introduction to ontologies and ontology engineering
TRANSCRIPT
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Introduction to
Ontologies and
Ontology Engineering
Patrick Lambrix
Linköping University
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My current research interests
Ontology engineering
Theory, methods, tools for ontology
alignment, completion, debugging;
organization of workshops and competitions
Development of ontologies for different areas
(materials science, animal health surveillance,
crime investigation, ice hockey)
Sports analytics
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GET THAT PROTEIN!
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Where?Which?
How?Vision: Web services
- Databases and tools (service
providers) announce their
service capabilities
- Users request services which
may be based on task
descriptions
- Service matchers find relevant
services (composition) based
on user needs and user
preferences, negotiate service
delivery, and deliver results to
user
Locating relevant information
Findable
Accessible
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Vision:
Based on the
meaning of the query:
- only relevant
information is
retrieved
- all relevant
information is
retrieved
Retrieving relevant information
Findable
Accessible
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Vision:
Integrate data sources that
are heterogeneous in
content, data quality, data
models, access methods,
terminology
Disease
information
Target
structure
Chemical
structure
Disease
models
Clinical
trials
Metabolism,
toxicology
Genomics
DISCOVERY
Integrating information
Interoperable
Reusable
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First step towards the vision:
adding semantic annotation to web resources
Scientific American, May 2001:
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Semantic annotations based on
ontologies
Locating information
Web service descriptions use ontologies
Users use ontologies when formulating requests
Service matchers find services based on meaning
Retrieving relevant information
Reduce non-relevant information (precision)
Find more relevant information (recall)
Integrating information
Relating similar entities in different databases
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GET THAT MATERIAL!
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Where?Which?
How?
Materials designElectronic
structure
Thermodynamic
properties
Cristal structure
Standardized terminology
Relationships between terms
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Ontologies
“Ontologies define the basic terms and
relations comprising the vocabulary of a
topic area, as well as the rules for
combining terms and relations to define
extensions to the vocabulary.”(Neches, Fikes, Finin, Gruber, Senator, Swartout, 1991)
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Nanoparticle Ontology
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Ontologies used …
for communication between people and organizations
for enabling knowledge reuse and sharing
as basis for interoperability between systems
as repository of information
as query model for information sources
Key technology for the Semantic Web
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Ontology-based querying
With ontology.
All MeSH Categories
Diseases Category
Eye Diseases
Scleral Diseases
Scleritis
...
Medical Subject
Headings (MeSH) return 1617 articles
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Ontology-based querying Without ontology.
return 1617 articles
return 695 articles
57% results are missed !
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Integration of data sources
What is Eye
Diseases ?Send me records
about Eye Diseases.
?
Eye diseases Eye disorders
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Integration of data sources
Send me records
about Eye Diseases.
Eye diseases = Eye disorders
Return records
about Eye Disorders.
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Components
concepts
- represent a set or class of entities in a domain
Football-player
- organized in taxonomies (hierarchies based on e.g. is-a or is-part-of)
Football-player is-a Athlete
instances
Eden Hazard
- often not represented in an ontology
(instantiated ontology)
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Components
relations
R: C1 x C2 x … x Cn
hasMember (between Sports-Team and Athlete)
hasMember (between Real Madrid and Eden Hazard)
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Components
axioms
‘facts that are always true’
The members of a football team are always football players.
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Different kinds of ontologies
Controlled vocabularies
Concepts
Taxonomies
Concepts, is-a
Thesauri
Concepts, predefined relations
Data models (e.g. EER, UML)
Concepts, relations, axioms
Logics
Concepts, relations, axioms
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(Description) Logics
Formal languages
Reasoning services
OWL-DL / OWL2 are based on description logics
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AL
R atomic role, A atomic concept
C,D A | (atomic concept)
T | (universal concept, top) owl:thing
| (bottom concept) owl:nothing
A | (atomic negation) owl:complementOf
C D | (conjunction) owl:intersectionOf
R.C | (value restriction) owl:allValuesFrom
R.T (limited existential quantification)
owl:someValuesFrom
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AL[X]C C (concept negation) owl:complementOf
U C U D (disjunction) owl:unionOf
E R.C (existential quantification)
owl:someValuesFrom
N ≥ n R, ≤ n R (number restriction)
owl:maxCardinality, owl:minCardinality
Q ≥ n R.C, ≤ n R.C (qualified number restriction)
owl:maxQualifiedCardinality,owl:minQualifiedCardinality
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AL (Semantics)
An interpretation I consists of a non-empty set
I (the domain of the interpretation) and an
interpretation function .I which assigns to
every atomic concept A a set AI I and to
every atomic role R a binary relation
RI I I.
The interpretation function is extended to
concept definitions using inductive definitions.
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AL (Semantics)
universal concept: TI = I
bottom concept: I = Ø
atomic negation: (A)I = I \ AI
conjunction: (CD)I = CIDI
value restriction: ( R.C)I = {a I|b.(a,b) RIb CI }
limited existential quantification: ( R.T)I = {a I| b.(a,b) RI}
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ALC (Semantics)
( C)I = I \ CI
(C U D)I = CI U DI
(≥ n R)I = {a I| # {b I | (a,b) RI } ≥ n }
(≤ n R)I = {a I| # {b I | (a,b) RI } ≤ n }
( R.C)I = {a I| b I : (a,b) RI ^ b CI}
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SemanticsIndividual i
iI I
Unique Name Assumption:
if i1 ≠ i2 then i1I ≠ i2
I
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Concepts and relations
Team
(Team)
Team
(not Team)
Team ≥ 10 hasMember
(Team and at least 10 members)
Team ≤ 10 hasMember
(Team and at most 10 members)
Concept/class
Relation/role/property
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Team hasMember.Football-player
(Team and all members are football players)
Team hasMember.Football-player
(Team and there is a member that is a football player)
Concepts and relations
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Axioms
C D (R S)
rdfs:subClassOf / rdfs:subPropertyOf
Football-player Athlete
(Every football player is an athlete)
C = D (R = S)
owl:equivalentClass / owl:equivalentProperty
(disjoint C D) owl:disjointWith
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Axioms
C(a) a rdf:type C
Team(Real Madrid)
(Real Madrid is an instance of Team)
R(a,b) a R b
hasMember(Real Madrid , Eden Hazard)
(Real Madrid has member Eden Hazard)
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Example
Teams have at least two members, while
large teams have at least 10 members.
Sports teams are teams which have only
athletes as members. A football team is a
team which has at least 11 members and all
the members are football players. Football
players are athletes. Real Madrid is a
football team that has Eden Hazard as a
member.
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Example
Team ≥ 2 hasMember
Large-Team = Team ≥ 10 hasMember
Sports-team = Team hasMember.Athlete
Football-Team = Team ≥ 11 hasMember
hasMember.Football-player
Football-player Athlete
Football-Team(Real Madrid)
hasMember(Real Madrid,Eden Hazard)
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Example
Every team has at least 2 members
Every large team is a team and has at least 10 members
Every sports team is a team and has only athletes as members
Every football team is a team and has at least 11 members
and has only football players as members
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Example
Every team has at least 2 members
Every large team is a team and has at least 10 members
Every sports team is a team and has only athletes as members
Every football team is a team and has at least 11 members
and has only football players as members
Reasoning:
Every football team is a large team
Every football team is a sports team
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Example
Real Madrid is an instance of football team
Real Madrid has member Eden Hazard
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ExampleReasoning:
Real Madrid is an instance of football team
Real Madrid is an instance of large team
Real Madrid is an instance of team
Real Madrid is an instance of sports team
Real Madrid has at least 11 members
All members in Real Madrid are football players
All members in Real Madrid are athletes
Real Madrid has member Eden Hazard
Eden Hazard is an instance of football player
Eden Hazard is an instance of athlete
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Start
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Create relation hasMember
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Create relation hasMember
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Create concept Team
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Edit concept Team using object restriction creator
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Edit concept Team using object restriction creator
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Create concept Large-Team
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Edit concept Large-Team using class expression editor
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Edit concept Large-Team using class expression editor
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Reasoning
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After adding all other concepts and reasoning
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Creating individual Real-Madrid and asserting it is a Football-Team
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Creating individual Real-Madrid and asserting it is a Football-Team
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Creating individual Eden-Hazard
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Creating individual Eden-Hazard
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Asserting the relation hasMember between Real-Madrid and Eden-Hazard
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Asserting the relation hasMember between Real-Madrid and Eden-Hazard
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Reasoning
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Nanoparticle Ontology
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The Celestial Emporium of Benevolent Knowledge, Borges"On those remote pages it is written that animals are divided into:
a. those that belong to the Emperor
b. embalmed ones
c. those that are trained
d. suckling pigs
e. mermaids
f. fabulous ones
g. stray dogs
h. those that are included in this classification
i. those that tremble as if they were mad
j. innumerable ones
k. those drawn with a very fine camel's hair brush
l. others
m. those that have just broken a flower vase
n. those that resemble flies from a distance"
Defining ontologies is not so easy ...
Slide from talk by C. Goble
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Defining ontologies is not so easy ...
Dyirbal classification of objects in the universe
Bayi: men, kangaroos, possums, bats, most snakes, most fishes, some birds, most insects, the moon, storms, rainbows, boomerangs, some spears, etc.
Balan: women, anything connected with water or fire,bandicoots, dogs, platypus, echidna, some snakes, some fishes, most birds, fireflies, scorpions, crickets, the stars, shields, some spears, some trees, etc.
Balam: all edible fruit and the plants that bear them, tubers, ferns, honey, cigarettes, wine, cake.
Bala: parts of the body, meat, bees, wind, yamsticks, some spears, most trees, grass, mud, stones, noises, language, etc.
Slide from talk by C. Goble
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Difficulties
Knowledge engineering
Is-a vs part-of
Concept vs individual
Synonyms as concepts, unconnected terms,
cycles in the hierarchy, domain and range
problems,missing disjointness …
http://oops.linkeddata.es/catalogue.jsp
Domain expert + knowledge engineer
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Difficulties
Completeness
Missing information
Correctness
Incoherence
Inconsistency
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Ontology tools
Ontology development tools
Ontology completion and debugging tools
Ontology alignment tools
Ontology evaluation tools
Ontology-based annotation tools
Ontology storage and querying tools
Ontology learning tools
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Debugging and Completing Ontologies
RepOSE
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Ontology tools
Ontology development tools
Ontology completion and debugging tools
Ontology alignment tools
Ontology evaluation tools
Ontology-based annotation tools
Ontology storage and querying tools
Ontology learning tools
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Aligning Ontologies
SAMBO
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Ontology tools
Ontology development tools
Ontology completion and debugging tools
Ontology alignment tools
Ontology evaluation tools
Ontology-based annotation tools
Ontology storage and querying tools
Ontology learning tools
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69
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Further reading Ontologies
Staab and Studer, Handbook on Ontologies, 2nd ed, Springer, 2009.
Lambrix, Strömbäck, Tan, Information integration in bioinformatics with ontologies and standards, in Bry, Maluszynski (eds), Semantic Techniques for the Web: The REWERSE perspective, chapter 8, 343-376, 2009.
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Description logics
Baader, Calvanese, McGuinness, Nardi, Patel-Schneider.
The Description Logic Handbook, Cambridge University
Press, 2003.
Donini, Lenzerini, Nardi, Schaerf, Reasoning in description
logics. Principles of knowledge representation, CSLI
publications, pp 191-236. 1996.
dl.kr.org
www.w3.org
Further reading
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OWL
https://www.w3.org/TR/owl-guide/
OWL2
https://www.w3.org/TR/owl2-primer/
Further reading
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Tools
Development
Protégé https://protege.stanford.edu/
Pitfalls
OOPS http://oops.linkeddata.es/
Debugging and completion
Repose
https://www.ida.liu.se/~patla00/research/RepOSE/
Ontology alignment
http://www.ontologymatching.org/
http://oaei.ontologymatching.org/
Further reading