=Paper=
{{Paper
|id=Vol-2893/paper_3
|storemode=property
|title=Logic Graphs: Complete, Semantic-Oriented and Easy to Learn Visualization Method for OWL DL Language
|pdfUrl=https://ceur-ws.org/Vol-2893/paper_3.pdf
|volume=Vol-2893
|authors=Ngoc Than Nguyen,Ildar Baimuratov
|dblpUrl=https://dblp.org/rec/conf/micsecs/NguyenB20
}}
==Logic Graphs: Complete, Semantic-Oriented and Easy to Learn Visualization Method for OWL DL Language==
https://ceur-ws.org/Vol-2893/paper_3.pdf
Logic Graphs: Complete, Semantic-Oriented and
Easy to Learn Visualization Method for OWL DL
Language
Than Nguyena , Ildar Baimuratova
a
ITMO University, Kronverksky Pr. 49, bldg. A, St. Petersburg, 197101, Russian Federation
Abstract
Visualization of an ontology is intended to help users to understand and analyze the knowledge it con-
tains. There are numerous ontology visualization systems, however, they have several common draw-
backs. First, most of them do not allow representing all required ontological relations. We consider the
OWL DL language, as it is sufficient for most practical tasks. Second, they visualize ontological struc-
tures just labeling them, not representing their semantics. Finally, the existing ontology visualization
systems mostly use arbitrary graphic primitives. Thus, instead of helping a user to understand an ontol-
ogy, they just represent it with another language. In opposite, there are semantic-oriented visualization
systems like Conceptual graphs, but they correspond to First-order logic, not to OWL DL. Therefore, our
goal is to develop an ontology visualization method, named “Logic graphs”, with three features. First,
it should be complete with respect to OWL DL. Second, it should represent the semantics of ontolog-
ical relation. Finally, it should apply existing graphic primitives, where it is possible. Previously, we
adopted Ch. S. Peirce’s Existential graphs for visualizing ALC description logic formulas. In this paper,
we extend our visualization system for SHIF and SHOIN description logics, based on Venn diagrams
and graph theory. The resulting system is complete with respect to the OWL DL language and allows
visualizing the semantics of ontological relations almost without new graphic primitives.
Keywords
Visualization, Ontology, Description logic, Existential graph, Venn diagrams, Graph theory,
1. Introduction
In recent years, ontologies have been widely used to capture comprehensive domain knowledge
in different areas. "Ontologies define the concepts and relationships used to describe and
represent an area of knowledge" [1]. Ontology visualization is intended to display the concepts
and structures of an ontology to help users to understand and analyze knowledge they contain.
There are numerous ontology visualization systems, the reviews can be found in [2, 3, 4].
However, these systems have several common drawbacks. First, most of them don’t allow
representing all required ontological relations. In particular, we consider the OWL DL language
[5], as it is sufficient for most practical tasks. Thus, in other words, they are incomplete with
respect to the OWL DL language.
Proceedings of the 12th Majorov International Conference on Software Engineering and Computer Systems, December
10-11, 2020, Online Saint Petersburg, Russia
" nguyenngocthan92@gmail.com (T. Nguyen); baimuratov.i@gmail.com (I. Baimuratov)
0000-0002-6679-7839 (T. Nguyen); 0000-0002-6573-131X (I. Baimuratov)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Second, they visualize ontological structures just labeling them, not representing their seman-
tics. For example, compare the visualization of conjunction from Graphol [6], Fig.1, with the
corresponding Venn diagram, Fig. 2. Venn diagram represents by itself that these two sets have
common elements, while in Graphol a user has to know that hexagon denotes conjunction.
Figure 1: Conjunction in Graphol Figure 2: Conjunction in Venn diagrams
Finally, the existing ontology visualization systems mostly use arbitrary graphic primitives.
Thus, instead of helping a user to understand an ontology, they just represent it with another
language. Considering again the example above, in Graphol the user has to learn that hexagon
denotes conjunction. It would be friendlier to use existing graphic primitives from mathematic
theories as the user won’t have to learn new ones.
On the other hand, there are semantic-oriented visualization systems like Conceptual graphs
[7] or Concept graphs [8], but they correspond to First-order logic, not to the OWL DL language.
Therefore, our goal is to develop a complete ontology visualization method, named “Logic
graphs” (LGs), with two features. First, it should represent the semantics of the ontological
structures, formulated as description logic axioms. Second, it should apply existing graphic
primitives from mathematical theories, where it is possible.
In our previous work [9], we adopted the Ch. S. Peirce’s Existential graph [10, 11, 12] for
description logic formulas. As the result, we developed the visualization method for ALC
description logic. In this paper, we extend our visualization system for SHIF and SHOIN
description logics.
The outline of the paper is as follows: Section 2 describes the OWL sub-languages and
the corresponding description logics. In Section 3, we review the state-of-the-art ontology
visualization tools and discuss several drawbacks they have. Section 4 gives a short description
of the visualization methods used in mathematics that we apply in our system. Section 5 presents
the Logic graph system in detail. In Section 6, we visualize some axioms of DoCO ontology
using the developed method, before the paper is concluded in Section 7.
2. OWL sub-languages and Description logics
In this section we describe the OWL DL language [5] and its logical foundation – Description
logics (DLs) [13] as the object language to be visualizaed.
Ontologies are denoted on the OWL language. The Web Ontology Language OWL is a
semantic markup language for publishing and sharing ontologies on the World Wide Web. The
OWL 1 standard provided three increasingly expressive sub-languages: OWL Lite, OWL DL,
and OWL Full. OWL Lite supports those users primarily needing a classification hierarchy and
simple constraints. OWL DL provides the maximum expressiveness, retaining computational
completeness (all conclusions are guaranteed to be computed) and decidability (all computations
will finish in finite time). OWL DL includes all OWL language constructs, but they can be
used only under certain restrictions. Finally, OWL Full is meant for users who want maximum
expressiveness and the syntactic freedom of RDF with no computational guarantees. Each of
these sub-languages is an extension of its simpler predecessor.
The formal foundation of OWL is description logics. Description Logics (DLs) are a family of
logic languages, which can be used to represent the terminological knowledge of an application
domain. In this paper, we consider the OWL DL language, as it is sufficient for most practical
tasks, and the corresponding SHOIN description logic. Its syntax and semantics is represented
in Table 1, where 𝐼 is an interpretation function and Δ is a domain. Therefore, our goal is to
develop the visualization system that would be complete with respect to the SHOIN DL and,
consequently, the OWL DL language.
Table 1
SHOIN description logic.
Name Syntax Semantic
concept 𝐶 𝐶 I ⊆ ΔI
role 𝑅 𝑅 ⊆ ΔI × ΔI
I
negation ¬𝐶 ΔI ∖ 𝐶 I
conjunction 𝐶 ⊓𝐷 𝐶 I ∩ 𝐷I
disjunction 𝐶 ⊔𝐷 𝐶 I ∪ 𝐷I
I
(∃𝑅.⊤) = 𝑎 ∈ ΔI : ∃𝑏 (𝑎, 𝑏) ∈ 𝑅I
{︀ }︀
existential restriction ∃𝑅.⊤
I
(∀𝑅.⊤) = 𝑎 ∈ ΔI : ∀𝑏 (𝑎, 𝑏) ∈ 𝑅I
{︀ }︀
universal restriction ∀𝑅.⊤
→ 𝑏 ∈ 𝐶I
𝑅− −
(𝑅 ) = (𝑏, 𝑎) ∈ ΔI × ΔI : (𝑎, 𝑏) ∈ 𝑅I
{︀ }︀
inverse role
+ I
(︀ I
)︀
transitive role 𝑅+ (︀ I )︀(𝑅 ) = ∪𝑖 (︀𝑅 I )︀𝑖 where (𝑎, 𝑏) (︀∈ I )︀
𝑅 𝑖 ∧ (𝑏, 𝑎) ∈ 𝑅 𝑖 → (𝑎, 𝑐) ∈ 𝑅 𝑖
functional role ≤ 1𝑅 ≤ 1𝑅 ⇔ {(𝑎, 𝑏) , (𝑎, 𝑐)} ⊆ 𝑅I ⇒ 𝑏 = 𝑐
I
role inclusion 𝑅⊑𝑆 𝑅{︀ ⊑}︀𝑆 I
nominal {𝑜} {︀ 𝑜I
I
{︀ I
}︀ }︀
≥ 𝑛𝑃 {︀ 𝑎 ∈ ΔI || {︀𝑏 | (𝑎, 𝑏) ∈ 𝑅 I }︀| ≥ 𝑛 }︀
number restrictions
≤ 𝑛𝑃 𝑎 ∈ Δ || 𝑏 | (𝑎, 𝑏) ∈ 𝑅 | ≤ 𝑛
3. Related works
3.1. Ontology Visualization Tools
According to [2, 3, 4], there are visualization tools such as TGViz, OntoTrack, KC-Viz, OntoRama,
Ontodia, OntoTrix, Flexviz, OntoGraf, GraphViz, Graffoo, OWLviz, NavigOWL, Glow, SOVA,
Jambalaya, Gephi, VOWL, Graphol, OntoSphere, and Tarsier. For each tool, we examined
the required properties, namely its completeness with respect to the OWL DL language, its
“semanticality”, the ability to represent the semantics of relations, and whether its graphic
primitives are new or adopted from common visualization systems.
As the result, some visualization tools like OWLViz, OntoTrack, KC-Viz, and OntoRama
visualize only the class hierarchy. GLOW, OntoGraf, FlexViz, and OWLViz represent various
types of property relations, but do not show property characteristics, required to fully understand
the information modeled in ontologies.
Although some visualization tools, such as Graphol or SOVA, show complete ontology
information, i.e. all classes and properties along with their attributes, these systems still have
several drawbacks. First, they visualize ontological structures by simply labeling them, without
representing their semantics, and second, existing ontology visualization systems mainly use
arbitrary graphic primitives. As a result, instead of helping the user to understand the ontology,
he or she has to learn one more language.
3.2. Conceptual graphs
On the other hand, there are other works on semantic-oriented visual frameworks. One of the
most known examples is the Conceptual graphs. The Conceptual graphs (CGs) is the family of
formalisms that originates from J. Sowa’s work [8], which in turn is based on Ch. S. Peirce’s
existential graphs [10, 11, 12], where knowledge is represented by labeled graphs and reasonong
mechanisms are based on graph operations. However, CGs don’t fit our purpose, as they are
found on First-order logic. According to [7], the ”intersection” between Description logics (DLs)
and CGs is less expressive than each formalism. For instance, DLs can’t express 𝑛-ary relations
for 𝑛 > 2, while in CGs one can’t represent value restrictions. The variation of CGs are F. Dau’s
Concept graphs [14], but they are still based on First-order logic.
In [15], the authors propose the visualization system based on the Ch. S. Peirce’s existential
graphs for the ALC description logic exactly, but it is not sufficient for representing the OWL
DL language.
4. Visualization methods used in mathematics
Developing our visualization method, we suggest applying existing graphic primitives from
mathematical theories, such as Venn diagrams [16], Ch. S. Pierce’s existential graphs [10, 11, 12]
and diagrams from the graph theory [17], where it is possible, in order to make the method
easy to learn, as the user won’t have to learn new ones.
4.1. Venn diagrams
The Venn diagrams depict elements as points in the plane and sets as regions inside closed
curves. A Venn diagram consists of multiple overlapping closed curves, usually circles, each
representing a set. We use Venn diagrams-based intuitions to denote concept equivalence and
belonging of an individual to a concept as such, as shown in the Fig. 3, Fig. 4, Fig. 5 .
4.2. Existential graphs
An existential graph is a type of visual notation for logical expressions, proposed by Charles
Sanders Peirce. Peirce presents the graphs as three general systems called “Alpha”,“Beta” and
Figure 3: Membership Figure 4: Equivalence Figure 5: Inclusion
“Gamma”. This division corresponds fairly well to the contemporary proposition, predicate, and
modal logic respectively. As description logic can be considered as a fragment of predicate logic,
the system Beta is the most relevant. In this system blank space is considered as the “sheet
of assertion”, or the space of true expressions, therefore, expressions, situated on this sheet
are conjuncted. Besides, there are “cuts” on the sheet that mean inversion of the expressions,
situated on these cuts, and curves, denoting predicates, existentially quantified by default. As
any complex expression can be represented with only conjunction, negation, and existential
quantifier, these two graphic primitives are sufficient for visualizing any expression of predicate
logic. The Beta existential graph system is represented in Table 2, where 𝑎 and 𝑏 are propositions,
𝑃 and 𝑅 — predicates, and 𝑠 and 𝑡 -– individuals. We use existential graphs for depicting logical
relations: negation, conjunction, and their derivatives.
4.3. Graph theory
A graph is graphically represented by drawing a point for each vertex and drawing an arc
between two vertices if they are joined by an edge. If the graph is oriented (digraph), then
the direction is indicated by an arrow. We use graph-based intuitions to represent inverse and
transitive roles.
In graph theory, an inverse relation is depicted with an arrow between two vertices 𝐴 and 𝐵,
such that it is oriented from the vertex 𝐴 to the vertex 𝐵. See Fig. 6. Transitivity is the same as
saying there must be a direct arrow from vertex 𝐴 to another vertex 𝐶, if one can walk from
that vertex to the last one through a list of arrows, travelling always along the direction of the
arrows. See Fig. 7.
Figure 6: Inversion
Figure 7: Transitivity
Table 2
Existential graphs.
1. a 2. ¬𝑎
3. 𝑎∧𝑏 4. 𝑎∨𝑏
5. 𝑎⊃𝑏 6. 𝑎≡𝑏
7. ∃𝑥𝑃 (𝑥) 8. ∀𝑥𝑃 (𝑥)
9. 𝑃 (𝑠) 10. 𝑅 (𝑠, 𝑡)
5. Logic graphs
We propose the complete ontology visualization method, named “Logic graphs”, aimed to
represent the semantics of description logic axioms and to be easy to learn, due to using existing
graphic primitives.
5.1. ALC
In our previous work [9], we adopted the existential graph visualization method for description
logic formulas. As the result, we developed the visualization method for ALC description logic.
It is represented in Table 3, where 𝐶, 𝐶1, and 𝐶2 are concepts, 𝑅 — role and 𝑎 and 𝑏 — objects.
𝐷𝑜𝑚𝑎𝑖𝑛 — the technical concept, introduced to represent domain concepts of roles. The exact
form of the figures corresponding to concepts is not crucial, here we use rectangles for practical
reasons
5.2. Logic SHIF
In this paper, we extend our visualization system for SHIF and SHOIN description logics. SHIF
extends ALC with inverse, transitive, and functional roles and role inclusion. In order to depict
inverse and transitive roles, we suggest applying the graph-theoretic intuitions.
An inverse role 𝑅− corresponds to an inversely directed arrow. See Fig. 8. If a role 𝑅+ is
Table 3
Logic graphs for ALC.
1. C 2. ¬𝐶
3. 𝐶1 ⊓ 𝐶2 4. 𝐶1 ⊔ 𝐶2
5. 𝐶1 ⊑ 𝐶2 6. 𝐶1 ≡ 𝐶2
7. ∃𝑅.𝐶 8. ∀𝑅.𝐶
9. 𝑎:𝐶 10. (𝑎, 𝑏) : 𝑅
Figure 8: Inverse roles
transitive and a corresponding edge connects a node 𝐴 with a node 𝐵 and a node 𝐵 with a
node 𝐶, then it connects the nodes 𝐴 and 𝐶. See Fig. 9.
Unfortunately, we suppose there is no simpler way to represent functional roles, than labeling
the corresponding property arrow with “≤ 1” notation. See Fig. 10.
For role inclusion, we suggest not to introduce a new graphic primitive, but to represent it
through its semantics. The semantics of role inclusion is following
𝑅 ⊑ 𝑆 ⇔ 𝑅I ⊆ 𝑆 I (1)
According to the definition of a role,
𝑅I ⊆ 𝑆 I ⇔ (𝑎, 𝑏) ∈ 𝑅I ⇒ (𝑎, 𝑏) ∈ 𝑆 I (2)
Let us define the set X such that
Figure 9: Transitive roles
Figure 10: Functional roles
𝑋 = 𝑎 : ∃𝑏 (𝑎, 𝑏) ∈ 𝑅I (3)
{︀ }︀
and the set Y such that
𝑌 = 𝑎 : ∃𝑏 (𝑎, 𝑏) ∈ 𝑆 I (4)
{︀ }︀
Theorem 1. 𝑅 ⊑ 𝑆 ⇒ 𝑋 ⊑ 𝑌
Proof. According to the definition of inclusion, the theorem can be represented as follows:
𝑅I ⊆ 𝑆 I ⇒ 𝑋 ⊑ 𝑌 . According to the definition of X, for every a if 𝑎 ∈ 𝑋 ⇒ (𝑎, 𝑏) ∈ 𝑅I , then,
according to the property (2), (𝑎, 𝑏) ∈ 𝑆 I and, finally, according to the definition of Y, 𝑎 ∈ 𝑌 .
Therefore, 𝑎 ∈ 𝑋 ⇒ 𝑎 ∈ 𝑌 for every a, and 𝑋 ⊑ 𝑌 .
As the result, we propose to depict role inclusion 𝑅 ⊑ 𝑆 with concept inclusion 𝑋 ⊑ 𝑌 , as
presented in the Fig. 11. Thus we extended Logic graphs for SHIF description logic.
5.3. Logic SHOIN
SHOIN extends SHIF with nominals and number restrictions. To represent a nominal, we apply
Venn diagrams intuitions. We suggest representing each individual, forming the nominal, as a
point. For example, see Fig. 12.
Figure 11: Logic graph for role inclusion
Figure 12: Nominal
Again, we suppose there is no simpler way to represent number restrictions, than labeling
them with the exact, minimum, or maximum cardinality restrictions written as numbers above
the property arrow. See Fig. 13. Thus, we extended Logic graphs for SHOIN description logic
and, therefore, the method is complete with respect to the OWL DL language.
Figure 13: Number restriction
6. Logic graphs for the DoCO ontology
The Document Components Ontology (DoCO) [18] is an ontology that provides a structured
vocabulary for document components. We use the DoCO ontology as an example for visualiza-
tion, as it is a real ontology, used in different applications and it contains nontrivial axioms. We
visualized some axioms using the developed method. See Table 4.
Table 4
Logic graphs for the DoCO ontology.
𝑎𝑏𝑠𝑡𝑟𝑎𝑐𝑡 ⊑ (𝑐ℎ𝑎𝑝𝑡𝑒𝑟 ⊔ 𝑠𝑒𝑐𝑡𝑖𝑜𝑛)
⊓(∃𝑖𝑠𝑝𝑎𝑟𝑡𝑜𝑓.𝑏𝑜𝑑𝑦𝑚𝑎𝑡𝑡𝑒𝑟
⊔𝑓 𝑟𝑜𝑛𝑡𝑚𝑎𝑡𝑡𝑒𝑟)
𝑎𝑓 𝑡𝑒𝑟𝑤𝑜𝑟𝑑 ⊑ 𝑠𝑒𝑐𝑡𝑖𝑜𝑛⊓
∃𝑖𝑠𝑝𝑎𝑟𝑡𝑜𝑓.𝑏𝑎𝑐𝑘𝑚𝑎𝑡𝑡𝑒𝑟)
𝑎𝑝𝑝𝑒𝑛𝑑𝑖𝑥 ⊑ (𝑠𝑒𝑐𝑡𝑖𝑜𝑛⊓
ℎ𝑒𝑎𝑑𝑒𝑑𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑟)⊓
(∃𝑖𝑠𝑝𝑎𝑟𝑡𝑜𝑓.𝑏𝑎𝑐𝑘𝑚𝑎𝑡𝑡𝑒𝑟)
𝑏𝑎𝑐𝑘𝑚𝑎𝑡𝑡𝑒𝑟 ⊑ 𝑑𝑖𝑠𝑐𝑜𝑢𝑟𝑠𝑒𝑒𝑙𝑒𝑚𝑒𝑛𝑡
⊓𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑟
𝑏𝑎𝑐𝑘𝑚𝑎𝑡𝑡𝑒𝑟 ⊑ ∀𝑖𝑠𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑑𝑏𝑦
(.¬𝑏𝑎𝑐𝑘𝑚𝑎𝑡𝑡𝑒𝑟 ⊔ 𝑏𝑜𝑑𝑦𝑚𝑎𝑡𝑡𝑒𝑟
⊔𝑓 𝑟𝑜𝑛𝑡𝑚𝑎𝑡𝑡𝑒𝑟)
𝐵𝑙𝑜𝑐𝑘𝑞𝑢𝑜𝑡𝑎𝑡𝑖𝑜𝑛 ⊑ 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑟
𝑐ℎ𝑎𝑝𝑡𝑒𝑟 ⊑ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠.𝑝𝑎𝑟𝑎𝑔𝑟𝑎𝑝ℎ
⊔𝑠𝑒𝑐𝑡𝑖𝑜𝑛
𝑐ℎ𝑎𝑝𝑡𝑒𝑟 ⊑ ∃𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠.¬(𝑐ℎ𝑎𝑝𝑡𝑒𝑟)
𝑐ℎ𝑎𝑝𝑡𝑒𝑟𝑙𝑎𝑏𝑒𝑙 ⊑ ¬𝑠𝑒𝑐𝑡𝑖𝑜𝑛𝑙𝑎𝑏𝑒𝑙
𝑐ℎ𝑎𝑝𝑡𝑒𝑟𝑠𝑢𝑏𝑡𝑖𝑡𝑙𝑒 ⊑ ∃𝑖𝑠𝑝𝑎𝑟𝑡𝑜𝑓.𝑐ℎ𝑎𝑝𝑡𝑒𝑟
𝑓 𝑖𝑔𝑢𝑟𝑒 ⊑ 𝑚𝑎𝑡𝑎 ⊔ 𝑚𝑖𝑙𝑒𝑠𝑡𝑜𝑛𝑒
ℎ𝑒𝑎𝑑𝑒𝑟 ≡ 𝑓 𝑟𝑜𝑛𝑡𝑚𝑎𝑡𝑡𝑒𝑟
𝑔𝑙𝑜𝑠𝑠𝑎𝑟𝑦 ⊑ 𝑠𝑒𝑐𝑡𝑖𝑜𝑛⊓
(∃𝑖𝑠𝑝𝑎𝑟𝑡𝑜𝑓.𝑏𝑎𝑐𝑘𝑚𝑎𝑡𝑡𝑒𝑟
⊔𝑓 𝑟𝑜𝑛𝑡𝑚𝑎𝑡𝑡𝑒𝑟)
𝑙𝑖𝑠𝑡 ⊑ ∀𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠.𝑏𝑙𝑜𝑐𝑘 ⊔ 𝑓 𝑖𝑒𝑙𝑑⊔
(𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑟 ⊓ (¬(ℎ𝑒𝑎𝑑𝑒𝑑𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑑
⊔𝑡𝑎𝑏𝑙𝑒)))
𝑡𝑎𝑏𝑙𝑒𝑜𝑓 𝑐𝑜𝑛𝑡𝑒𝑛𝑡𝑠 ⊑
∃ℎ𝑎𝑠𝑝𝑎𝑟𝑡.𝑙𝑖𝑠𝑡𝑜𝑓 𝑟𝑒𝑓 𝑒𝑟𝑒𝑛𝑐𝑒𝑠⊓
(∀𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑠.∃𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛.𝑠𝑒𝑐𝑡𝑖𝑜𝑛)
𝑡𝑎𝑏𝑙𝑒 ⊑ 𝑡𝑎𝑏𝑙𝑒
7. Conclusion and Future works
The developed visualization method, named “Logic graphs”, is complete with respect to the
OWL DL language. It allows visualizing the semantics of logical expressions almost without
new graphic primitives, except for functional roles and number restrictions. As the result, a
user familiar with the set theory, graph theory and description logic will be able to use Logic
graphs without additional instructions. Several examples were provided considering the DoCO
ontology.
In further research, we are going, first, to perform formal quantitative evaluation of Logic
graphs and compare them with other visualization systems. Second, we are going to develop
an ontology visualization application, implementing LGs, based on the Ontodia library [19].
Finally, we are going to perform a usability study to assess if LGs are more convenient for
ontology practioners.
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