located at the center of the cart and selecting

Virtual Reality Ontology Object Manipulation (VROOM)

365

a ﬁle to open by pressing the trigger on the VR con-

troller. The ﬁle reader takes a .csv ﬁle that contains

the ontology data and loads it into a graphical tree

structure in the VR environment.

Figure 6: File explorer UI.

3.2.7 Tree Validation and Garbage Collection

Ontology trees are validated when loaded, and any

orphaned or invalid nodes are automatically placed

in the garbage bin. Nodes can also be put into the

garbage bin using the VR controllers. The user can

review the list of nodes places inside the garbage bin

to decide whether to keep a node there or retrieve it

into the working space to ﬁnd an appropriate parent

and/or child(ren) for this speciﬁc node.

3.3 Interaction

There are four different types of interactions that the

user can engage in:

• Raycast: A hand that uses the XRRayInterac-

tor(Unity Technologies, 2019) to allow for manip-

ulation of interactable objects in Unity.

• Typing: A version of the Raycast hand that is in-

visible and shortened to the length of the pointer

ﬁnger for easier use with UI interactions not re-

quiring hands.

• Interacting Hand: An action-based controller

that uses the direct interactor to interact with the

interactable objects.

• Hand Controller (Device-based): This hand is a

part of the Hand Controller Uses Hand controller

and is used for the sole purpose of animating

the Custom hands from the Oculus VR package

with the Hand Anim script (Facebook Technolo-

gies, ). Set the interactor’s Select Action Trigger

to State to have this fully working with the anima-

tion script.

This component is put on the Oculus VR package’s

hand model and is then set up using the animator from

that same package and the Device-based controller

mentioned in the previous section.

Figure 7: A pair of hands that are controlled in VR for ontol-

ogy manipulation. Hand model from the Oculus Integration

package (Facebook Technologies, ).

3.3.1 Gestures

We have implemented two gestures – an expand ges-

ture and a collapse gesture. Speciﬁcally, nodes can be

collapsed into groupings to minimize the total num-

ber of nodes onscreen at the same time. Collapsing

parts of the tree structure only impact the ontology

visually. Collapsing and expanding are complemen-

tary gestures and have a similar operational concept.

When both triggers on a controller are pulled, the

midpoint of the two hands is found. If this midpoint is

within a speciﬁc range of a Node or NodeGroup, then

that object is selected. Then, the distance between

the two hands is checked to ensure that they are far

enough apart (in the case of the collapse gesture) or

close enough together (in the case of the expand ges-

ture). If the user’s hand placement is valid, the corre-

sponding gesture is started, and the selected object is

highlighted. Whenever the triggers are released, the

midpoint of the hands is again calculated, and the dis-

tance to the selected object is checked; if the mid-

point is too far from the selected object, then the ges-

ture is canceled. Otherwise, the distance between the

hands is checked to ensure that they are close enough

together (in the case of the collapse gesture) or far

enough apart (in the case of the expand gesture). If the

user’s ﬁnal hand placement is valid, the active gesture

is completed, the action is performed, and the object

stopped being highlighted. Whenever a gesture is in

progress, the midpoint of the hands is calculated, and

the distance to the selected object is checked. The

highlight is removed if the midpoint is too far from

the chosen object; otherwise, the object remains high-

lighted. This is to provide visual feedback for the cur-

rent status of the gesture.

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366

Figure 8: Left: Branch with subtrees. Right: Same branch

with some collapsed subtrees (the collapsed nodes are in

red).

3.3.2 Keyboard

A 3-D keyboard was created to efﬁciently change

node property data and perform search queries while

using VR controllers. The keyboard automatically ap-

pears when a textbox is selected with the VR con-

trollers and allows users to type directly into the

textbox without needing to switch to a physical key-

board. The keyboard disappears when typing is com-

pleted in order not to clutter up the environment. This

feature is also used for modifying ontology data or

performing search queries on the ﬂy while remaining

in the VR environment.

3.4 Search

Search functionality is one of the critical features of

the entire ontology manipulation system. The search

is implemented using a UI window where the user can

enter keywords she is interested in by utilizing the vir-

tual keyboard. Nodes with the corresponding terms

are highlighted in red in the VR space for easy recog-

nition. An inverted index was created on the nodes’

data properties to ﬁnd appropriate matches quickly.

The user is then can be teleported to the required node

to continue the intended investigation.

4 RESULTS

To test the functionality of VROOM, we needed a

moderately sized biomedical ontology that would be

considered to be of “wider interest.” CIDO, the Coro-

navirus Infectious Disease Ontology, fulﬁlled both

these requirements (He and et al., 2020). CIDO is

available for download at (Stanford NCBO, 2021).

Currently it contains 8,775 classes. Due to the ur-

gency caused by the COVID pandemic, CIDO has

been growing rapidly. At the time when we down-

loaded it, its size was slightly above 6,000 classes.

With CIDO we ascertained the functionality of the

tools described above, however, response times were

slow. Subsequently we developed a subset of CIDO

for experiments with human subjects. A partial view

of this subset is shown in Protege format in Figure 9.

After loading CIDO into Protege we exported it into

a .csv ﬁle that we used as the load ﬁle for VROOM.

Figure 9: A subset of CIDO in Protege format.

4.1 Future Work

We are currently in the process of conducting a user

study in terms of evaluating the system’s usability and

functionality. Preliminary results show that the users

enjoyed the system. During a recall test, one user

stated that he was visualizing the ontology in the for-

mat that he had seen in VR in order to answer the

questions. This is a promising observation and we

plan to have quantitative results in a forthcoming pa-

per.

5 CONCLUSIONS

In this paper, we have demonstrated an example of

a biomedical ontology visualization system based on

VR. We have leveraged the power of VR to make

the interaction modalities as natural as possible. Our

long-term goal is to extend VROOM for multi-user

interaction to enable collaborative ontology editing in

VR. We will increase the number of available gestures

that a user can utilize to interact with the system in

the near term. We will also add new features, such as

more sophisticated search and navigation functionali-

ties. We are currently preparing a usability study with

students and medical experts to evaluate VROOM’s

key features with novices and expert users alike.

ACKNOWLEDGEMENTS

Thanks to the development team of the Virtual On-

tology: Jordan Sefa, Uras Oran, Jacob Hayne, Daniel

Virtual Reality Ontology Object Manipulation (VROOM)

367

Vergilis, Tyler DeMarco, and Ahmed Zaheen Abdul

Farid, Nicholas Patterson, and Pranathi Pothukanuri.

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