Introduction to Ontology Engineering with Fluent Editor 2014

Introduction to Ontology Engineering
with Fluent Editor 2014
An introductory course for Ontology Engineering using
Controlled Natual Language
© 2014 Cognitum. All rights reserved.

Fluent Editor™ 2014
Semantic web
 Semantic web is…
 … a web of Linked Data
 Linked Data ...
 … is a structered data format –
Web Ontology Language (OWL)
in RDF format
 … - machines can understand it
and reason about it (formal logic)
 … is linked to other data (your
data refres to some other
ontologies, people can refer to
yours)

Ontology
 Ontology ...
 … is a formal description of a
domain of knowledge (university
education)
 … lists most important concepts
(staff, student) and instances
(Prof. Smith, Student John)
 … describes relationships
between objects (Prof. Smith
teaches John, requirements for
obtaining a diploma)
 … typically is written in Web
Ontology Language (OWL) RDF
format

Fluent Editor (FE)
 FE is..
 An ontology editor for editing
and manipulating ontologies.
 FE supports..
 Controlled Natural Languge
interface + Predictive Editor.
 Knowledge representation for
semantic technologies :
formal logic, OWL 2, RDF, SWRL
 Reasoning engine : HermiT

Controlled Natural Language in FE
CNL is a subset of natural language with restricted grammar and vocabulary
in order to reduce the ambiguity and complexity inherent in full natural language.
Ontology OWL 2 + SWRL Controlled English in FluentEditor
Ontorion Controlled Natural Language (OCNL) in Fluent Editor is automatically
translated into and from description logic, OWL 2, SWRL.

FluentEdior Interface (1)
Taxonomy Tree derived from
the knowledge entered in CNL interface
CNL Interface for interaction with a user

Reasoner Interface
for quering questions
in CNL

XML Preview
for previewing a CNL
sentence in an XML
format.

Concept/Class Definition (1)
young-male-man very-beautiful-girl
Class identifiers start with a small letter and use dashes
between words.
All standard OWL class identifiers are transformed in this rule.
ex) OWL: VeryBeautifulGirl → FE CNL: very-beautiful-girl

Instances
John is a person.
Instance identifier = each part starts with a capital letter
and they are separated with dashes.
John-Dow Tanker-Accident-X
OWL: JohnDow → FE CNL: John-Dow
THE-”K22 P2”
To specify the instance of a concept, class assertion is enough.

Property Names
OWL: isPartOf → FE CNL: be-part-of
OWL: hasBirthDate → FE CNL: have-birth-date

Ontology & References

Concept Subsumption
Every boy is a young-male-man.
Saying that one concept subsumes the other we define
IS-A/taxonomic relation and a concept hierarchy.

Value Partition / Disjoint Union
Something is a person if-and-only-if-it-either is a
child, is a young-thing, is a middle-age-thing or
is an old-thing.
A disjoint union axiom states that a class C is a disjoint union
of the class expressions CEi , 1 ≤ i ≤ n, all of which are pairwise
disjoint.

Defining Facts – Properties(roles)
Single fact
 … and one more
Tom is-a-child-of Mike.
Poland has-capital Warsaw.

Defining Facts – Property(role) Restrictions
Existential role restrictions
Universal role restrictions
Every person is-a-child-of
a parent.
Every person is-a-child-of
nothing-but parents.
These restrictions are complementary to each other.
However, they do not imply each other.
Something is a happy-person if-and-only-if-it
has-child a happy-person and has-child
nothing-but happy-persons.

Data Property Assertions
John has-name equal-to 'John'.
Lenka borns-on-date equal-to 1975-11-10.
Tanker-Accident has-time equal-to 2013-07-
08T09:30:40.40. hasTime=“2013-07-08T09:30:40.40”

Data Property Domain & Range
Every-single-thing that has-name (some value)
is a person.
Every-single-thing has-name nothing-but (some
string value).
Keywords for date property values
• (some value) : equivalent to rdfs:Literal
• (some string value) : xsd:string
• (some integer value) : xsd:int
• (some boolean value) : xsd:boolean
• (some real value) : xsd:double
• (some datetime value) : xsd:datetime

Semantic Rules Schema
 SWRL: antecedent (body) → consequent (head)
 FE : If <antecedant> then <consequent>.
„Whenever the conditions specified in the antecedent hold, then
the conditions specified in the consequent must also hold”
 FE : If <clause> [and <clause>]*
then <consequent-clause> [and <consequent-clause>]*.
If a person is-year-old greater-or-equal-to 18 then the person is an adult-person.
Language Rules
SWRL antecedent (body) → consequent (head)
FE If <antecedant> then <consequent>.

Variables in Semantic Rules
Variables in semantic rules :
• a/the class-name
• a/the thing
• a/the class-name (n) : If more variables of the same type to
come, mark them in different numbers in parenthesis.
If a person(1) has-parent a person(2) and the person(2) is a female-person then
the person(1) has-mother the person(2).
If a patient has-tumor-rupture Not-Specified then the patient has-risk-group
Risk-Group-Tn.
If a thing is a person then the thing has-name (some string value).
If a thing (1) hosts a thing(2) and the thing(2) hosts an application then the
thing(1) hosts the application.

Instances & Property Assertions
Server-1 is a server.
Server-2 is a server.
Virtual-Machine-1 is a virtual-machine and is-running-on Server-1.
Virtual-Machine-1 hosts Application-1.
Virtual-Machine-2 is a virtual-machine and is-running-on Server-2.
Virtual-Machine-2 hosts Application-2.
Server-1 has-ip-address equal-to '173.194.70.102'.
Application-1 is an application that serves Customer-1 and serves Customer-2.
Application-2 is an application that serves Customer-3.
Application-1 has-name equal-to 'Fluent Editor'.
Application-1 has-name equal-to 'Fluent Editor 2014'.
Application-2 has-name equal-to 'Ontorion'.
Customer-1 is a customer and has-severity Critical.
Customer-2 is a customer and has-severity Medium.
Customer-3 is a customer and has-severity Low.

Property Axioms
Something is a severity if-and-only-if-it is either Critical or Medium or Low.
Something is a priority if-and-only-if-it is either Critical or Medium or Low.
Every-single-thing that has-reported-date (some datetime value) is an incident.
Every-single-thing that was-reported-by something is an incident.
Every-single-thing was-reported-by nothing-but operators.
Part-2:'Incidents'.
Incident-1 has-reported-date equal-to 2014-09-01 and is reported by Operator-1.
Incident-1 affects Server-1.
Incident-2 has-reported-date equal-to 2014-09-09 and is reported by Operator-1.
Incident-2 affects Application-2.

Semantic Rules
Questions:
• Who-Or-What reports Incident-1 ?
• Who-Or-What is affected by Incident-1 ?
• Who-Or-What is affected by something that is reported by Operator-1 ?
• Who-Or-What serves something that has-severity Critical ?
• Who-Or-What affects something that serves something that has-severity Critical ?
If the incident affects a server and a virtual-machine is-running-on the server
and the virtual-machine hosts an application then the application is affected by
the incident and the virtual-machine is affected by the incident.
If an incident affects a virtual-machine and the virtual-machine hosts an
application then the application is affected by the incident.
If an application is affected by the incident and the application serves a
customer and the customer has-severity a severity then the incident has-priority
the severity.

How to build ontology
 Start simple
 … what are the most important
concepts?
 … what are the relations
between these concepts?
 … what knowledge should be
inferred (add rules)?
 Think big
 … Search in the Linked Open
Vocabularies to find the more
common vocabularies

Referencing – pros & cons
 Cons
 …be careful when using other
ontologies, check the source and
check that it is working correctly
(e.g. QUDT)
 … do not be tempted to model a
world when defining music
genres (BBC ontologies)
 … do not reference ontology too
big for your machine (SNOMED)
 Pros
 … you can obtain reliable
properties of chemical
compounds (RSC ontologies)
 … your knowledge will be
updated (DBpedia)
 … your ontology will share a
common context (DC ontology)

Performance matters
 To improve performance…
… think what are the typical questions to your ontology
… think what facts will typically be reasoned in your
ontology
… use OWL profiles: OWL RL, OWL EL
OWL profile is a subdialect of full OWL DL – it uses fewer
types of statements and rules, but gives better
guarantees on performance

Further learning
If you are interested, you can:
• Download free (for non-commercial use) version of FluentEditor from page
http://www.cognitum.eu/semantics/FluentEditor/
• Build your own ontology of a chosen topic
• Try to add references to some datasets that will give context to your ontology
(good start: Dublin Core (DC) ontology or HCLS/POMROntology – Problem-
Oriented Medical Record Ontology
• Explore DBpedia (semantic Wikipedia) with Fluent Editor - download file
DBpedia Ontology T-BOX (Schema) from http://wiki.dbpedia.org/services-
resources/ontology
• Stay tuned with techblog.cognitum.eu

Fluent Editor http://www.cognitum.eu/Semantics/FluentEditor/
Ontorion Server http://www.cognitum.eu/Semantics/Ontorion/
Cognitum Technology Blog http://techblog.cognitum.eu
Cognitum | PL, Warsaw
office@cognitum.eu
+48 22 250 2541
www.cognitum.eu/semantics
The fragment of Linked Open Data cloud diagram
has been taken from http://lod-cloud.net/
Pictures that visualize the presented axioms are done
with the use of OWLGred editor and Protege.
Some examples are taken from OWL 2 primer:
http://www.w3.org/TR/2012/REC-owl2-primer-20121211
The company, product and service names used in this web site are for identification purposes only.
All trademarks and registered trademarks are the property of their respective owners.

Introduction to Ontology Engineering with Fluent Editor 2014

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