- The document describes an approach to combine ontology visualization using graphical notation with contextual verbalization in controlled natural language. - It uses the OWLGrEd editor and notation, which maps ontology axioms to graphical elements, and integrates a verbalizer to generate explanations of axioms related to selected elements in multiple languages like English and Latvian. - This allows users to understand ontology structures and restrictions from diagrams while getting contextual explanations in readable controlled natural language without learning a new notation.

1. Towards Self-explanatory
Ontology Visualization
with Contextual Verbalization
Renārs Liepiņš, Uldis Bojārs, Normunds Grū̄zītis,
Kārlis Čerāns, Edgars Celms
Institute of Mathematics and Computer Science
University of Latvia
12th International Baltic Conference on Databases and Information Systems (DB&IS), Riga, Latvia | 4-6 July 2016

2. Assistants are teachers. They are
not professors. Professors are
teachers who teach mandatory
courses. Mandatory courses are
taught only by professors. A
course may be taught by more
than one teacher.
Courses are taken by students.
Second-year students have
passed at least two courses.
Teachers may take courses as
well but not the ones that they
teach.
Courses constitute academic
programs. Each student is
enrolled only in one academic
program.
Domain experts vs. Knowledge engineers
Conceptual
Modelling
Ontology
Engineering
<<owlClass>>
Student
<<owlClass>>
AcademicProgram
<<owlClass>>
Course
<<owlClass>>
Person
<<owlClass>>
Teacher
<<owlClass>>
MandatoryCourse
<<owlClass>>
OptionalCourse
<<owlClass>>
Professor
<<owlClass>>
Assistent
<<disjointWith>>
<<objectProperty>>
teaches
<<objectProperty>>
takes
<<disjointWith>>
+teaches
+includes
+takes
+enrolls
+constitutes
Class: Assistant
SubClassOf: Teacher
DisjointWith: Professor
Class: Professor
SubClassOf: Teacher,
teaches some MandatoryCourse
DisjointWith: Assistant
Class: MandatoryCourse
SubClassOf: Course,
inverse (teaches) only Professor
ObjectProperty: teaches
Domain: Teacher
Range: Course

3. Cl ass: Per son
SubCl assOf : owl : Thi ng
Obj ect Pr oper t y: hasChi l d
Domai n: Per son
Range: Per son
Dat aPr oper t y: hasAge
Domai n: Per son
Range: xsd: i nt eger
Manchester
Notation
hasAge
likes
integerPerson
(external)
VOWL
Notation
Natural
Language
Every person is a thing.
Everything that likes something is a person.
Everything that is liked by something is a person.
Everything that has an age is a person.
Everything that is an age of something is an integer.
OWLGrEd
Notation
Diagrams vs. Controlled natural language (CNL)
• Visual notations provide a better overview of the ontology structure and
entity connections
• However, one has to learn the notation to understand its semantics
• CNL is readable right away and often helps to understand complex
ontology restrictions
• However, the structure and interconnections become less apparent

4. Combining diagrams with CNL verbalization
Ontology Visualization
User Selection
Relevant
Axioms
Verbalization of
User Selection
OWL Ontology
A B
C
Every C is a B
Collector
of
Relevant
Axioms
Visualizer Verbalizer

5. • Every diagram element represents a set of ontology axioms – the
axioms are generally presented locally in the diagram
• A single ontology axiom can be related to several elements of the diagram
• Ontology symbols are lexically motivated and consistent
• Preferably with lexical annotations
• We do not have to design a new verbalizer for each new graphical OWL
notation, because they all are mapped to the same underlying axioms
• By reusing ontology verbalizers, existing ontology visualization systems can
be easily extended with on-demand contextual explanations
• Contextual verbalization can be helpful in both exploring existing
ontologies and authoring new ontologies
• It would motivate to follow a lexical and consistent naming convention
Assumptions and consequences

6. OWLGrEd: a notation and editor for OWL

7. Developed at IMCS UL, used across the globe

8. • A compact UML-style graphical notation and editor for OWL 2
• Uses the Manchester OWL Syntax for class expressions
• Builds on 20-year experience in graphical modeling languages and tools
• Integrated with Protégé
• The OWLGrEd/CNL extension:
• Supports the lexicalization of ontology elements and the verbalization of
ontology axioms
• Tested on the highly analytic English and the highly inflected Latvian
• Uses:
• The OWL subset of Attempto Controlled English (ACE) as an interlingua
• Grammatical Framework (GF) for the multilingual verbalization
OWLGrEd and its experimental CNL extension

9. interlingual ACE
statements
OWLGrEd and its experimental CNL extension
OWLGrEd:
editor
OWLGrEd:
viewer
Multilingual
domain
lexicon (GF)
Multilingual ontology
grammar (GF)
compiled into
generates General,
multilingual ACE
grammar (GF)
Ontology
Axioms Annotations
CNL
Verbalizer
Attempto
OWL
verbalizer
axioms
collected by
ACE statements
using ontology
symbols
ACE-English / ACE-Latvian statements
1
2
yields adds
OWL
axioms
CNL
statements
3

10. Lexical terms in English and
Latvian
Ontology symbol, generated
form the English term
Domain lexicon: declaring a class

11. Properties are always declared / lexicalized in
the context of their domain and range classes
In Latvian, the grammatical case of the subject
and object depends on the verb;
The user can implicitly correct the case, if the
automatically suggested case is incorrect
Domain lexicon: declaring a property

12. Contextual verbalization in action (editor)

13. Contextual verbalization in action (viewer)

14. Contextual verbalization in action (viewer)

15. Contextual verbalization in action (viewer)

16. Contextual verbalization in action (viewer)

17. Contextual verbalization in action (viewer)

18. • CNL is more understandable to domain experts and end-users because
it does not have to be learned, or the learning time is very short
• However, comparative studies have shown the domain experts still tend
to prefer the graphical notations in the long run
• We have proposed an approach that combines the benefits of both a
graphical notation and CNL verbalization
• A graphical representation gives the user an overview of the ontology
while the contextual verbalization explains what the particular graphical
element means
• The interactive CNL explanations help domain experts to learn and use the
graphical notation without a special training (documentation)
• Target audience: domain experts, students, end-users
• Also knowledge engineers, especially for validating the inferred axioms
Conclusion