A little more semantics goes a lot further! Getting more out of Linked Data with OWL

A little more semantics goes a lot further!
Getting more out of Linked Data with OWL
Dr. Michel Dumontier
Dr. Robert Hoehndorf

Abstract
This tutorial will provide detailed instruction to create and make use of formalized ontologies
from linked open data for advanced knowledge discovery including consistency checking and
answering sophisticated questions.
Automated reasoning in OWL offers the tantalizing possibility to undertake advanced
knowledge discovery including verifying the consistency of conceptual schemata in
information systems, verifying data integrity and answering expressive queries over the
conceptual schema and the data. Given that a large amount of structured knowledge is now
available as linked data, the challenge is to formalize this knowledge iso that intended
semantics become explicit and that the reasoning is efficient and scalable. While using the
full expressiveness of OWL 2 yields ontologies that can be used for consistency verification,
classification and query answering, use of less expressive OWL profiles enable efficient
reasoning and support different application scenarios. In this tutorial,
- we describe how to generate OWL ontologies from linked data
- check consistency of knowledge
- automatically transform ontologies into OWL profiles
- use this knowledge in applications to integrate data and answer sophisticated questions
across domains.
- expressive ontologies enables data integration, verifying consistency of knowledge and
answering questions
- formalization of linked data will create new opportunities for knowledge discovery
- OWL 2 profiles support more efficient reasoning and query answering procedures
- recent technology facilitates the automatic conversion of OWL 2 ontologies into profiles
- OWL ontologies can dramatically extend the functionality of semantically-enabled web sites
OWLED2011::Dumontier|Hoehndorf::Formalizing Linked Data Tutorial 2

skills obtained
• understand the nature and capability of a formal ontology and information system
• understand the subtle differences between OWL2 and its profiles, including difference in
constructs, when to apply these profiles and how to convert ontologies in this format
• understand the distinction between a class and an individual and their descriptions
• understand how to convert RDF triples in Linked Data into axioms for an OWL ontology
• understand how to execute standard reasoning services (classification, consistency
checking, realization, query answering) on an OWL ontology using the OWL API and an
OWL reasoner, with focus on OWL-EL ontologies and reasoners.
• understand how to identify inconsistencies and simple patterns to remove or repair them
• Understand how to convert large amounts of linked data into a large scale OWL
knowledge base and enable tractable reasoning over it

90 min Outline
1. introduction (10min)
• case study: SGD
• linked data vs ontology
• RDF vs OWL
• Motivation: can we use some features of OWL to organize, verify and exploit Linked Data?
2. Formalization
• OWL2 – elements, expressions and axioms
• Triples to axioms
• Role of top level ontologies (classes + relations)
• Axiom patterns
3. Practical Reasoning
• classification using CEL/CB/Pellet/HermiT/...
• OWL profiles
• Modularization (EL Vira)
• Diagnosis and Repair
• Explanations
• Inference of new triples
4. Conclusion
4OWLED2011::Dumontier|Hoehndorf::Formalizing Linked Data Tutorial

Saccharomyces Genome Database
A repository for all things yeast.
includes :
• molecular entities, their parts
o chromosomes; genes, open reading frames, etc
o rna, proteins; domains
• qualities, realizables (dispositions, functions)
• interactions and their participants
• complexes, their parts and their topology
• pathways and their components
• phenotypes and their basis

Hexokinase (HXK1)
The HXK1 gene encodes the HXK1 protein - which is
responsible for the conversion of glucose to glucose-6-
phosphate in the first step of glycolysis.
Gene:
(region of DNA)
Protein
(macromolecule)

Questions we may want to ask about HXK1:
• What kind of thing is HXK1?
• What are the implications of being a gene?
o In which chromosome does it appear?
o Which entities does it encode?
• What are the implications of being a protein?
o What is its function?
o Where is it located in the cell?
o If HXK1 participates in processes that involves other
cellular components, where else must HXK1 be located?
• Is HXK1 annotation consistent?
o does the annotation contradict common biological
knowledge?
o Is it possible for HXK1 to have multiple locations when it
can only be located on one chromosome?

SGD refers to other data sources
Gene Ontology
- functions, locations, processes
Ascomycetes Phenotype Ontology
- experiments, interactions and phenotypes
Pubmed
- abstracts of published research articles + MeSH terms
over 40 references to other molecular/data entities for which
the relation is unclear…

Bio2RDF’s
RDFized data
fits together
9syntactic integration OWLED2011::Dumontier|Hoehndorf::Formalizing Linked Data Tutorial

SGD as RDF-based Linked Open Data

Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
SGD is provided by Bio2RDF and forms part of the
growing linked open data cloud

Semantic Integration
• Requires a level of abstraction/generalization
where the relationship between each resource is
formalized
– classes
– relations
• How do we ensure that our representation
facilitates integration across datasets?
• How can we get our formalization to interoperate
with ontologies?

Early conceptualization

More advanced conceptualization

Semantic Technologies: RDF vs OWL
RDF: simple triples, graph-based queries, supports
very large amount of data
OWL: significantly more expressive language, strong
axioms, inference capabilities, consistency
verification, but can be rather slow

RDF-based Linked Data
• Provides the basis for simple data syndication and
syntactic data integration
o IRIs
o Statements (aka triples) take the form of
o <subject> <predicate> <object>
• Easy to implement
o stand-alone datasets
o logical layer over databases
• Limited reasoning
o class and property hierarchies
o domain/range restrictions
o can’t automatically discover inconsistency
• Standardized Queries - SPARQL
• Scalable - to billions of triples

OWL - The Web Ontology Language
• Enhanced vocabulary (strong axioms) to express
knowledge relating to classes, properties, individuals and
data values
o quantifiers (existential, universal, cardinality restriction)
o negation
o disjunction
o property characteristics
o complex classes in domain and range restrictions
o property chains
• Advanced reasoning

Advanced Reasoning
• Consistency: determines whether the ontology contains
contradictions.
• Satisfiability: determines whether classes can have
instances.
• Subsumption: is class C1 implicitly a subclass of C2?
• Classification: repetitive application of subsumption to
discover implicit subclass links between named classes
• Realization: find the most specific class that an individual
belongs to.

OWL Challenges and Solutions
Inconsistency:
• needs to be resolved to ask any questions involving the
ontology
• Solution: explicitly accommodate multiple meanings,
remove contradictory axioms
Unsatisfiability (of a class):
• may indicate a modelling error
• needs to be resolved to ask meaningful questions about the
class
• Solution: explicitly accommodate multiple meanings,
redefine class, remove contradicting class restrictions

OWL Challenges and Solutions
Scalability:
• answers to OWL queries requires reasoning
• inference in OWL is highly complex (worst case: 2
NEXPTIME)
• highly optimized reasoners are getting better and better, but
can still be slow with large ontologies
• tractable OWL profiles (EL, QL, RL) enable more efficient
and guaranteed polynomial-time inferences
• use ontology modularization approaches to increase
performance

Linked data and OWL: Motivation
• use OWL reasoning to identify mistakes in RDF data
o incorrect content of assertions
o incorrect use of relations
o conflicting conceptualizations
o incorrect same-as assertions
• verify, fix and exploit Linked Data through expressive OWL
reasoning
• generate/infer new triples to write back into RDF and use
for efficient retrieval
Proposal:
Convert RDF to OWL to perform inferences and represent
inferences in RDF after classification.

OWL can help you create rich, machine-
understandable descriptions!
• transform our expert knowledge into axioms and
expressions that can be automatically reasoned about
o a transcription factor is
 a protein
 that binds to DNA
 and regulates the expression of a gene.
o can we mine 'omic datasets to discover which
proteins are transcription factors?
• create rich expressions from combinations of classes,
relations and individuals
• assert statements of truth using axioms.

Elements of OWL 2.0
• The “ontology” of OWL 2 consists of:
• Classes
• Object properties
• Data properties
• Individuals
• Expressions
• Axioms
• Plus RDF stuff (like datatypes)

Classes and class axioms
• a class is a set of individuals that share one or more characteristics
o a protein
• classes can be organized in a hierarchy using subClassOf axioms
o i.e. every member of C2 is a member of C1
o subClassOf (protein molecule)
• special classes
o owl:Thing is the superclass of all things
o owl:Nothing is the subclass of all things, denotes an empty set
• classes can be made disjoint from one another
o i.e. there is no member of C1 that is also a member of C2
o disjointClasses (protein DNA )
• classes can be said to be equivalent
o i.e. all members of C1 are members of C2 and all members of C2
are members of C1
o EquivalentClass (Peptide Polypeptide )

Object Properties and axioms
• an object property OP is a relation between two individuals
o 'has part' is an object property that denotes the mereological
relation between two individuals
• OPs can be organized in a hierarchy
o given OP1 and OP2 and OP2 is a subproperty of OP1 then if an
individual x is connected by OP2 to an individual y, then x is
also connected by OP2 to y.
o subPropertyOf ('has proper part' 'has part')
o owl:TopObjectProperty, owl:BottomObjectProperty
• We can restrict the domain and range to allowed values
• ObjectPropertyDomain ('is participant in', 'process')
• ObjectPropertyRange ('is participant in', 'physical entity')
• We can also assert objects to be disjoint or equivalent

description of object properties
• Inverse
o we say that 'has part' is an inverse for 'is part of'
o we can also refer to this as inv('is part of')
• Symmetric
o to cases where the inverse relation is the very same relation
o e.g. the inverse for 'is related to' is 'is related to‘
• Transitive
o a transitive relation if individual x is connected to an individual y
that is connected by to an individual z, then x is also connected by
to z
o e.g. 'has part' is transitive

description of object properties
• Reflexive
o reflexive infers that the relation automatically refers back to the
individual
o e.g. 'has part' is reflexive because protein has itself as a part.
• Functional
o restrict the range of the relation to a single individual, and therefore
all individuals in the range must be the same.
o e.g. 'has unique identifier‘
• Inverse Functional
o restrict the domain of the relation to a single individual, therefore all
individuals in the domain must be the same
o e.g. 'is unique identifier of'

Class Expressions
Class expressions are rich descriptions of classes through the
logical combination of ontological primitives (classes, object
properties, datatype properties, individuals)
Protein subClassOf
molecule and ‘has proper part’ min 2 ‘amino acid residues’
Combinations specified using logical operators
• conjunction (and), disjunction (or), negation (not)
Object or data property expressions provide a qualified cardinality
over the relation
o minimum: rel min # Y
o maximum: rel max # Y
o exact: rel exactly # Y (minimum + maximum)
o some: rel min 1 Y

Class Expressions
o The quantifications can qualified by the object type
o rel only Y – the only values allowed are of type Y
• To form complex class expressions like
o 'molecule' and not 'dna'
o 'has part' min 2 'amino acid'
o 'is located in' only ('nucleus' or 'cytoplasm')
• and be expressed as axioms in the ontology
Protein subClassOf
molecule and ‘has proper part’ min 2 ‘amino acid residues’
Transcription Factor equivalentClass
‘protein’
and ‘has disposition’ some ‘to bind to DNA’
and ‘has function’ some ‘to regulate gene expression’

Triples to axioms
Convert RDF triples into OWL axioms.
Triple in RDF:
<Nucleus> <partOf> <Cell>
• Nucleus and Cell are classes
• partOf is a relation between 2 classes
• intended meaning:
every instance of Nucleus is partOf some instance of Cell
• formalize as OWL axiom:
Nucleus subClassOf
partOf some Cell

Triples to axioms
Triple in RDF:
<Cytosol> <isLocationOf> <HXK1>
• Cell and HXK1 are classes
• isLocationOf is an axiom pattern involving 2 classes
• every instance of HXK1 is located at some instance of Cytosol
• not intended:
• for every instance of Cytosol, there is an instance of HXK1 located
in it.
HXK1 subClassOf
hasLocation some Cytosol
inv(isLocationOf) some Cytosol

Triples to axioms
Convert RDF triples into OWL axioms.
Triple in RDF:
<C1 R C2>
• C1 and C2 are classes, R a relation between 2 classes
o C1 SubClassOf: C2
o C1 SubClassOf: R some C2
o C1 SubClassOf: R only C2
o C2 SubClassOf: R some C1
o C1 SubClassOf: S some C2
o C1 DisjointFrom C2
o C1 and C2 SubClassOf: owl:Nothing
o R some C1 DisjointFrom: R some C2
o C1 EquivalentClasses C2
o ...
• in general: P(C1, C2), where P is an OWL axiom (template)
Challenge:
Formalizing data requires
one to commit to a
particular meaning – to
make an ontological
commitment

Triples to axioms
Formalizing RDF triples in OWL often introduces new OWL
object properties.
• Which object properties should be included?
• What axioms hold for included object properties?
• Can domain and range restrictions be generalized across
multiple domains, i.e., reused across multiple linked data
sources to ensure consistency between them?
Challenges

Top level ontologies contain
generalized (domain independent)
classes and relations
They can be used to constrain what can be said about these
entities (and hence will later be useful for checking the
consistency of data annotated using these terms).

Basic classes in top-level ontologies
• Material entity
• Example: Apple, Human, Cell, Planet
• Has mass as an quality
• Located in space and time
• Independent of other entities
• it exists in whole whenever it exists
• Quality
• Example: mass, color, concentration
• Dependent: always the quality of some entity
• Quality of object: size, shape, length
• Quality of process: duration, rate
• Quality of quality: shade (of color), intensity

Basic classes in top-level ontologies
• Function
• e.g. to bind, to catalyze (a reaction), to kill bacteria
• Dependent: always the function of some thing
• Similar to a property of an object
• Represents the potential to do something (an action) in
some process
• capabilities, dispositions and tendencies
• Process
• Example: running a marathon, binding, cell division
• Located in space and time
• Independent of other entities
• Temporally extended

Top-level ontologies make a
commitment to these being different
things
Material object, Process, Function and Quality are mutually
disjoint.

Basic Relations in Top Level Ontologies
• Mereological: parthood
– ‘has part’, ‘has proper part’, ‘has component part’
• Participatory
– ‘is participant in’, ‘is agent in’, ‘is target in
• Topology
– ‘is connected to’, ‘located in’, ‘contains’, ‘is adjacent to’
• Temporal
– ‘derives from’, ‘precedes’, ‘meets’, ‘overlaps’, etc
• Referential
– ‘describes’, ’references’, ‘represents’

Relations in top-level ontologies
• relations (object properties) in OWL hold between
instances
• domain and range restrictions from top-level ontology
can be applied for general relations, e.g.:
o ‘has part’ can be restricted with "Material object" as
both domain and range
o ‘participates in’ can be restricted with a domain of
"Material object" and a range of "Process“
o re-use of relations enables inferences across
resources

Enforce ontological commitment by
mapping to a top-level ontology
Foundation of domain classes and relations in top-level
ontology:
• every domain class becomes a subclass of a class in top-
level ontology
• every object property used in OWL axioms becomes a sub-
property of an object property in the top-level ontology
• assert additional axioms to restrict domain classes and
delimit it from other domains (where appropriate)
o e.g., if a particular resources uses (in RDF) the relation
part-of exclusively between processes, the additional
constraint can be added

Top-level ontology
Application of a top-level ontology:
• can help to make the ontological commitment that is
employed within an information system explicit,
• can guarantee basic agreement about fundamental types,
• agreement about common relations,
• provides common domain and range restrictions across
multiple domains, and therefore
• enables re-use of relations and types across data sources,
domains, levels of granularities, information systems.

Formalization of SGD’s Linked Data
SGD uses at least the following relations in RDF:
• isPartOf
• hasParticipant
• isFunctionOf
• isLocationOf
Can we create patterns from which linked data can be
appropriately formalized into OWL axioms?
axiom patterns

Formalization of SGD Linked Data
?X isPartOf ?Y
Can be translated to axiom pattern
?X subClassOf: part-of some ?Y
"part-of" is an object property contained in our top-level
ontology.
Example:
HXK1 isPartOf chromosome6_Crick
translated to
HXK1 subClassOf: part-of some chromosome6_Crick

?X hasParticipant ?Y
translated to axiom pattern
?Y subClassOf: participates-in some ?X
"participates-in" is an object property contained in our top-level
ontology.
Example:
GO:0005975 (carbohydrate metabolism) hasParticipant HXK1
translated to
HXK1 subClassOf: participates-in some GO:0005975

?X isLocationOf ?Y
translated to axiom schema
?Y subClassOf: located-in some ?X
Example:
GO:0005737 (cytoplasm) isLocationof HXK1
translated to
HXK1 subClassOf: located-in some GO:0005737
What if "located-in" is not present in our top-level ontology…

Top-level foundation for located-in relation:
• declare located-in as sub-property of part-of
o verify how located-in is used within SGD, i.e., does
located-in imply part-of?
o counter-example: misfolded protein located-in chaperone
protein, but not misfolded protein part-of chaperone
protein
• create located-in as super-property of part-of in our top-
level ontology:
o does part-of imply located-in within SGD?
o cell body part-of cell, but not cell body located-in cell

Top-level foundation for located-in relation:
• add located-in to our top-level ontology
o adding the new relation allows its reuse across multiple
resources
o inclusion may require addition of further classes (e.g.,
spatial regions)
o relation to part-of must be clarified (and part-of may even
be replaced by located-in)
Establishing the relation between relations and classes
depends on how the relations and classes are being applied.

Top-level foundation:
Translate
HXK1 rdf:type OpenReadingFrame
to
HXK1 subClassOf: OpenReadingFrame
OpenReadingFrame (Sequence Ontology) is a subclass of
Sequence.

Foundation for SGD classes in top-level ontology:
• declare Sequence to be a subclass of Material object
• import (owl:imports) Sequence Ontology
• declare Biological Process (GO) subclass of Process
• declare Molecular Function (GO) subclass of Function
• import GO
• ...
to create a top-level foundation (i.e., super-class in top-level
ontology for all classes) for SGD

Implementation
• expand relations in RDF based on relational patterns
• relational patterns are OWL axioms with 2 variables (which
are filled by subject and object, respectively)
• implementation based on OWL API
• adopt implementation of relational patterns in OBO
language (http://code.google.com/p/obo2owl/)
Hoehndorf, Robert, Oellrich, Anika, Dumontier, Michel, Kelso, Janet, Herre,
Heinrich, and Rebholz-Schuhmann, Dietrich (2010). Relational patterns in OWL
and their application to OBO. OWL: Experiences and Directions (OWLED).
paper: http://www.webont.org/owled/2010/papers/owled2010_submission_3.pdf
presentation: http://www.slideshare.net/micheldumontier/relational-patterns-in-
owl-and-their-application-to-obo
BMC Bioinformatics: http://www.biomedcentral.com/1471-2105/11/441

Another way?
• OPPL is an abstract formalism that allows for
manipulating ontologies written in OWL.
• Use OPPL to select triples and create the axioms

Operations on OWL ontologies
• Consistency: determines whether the ontology contains
contradictions.
• Satisfiability: determines whether classes can have
instances.
• Subsumption: is class C1 implicitly a subclass of C2?
• Classification: repetitive application of subsumption to
discover implicit subclass links between named classes
• Realization: find the most specific class that an individual
belongs to.
OWL reasoners can perform these operations and make the
results accessible for further processing.

Practical reasoning with OWL
ontologies
• Ontology editors such as Protege interface with reasoners to
perform consistency and class satisfiability,
classification, realisation, and provide explanations.
• Some reasoners are setup to be used as the command line
to execute requests including SPARQL querying.
• Programmatic use of reasoners via APIs. Maximal flexibility,
e.g., one can request all subclasses of a given class,
including implicit once, or all entailed statements with a
specified subject and predicate

OWLAPI

Classifying the ontology

OWL Reasoners
OWL DL Reasoners
• Pellet: Clark & Parsia, dual-licensed, Java.
• Fact++: Manchester University, open-source, C++ with a Java API.
• HermiT: Oxford University, open-source, Java.
• Racer Pro: Racer Systems, commercial, Lisp with a Java API.
OWL Profile/subset reasoners
• Jena: Hewlett-Packard, open-source, Java.
• OWLIM: Ontotext, dual-licensed, Java.
• CB:
• CEL:
• JCEL (Pellet)
• ELLY:

Automated reasoning over SGD
• SGD in OWL contains more than 800,000 axioms
• included ontologies contains several thousand axioms
o GO has approx. 35,000 classes
o ChEBI contains almost 100,000 classes
o complex definitions of classes create links between large
ontologies
• Reasoning in OWL 2 DL is highly complex (worst-case
2NEXPTIME complete).
• Consequence: OWL reasoning can rarely be employing in a
large scale.
• Expressive OWL reasoners do not classify the formalized
SGD repository.

OWL Profiles
• OWL 2 defines three different tractable profiles:
• EL
o polynomial time reasoning for schema and data
o Useful for ontologies with large conceptual part
• QL
o fast (logspace) query answering using RDBMs via SQL
o Useful for large datasets already stored in RDBs
• RL
o fast (polynomial) query answering using rule-extended
DBs
o Useful for large datasets stored as RDF triple

OWL RL
Features:
• identity of classes, instances, properties
• subproperties, subclasses, domains, ranges
• union and intersection of classes (some restrictions)
• property characterizations (functional, symmetric, etc)
• property chains
• keys
• some property restrictions (but not all inferences are
possible)
Limitations:
• not all datatypes are available
• no datatype restrictions
• no minimum or exact cardinality restrictions
• maximum cardinality only with 0 and 1
• some consequences cannot be drawn

OWL EL
Features
• existential quantification to a class expression or data range
• existential quantification to an individual or a literal
• self-restriction
• enumerations involving a single individual or a single literal
• intersection of classes and data range
• class axioms: subClassOf, equivalence, disjointness
• property axioms: domain, range, equivalence, transitive, reflexive, inclusion with
or without property chains; functional data properties. keys.
• assertions (sameAs, DifferentFrom, Class, Object Property, Data Property,
Negative Object/Data Property
Not supported
• universal quantification to a class expression or a data range
• cardinality restrictions
• disjunction (union)
• class negation
• enumerations involving more than one individual
• object properties: disjoint, symmetric, asymmetric, irreflexive, inverse, functional
and inverse-functional

Ontology modularization
Can we automatically extract a large (maximal) OWL (EL, QL,
RL) module from an ontology?
1. D EquivalentTo: not A (not EL)
2. C EquivalentTo: not B (not EL)
3. B subClassOf: A (EL)
Inference:
• D subClassOf: C (EL) (Inference from (1)-(3))
EL module of (1)-(3):
• {B subClassOf: A}, or
• {B subClassOf: A, D subClassOf: C}

EL Vira modularization
• ontology modularization
• identify EL, QL, RL axioms in deductive closure
• retain signature of ontology
• maximality is an open problem
http://el-vira.googlecode.com

Consistency repair
• Unsatisfiable classes result from contradictory class
definitions
• Conflict in asserted axioms, in imported ontologies or
through combination of both
• Conflicts can be hidden through domain/range
restrictions, subclass relations, axioms for relations, etc.
• Conflicting axioms may be challenging to identify!

Protege 4: Explanations

Consistency repair

Ontology repair and disambiguation
• Ontological constraints may have been too strong
• Complex relations (between classes) that are used in
multiple meanings can be relaxed by explicitly introducing
a disjunction that accommodates the different meanings,
e.g.:
o (1) Hxk1 part-of Chromosome6_Crick_strand
o (2) Hxk1 part-of Hxk1_ATP_complex
o (3) Hxk1 part-of Carbohydrate_metabolism
o only (1) is consistent with background knowledge that
Genes (as material objects) must be part of material
objects (more specifically DNA), and that Genes cannot
be part of protein complexes

1. Hxk1 part-of Chromosome6_Crick_strand
2. Hxk1 part-of Hxk1_ATP_complex
3. Hxk1 part-of Carbohydrate_metabolism
part-of here means either
?X subClassOf: part-of some ?Y, or
?X subClassOf: encodes some (part-of some ?Y), or
?X subClassOf: participates-in some ?Y, or
?X subClassOf: encodes some (participates-in some ?Y)

?X subClassOf: part-of some ?Y, or
?X subClassOf: encodes some (part-of some ?Y), or
?X subClassOf: participates-in some ?Y, or
?X subClassOf: encodes some (participates-in some ?Y)
All four interpretations are disjoint!
Create new interpretation for part-of:
?X subClassOf:
part-of some ?Y or
encodes some (part-of some ?Y) or
participates-in some ?Y or
encodes some (participates-in some ?Y)

Inference of revised RDF
representation
• Query OWL ontology for relational patterns that were used
in relation expansion
• generates deductive closure of a set of RDF triples with
respect to inferences in OWL
• naive implementation:
o given a pattern P(?X, ?Y), substitute all combination of
named classes for ?X and ?Y
o runtime: n*n
o more efficient implementation work-in-progress

Inference of revised RDF
representation
In the definition:
?X subClassOf:
part-of some ?Y or
encodes some (part-of some ?Y) or
participates-in some ?Y or
encodes some (participates-in some ?Y)
one or more of the classes in the disjunction may become
unsatisfiable!
• reasoner can be used to decide which interpretation is
correct
• eliminate remaining interpretations
• useful to "split" relations in RDF that have multiple
conflicting meanings

Summary - RDF and OWL
RDF provides
• light-weight semantics
• fast queries
• highly scalable implementations
• large volumes of data (e.g., DBPedia, other Linked Data
repositories)
OWL provides
• Constructs to formalize the intended semantics
• An OWLAPI to develop, manage, and serialize OWL
ontologies
• Efficient reasoners of get inferences, compute modules and
get explanations.
• syntactic subset for better performance, albeit some
inferences may be lost

Summary - Reasoning in OWL
• verification: reveal contradictory definitions of classes
(unsatisfiable classes), conflicting conceptualizations and
reveal hidden inferences (that may be considered invalid
through manual verification)
• repair: through explicit definitions using disjunction,
constraints can be relaxed and contradictions reduced
• more facts: OWL queries for relational patterns can be
used to generate RDF triples that are closed against the
constraints and axioms of an OWL knowledge base
• powerful queries: queries in OWL can be made for
instances and for classes satisfying complex expressions

Conclusions
• ontologies are tools for better knowledge management
• ontology (philosophy) is a useful source of well-developed
theories that can be applied to ontology design, but only
when put into practice as a formalized ontology
• formal ontologies can help in getting us closer to the goal of
large-scale integration, verification and analysis of data
across domains and levels of granularity
• The combination of formal ontologies + scalable reasoning
will be instrumental in making sense of the Semantic Web.

A little more semantics goes a lot further! Getting more out of Linked Data with OWL

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A little more semantics goes a lot further! Getting more out of Linked Data with OWL