Ontology engineering

1
Ontology engineering

• Valentina Tamma
• Based on slides by A. Gomez Perez, N. Noy, D.
  McGuinness, E. Kendal, A. Rector and O. Corcho

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Content

• Background on ontology
• Ontology and ontological commitment
• Logic as a form of representation
• Ontology development phases
• Modelling problems and patterns
• N-ary relationships
• Part whole relationships

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What Is Ontology Engineering?

• Ontology Engineering
• Defining terms in the domain and relations among
  them
• Defining concepts in the domain (classes)
• Arranging the concepts in a hierarchy
  (subclass-superclass hierarchy)
• Defining which attributes and properties (slots)
  classes can have and constraints on their values
• Defining individuals and filling in slot values

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Methodological Questions

• How can tools and techniques best be applied?
• Which languages and tools should be used in which
  circumstances, and in which order?
• What about issues of quality control and resource
  management?
• Many of these questions for ontology engineering
  have been studied in other contexts
• E.g. software engineering, object-oriented
  design, and knowledge engineering

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Historical context
Philosophy
Artificial Intelligence
Ontology
Knowledge Representation and logic
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Philosophical roots

• Socrates questions of being, Platos studies of
  epistemology
• the nature of knowledge
• Aristotles classifications of things in the
  world and contribution to syllogism and inductive
  inference
• logic as a precise method for reasoning about
  knowledge
• Anselm of Canterbury and ontological arguments
  deriving the existence of God
• Descartes, Leibniz,

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In computer science

• Cross-disciplinary field with historical roots in
  philosophy, linguistics, computer science, and
  cognitive science
• The goal is to provide an unambiguous description
  of the concepts and relationships that can exist
  for an agent or a community of agent, so they can
  understand, share, and use this description to
  accomplish some task on behalf of users

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So what is an ontology then?
An ontology is a (formal), explicit specification
of a shared conceptualisation
T. Gruber, 1993 R. Studer, V. R. Benjamins, and
D. Fensel, 1998
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What is a conceptualisation

• Conceptualisation the formal structure of
  reality as perceived and organized by an agent,
  independently of
• the vocabulary used (i.e., the language used)
• the actual occurrence of a specific situation
• Different situations involving the same objects,
  described by different vocabularies, may share
  the same conceptualisation.

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Logic as a representation formalism

• Predicate logic is more precise than natural
  language, but it is harder to read
• Every trailer truck has 18 wheels

From John F. Sowa Knowledge Representation
Logical, Philosophical, and Computational
Foundations, Brooks/Cole, 2000.
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Logic as a representation formalism

• Logic is a simple language with few basic
  symbols.
• The granularity of representation depends on the
  choice of predicates i.e. an ontology of the
  relevant concepts in the domain.
• Different choices of predicates (with different
  interpretations) represent different ontological
  commitments.

From John F. Sowa Knowledge Representation
Logical, Philosophical, and Computational
Foundations, Brooks/Cole, 2000.
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Ontological commitment

• Agreement on the meaning of the vocabulary used
  to share
• knowledge.

A pipe ?!?
We need a pipe
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Knowledge engineering

• Knowledge engineering is the application of logic
  and ontology to the task of building computable
  models of some domain for some purpose. John
  Sowa

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Level of Granularity

• An ontology specifies a rich description of the
• Terminology, concepts, vocabulary
• Properties explicitly describing concepts
• Relations among concepts
• Rules distinguishing concepts, refining
  definitions and relations (constraints,
  restrictions, regular expressions) relevant to a
  particular domain or area of interest.

Based on the AAAI99 Ontology Panel McGuinness,
Welty, Uschold, Gruninger, Lehman
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Ontology based information systems

• Ontologies provide a common vocabulary and
  definition of rules defining the use of the
  ontologies by independently developed resources,
  processes, services
• Agreements among companies, organizations sharing
  common services can be achieved with regard to
  their usage and the meaning of relevant concepts
  can be expressed unambiguously

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Ontology based information systems

• By composing component ontologies, mapping
  ontologies to one another and mediating
  terminology among participating resources and
  services, independently developed systems, agents
  and services can work together to share
  information and processes consistently,
  accurately, and completely.

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Ontology based information systems

• Ontologies also facilitate conversations among
  agents to collect, process, merge, and exchange
  information.
• Improve search accuracy by enabling contextual
  search through the use of concept definitions and
  relations among them.
• Used instead of/in addition to statistical
  relevance of keywords.

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Ontology design process
Really more like
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Ontology design process
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Ontology design process
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Requirement analysis

• Performing Requirements, Domain Use Case
  Analysis is a critical stage as in any software
  engineering design. It allows ontology engineers
  to ground the work and prioritise.
• The analysis has to elicit and make explicit
• The nature of the knowledge and the questions
  (competency questions) that the ontology (through
  a reasoner) needs to answer. This process is
  crucial for scoping and designing the ontology,
  and for driving the architecture
• Architectural issues
• The effectiveness of using traditional
  approaches with knowledge intensive approaches

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Aim

• The main goal of this phase is to support the
  application in dealing with
• Changing assumptions
• Hypothesis generation (analogy)
• System evolution, or dynamic knowledge
  evolution - where time
• and situations change necessitating re-evaluation
  of assumptions
• Support for interoperation with other
  (potentially legacy) systems
• - Generation of explanation for dialogue
  generation facilitate interface with users
• - Standardization of terminology to reflect the
  engineers different backgrounds
• Separation of concerns is crucial when dealing
  with knowledge
• Declarative domain knowledge (what?) needs to be
  treated differently from procedural knowledge
  (how?)
• Ontologies vs Problem solving methods
• Background (unchanging) knowledge from changing
  information
• Provenance and level of trust of knowledge

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Application requirements

• Application requirements can be acquired by
• Identifying any controlled vocabulary used in the
  application
• Identifying hierarchical or taxonomic structures
  intrinsic in the domain that might be used for
  query expansion
• Vegetarian pizza such as margherita, funghi,
  grilled vegetables pizza
• Analysing structured queries and the knowledge
  they require
• Expressive power required Efficient inference
  (requiring limited expressive power) vs.
  increased expressivity (requiring expensive or
  resource bounded computation)
• Ad-hoc reasoning to deal with particular domain
  requirements
• temporal relations, geospatial, process-specific,
  conditional operations
• Computational tractability
• Need for Explanations, Traces, Provenance

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Domain requirements

• Take into account heterogeneity, distribution,
  and autonomy needs
• software agents based applications
• Open vs. Closed World (does lack of information
  imply negative
• information?)
• Static vs dynamic ontology processes
• Evolution, alignment
• Limited or incomplete knowledge
• Knowledge evolution over time
• Analysis and consistency checking of instance
  data
• Use Case analysis should facilitate the
  understanding of
• The information that is likely to be available
• The questions that are likely to be asked
• Types and roles of users

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Conceptual modelling

• A data model describes data, or database schemas
  an ontology describes the world
• Adam Farquhar, Ontology 101, Stanford
  University, 1997
• Resources and their relationships are described
  from an objective standpoint, and they do not
  reflect the definitions in databases, or the
  views of programmers.
• Experts from different backgrounds with
  significant domain knowledge will classify
  knowledge differently from someone interested in
  optimization of algorithms, or forcing
  information into an existing framework, or legacy
  applications
• Shortcuts at the top levels do not help
  automation and mapping among ontologies and
  terminology at lower levels provides significant
  benefit

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Ontology design process
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Determine ontology scope

• Addresses straight forward questions such as
• What is the ontology going to be used for
• How is the ontology ultimately going to be used
  by the software implementation?
• What do we want the ontology to be aware of, and
  what is the scope of the knowledge we want to
  have in the ontology?

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Competency Questions

• Which investigations were done with a
  high-fat-diet study?
• Which study employs microarray in combination
  with metabolomics technologies?
• List those studies in which the fasting phase had
  as duration one day.
• What is a vegetarian pizza?
• What type of wine can accompany seafood?

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Ontology design process
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Consider Reuse

• We rarely have to start from scratch when
  defining an ontology
• There is almost always an ontology available from
  a third party that provides at least a useful
  starting point for our own ontology
• Reuse allows to
• to save the effort
• to interact with the tools that use other
  ontologies
• to use ontologies that have been validated
  through use in applications

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Consider Reuse

• Standard vocabularies are available for most
  domains, many of which are overlapping
• Identify the set that is most relevant to the
  problem and application issue
• A component-based approach based on modules
  facilitates dealing with overlapping domains
• Reuse an ontology module as one would reuse a
  software module
• Standards complex relationships are defined such
  that term usage and overlap is unambiguous and
  machine interpretable
• Initial brainstorming with domain experts can be
  highly productive then subsequent refinement and
  iteration lead to the level required by the
  application

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What to Reuse?

• Ontology libraries
• DAML ontology library (www.daml.org/ontologies)
• Protégé ontology library (protege.stanford.edu/plu
  gins.html)
• Upper ontologies
• IEEE Standard Upper Ontology (suo.ieee.org)
• Cyc (www.cyc.com)
• General ontologies
• DMOZ (www.dmoz.org)
• WordNet (www.cogsci.princeton.edu/wn/)
• Domain-specific ontologies
• UMLS Semantic Net
• GO (Gene Ontology) (www.geneontology.org)

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Ontology design process
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Enumerate terms

• Write down in an unstructured list all the
  relevant terms that are expected to appear in the
  ontology
• Nouns form the basis for class names
• Verbs (or verb phrases) form the basis for
  property names
• Card sorting is often the best way
• Write down each concept/idea on a card
• Organise them into piles
• Link the piles together
• Do it again, and again
• Works best in a small group

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Example animals plants ontology

• Dog
• Cat
• Cow
• Person
• Tree
• Grass
• Herbivore
• Male
• Female

• Dangerous
• Pet
• Domestic Animal
• Farm animal
• Draft animal
• Food animal
• Fish
• Carp
• Goldfish

• Carnivore
• Plant
• Animal
• Fur
• Child
• Parent
• Mother
• Father

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Ontology design process
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Define classes and their taxonomy

• A class is a concept in the domain
• Animal (cow, cat, fish)
• A class of properties (father, mother)
• A class is a collection of elements with similar
  properties
• A class contains necessary conditions for
  membership (type of food, dwelling)
• Instances of classes
• A particular farm animal, a particular person
• Tweety the penguin

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Organise the conceptsExample Animals Plants

• Dog
• Cat
• Cow
• Person
• Tree
• Grass
• Herbivore
• Male
• Female

• Healthy
• Pet
• Domestic Animal
• Farm animal
• Draft animal
• Food animal
• Fish
• Carp
• Goldfish

• Carnivore
• Plant
• Animal
• Fur
• Child
• Parent
• Mother
• Father

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Extend the concepts Laddering

• Take a group of things and ask what they have in
  common
• Then what other siblings there might be
• e.g.
• Plant, Animal ? Living Thing
• Might add Bacteria and Fungi but not now
• Cat, Dog, Cow, Person ? Mammal
• Others might be Goat, Sheep, Horse, Rabbit,
• Cow, Goat, Sheep, Horse ? Hoofed animal
  (Ungulate)
• What others are there? Do they divide amongst
  themselves?
• Wild, Domestic ? Domestication
• What other states Feral (domestic returned to
  wild)

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Choose some main axes

• Add abstractions where needed
• e.g. Living thing
• identify relations (this feeds into the next
  step)
• e.g. eats, owns, parent of
• Identify definable things
• e.g. child, parent, Mother, Father
• Things where you can say clearly what it means
• Try to define a dog precisely very difficult
• A natural kind
• make names explicit

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Example

• Relations
• eats
• owns
• parent-of

• Living Thing
• Animal
• Mammal
• Cat
• Dog
• Cow
• Person
• Fish
• Carp
• Goldfish
• Plant
• Tree
• Grass
• Fruit

• Modifiers
• domestic
• pet
• Farmed
• Draft
• Food
• Wild
• Health
• healthy
• sick
• Sex
• Male
• Female
• Age
• Adult
• Child

• Definable
• Carinvore
• Herbivore
• Child
• Parent
• Mother
• Father
• Food Animal
• Draft Animal

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Identify self-standing entities

• Things that can exist on there own
• People, animals, houses, actions, processes,
• Roughly nouns
• Modifiers
• Things that modify (inhere) in other things
• Roughly adjectives and adverbs

43
Reorganise everything but definable things into
pure trees these will be the primitives

• Relations
• eats
• owns
• parent-of

• Self_standing
• Living Thing
• Animal
• Mammal
• Cat
• Dog
• Cow
• Person
• Pig
• Fish
• Carp Goldfish
• Plant
• Tree
• Grass
• Fruit

• Modifiers
• Domestication
• Domestic
• Wild
• Use
• Draft
• Food
• pet
• Risk
• Dangerous
• Safe
• Sex
• Male
• Female
• Age
• Adult
• Child

• Definables
• Carnivore
• Herbivore
• Child
• Parent
• Mother
• Father
• Food Animal
• Draft Animal

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Comments can help to clarify

• Self_standing
• Living Thing
• Animal
• Mammal
• Cat
• Dog
• Cow
• Person
• Pig
• Fish
• Carp Goldfish
• Plant
• Tree
• Grass
• Fruit

• Abstract ancestor concept including all living
  things restrict to plants and animals for now

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Class inheritance

• Classes are organized into subclass-superclass
  (or generalization-specialization)
• Hierarchies
• Classes are is-a related if an instance of the
  subclass is an instance of the superclass
• Classes may be viewed as sets
• Subclasses of a class are comprised of a subset
  of the superset
• Examples
• Mammal is a subclass of Animal
• Every penguin is a bird or every instance of a
  penguin (like Tweety is an instance of bird
• Draft animal is a subclass of Animal

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Levels in the class hierarchy

• Different modes of development
• Top-down - define the most general concepts
  first and then specialize them
• Bottom-up - define the most specific concepts and
  then organize them in more general classes
• Combination (typical breadth at the top level
  and depth along a few branches to test design)
• Class inheritance is Transitive
• A is a subclass of B
• B is a subclass of C
• therefore A is a subclass of C

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Levels in the class hierarchy
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Ontology design process
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Define properties

• Often interleaved with the previous step
• Properties (or roles in DL) describe the
  attributes of the members of a class
• The semantics of subClassOf demands that whenever
  A is a subclass of B, every property statement
  that holds for instances of B must also apply to
  instances of A
• It makes sense to attach properties to the
  highest class in the hierarchy to which they
  apply

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Define properties

• Types of properties
• intrinsic properties flavor and color of
  wine
• extrinsic properties name and price of wine
• parts ingredients in a dish
• relations to other objects producer of wine
  (winery)
• They are represented by data and object
  properties
• simple (datatype) contain primitive values
  (strings, numbers)
• complex properties contain other objects
  (e.g., a winery instance)

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Modifiers and relations

• Relations
• eats
• owns
• parent-of

• Modifiers
• Domestication
• Domestic
• Wild
• Use
• Draft
• Food
• pet
• Risk
• Dangerous
• Safe
• Sex
• Male
• Female
• Age
• Adult
• Child

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Ontology design process
53
Identify the domain and range constraints for
properties

• Animal eats Living_thing
• eats domain Animal range
  Living_thing
• Person owns Living_thing except person
• owns domain Person range
  Living_thing not Person
• Living_thing parent_of Living_thing
• parent_of domain Living_thing
• range Living_thing

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If anything is used in a special way,add a text
comment

• Animal eats Living_thing
• eats domain Animal range
  Living_thing

• ignore difference betweenparts of living
  thingsand living thingsalso derived from
  livingthings

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For definable things

• Paraphrase and formalise the definitions in terms
  of the primitives, relations and other
  definables.
• Note any assumptions to be represented elsewhere.
• Add as comments when implementing
• A Parent is an animal that is the parent of
  some other animal (Ignore plants for now)
• Parent Animal and parent_of some Animal
• A Herbivore is an animal that eats only
  plants(NB All animals eat some living thing)
• Herbivore Animal and eats only Plant
• An omnivore is an animal that eats both plants
  and animals
• Omnivore Animal and eats some Animal and
  eats some Plant

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Which properties can be filled inat the class
level now?

• What can we say about all members of a class?
• eats
• All cows eat some plants
• All cats eat some animals
• All pigs eat some animals eat
  some plants

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Fill in the details(can use property matrix
wizard)
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Check with classifier

• Cows should be Herbivores
• Are they? why not?
• What have we said?
• Cows are animals and, amongst other things,
  eat some grass and eat some leafy_plants
• What do we need to sayClosure axiom
• Cows are animals and, amongst other things,eat
  some plants and eat only plants

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Closure Axiom

• Cows are animals and, amongst other things,eat
  some plants and eat only plants

Closure Axiom
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In the tool

• Right mouse button short cut for closure axioms
• for any existential restriction

adds closure axiom
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Open vs Closed World reasoning

• Open world reasoning
• Negation as contradiction
• Anything might be true unless it can be proven
  false
• Reasoning about any world consistent with this
  one
• Closed world reasoning
• Negation as failure
• Anything that cannot be found is false
• Reasoning about this world
• Ontologies are not databases

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Ontology design process
63
Creating instances

• Create an instance of a class
• The class becomes a direct type of the instance
• Any superclass of the direct type is a type of
  the instance
• Assign slot values for the instance frame
• Slot values should conform to the facet
  constraints
• Knowledge-acquisition tools often check that
  constraints are satisfied

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Creating instances

• Filling the ontologies with such instances is a
  separate step
• Number of instances gtgt number of classes
• Thus populating an ontology with instances is not
  done manually
• Retrieved from legacy data sources (DBs)
• Extracted automatically from a text corpus

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Ontology design process
66
Ontology editors

• Help with
• Initial conceptual modelling
• Use of Description Logic to represent classes,
  properties, and restrictions.
• Error detection and consistency checking while
  writing an ontology
• Several editors now available, we use Protégé 4

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Remember DL?
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Protégé 4
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Its not easy

• Even those domains that seem simple and
  uncomplicated require a careful analysis and
  their modelling requires careful consideration
• Common problems have been addressed by W3C SWBP
  cookbook style documents and the definition of
  ontology patterns
• Some useful hints follow

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Normalisation and UntanglingLet the reasoner do
multiple classification

• Tree
• Everything has just one parent
• A strict hierarchy
• Directed Acyclic Graph (DAG)
• Things can have multiple parents
• A Polyhierarchy
• Normalisation
• Separate primitives into disjoint trees
• Link the trees with definitions restrictions
• Fill in the values
• Let the classifier produce the DAG

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Tables are easier to manage than DAGs /
Polyhierarchies
and get the benefit of inferenceGrass and
Leafy_plants are both kinds of Plant
72
Remember to add any closure axioms
ClosureAxiom
Then let the reasoner do the work
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NormalisationFrom Trees to DAGs

• Before classification
• A tree

• After classification
• A DAG
• Directed Acyclic Graph

74
Common traps of restrictions

• Some does not imply onlyOnly does not imply
  some
• Trivial satisfaction of universal restrictions
• Domain and Range Constraints
• What to do when it all turns red
• Dont panic!

75
someValuesFrom means some

• someValuesFrom means some
• means at least 1
• Dog_owner complete Person and hasPet
  someValuesFrom Dog
• meansA Pet_owner is any person who has as a pet
  some (i.e. at least 1) dog
• Dog_owner partial Person and hasPet
  someValuesFrom Dog
• means All Pet_owners are people and have as a
  pet some (i.e. at least 1) dog.

76
allValuesFrom means only

• allValuesFrom means only
• means no values except
• First_class_lounge complete Lounge and
  hasOccupants allValuesFrom FirstClassPassengers
• Means A first class lounge is any lounge
  where the occupants are only first class
  passengers orA first class lounge is any
  lounge where there are no occupants except first
  class passengers

77
allValuesFrom means only

• First_class_lounge partial
• Lounge and hasOccupants allValuesFrom
  FirstClassPassengers
• MeansAll first class lounges have only
  occupants who are first class passengersAll
  first class lounges have no occupants except
  first class passengersAll first class lounges
  have no occupants who are not first class
  passengers

78
Some does not mean only

• A dog owner might also own cats, and turtles,
  and parrots, and
• It is an open world, if we want a closed world we
  must add a closure restriction or axiom
• Dog_only_owner complete Person and hasPet
  someValuesFrom Dog and
  hasPet allValuesFrom Dog
• A closure restriction or closure axiom
• The problem in making margherita pizza a veggie
  pizza
• Closure axioms use or (disjunction)
• dog_and_cat_only_owner complete hasPet
  someValuesFrom Dog and hasPet someValuesFrom
  Cat and hasPet allValuesFrom (Dog or Cat)

79
Only does not mean some

• There might be nobody in the first class lounge
• That would still satisfy the definition
• It would not violate the rules
• A pizza with no toppings satisfies the definition
  of a vegetarian pizza
• Pizza has_topping_ingredient allValuesFrom
  Vegetarian_topping
• It has no toppings which are meat
• It has not toppings which are not vegetables
• It has no toppings which arent fish

80
Only does not mean some

• Analogous to the empty set is a subset of all
  sets
• One reason for a surprising subsumption is that
  you have made it impossible for there to be any
  toppings
• allValuesFrom (cheese and tomato)

81
Trivial Satisfiability

• A universal (only) restriction with an
  unsatisfiable filler is trivially satisfiable
• i.e. it can be satisfied by the case where there
  is no filler
• If there is an existential or min-cardinality
  restriction, inferred or explicit, then the class
  will be unsatisfiable
• Can cause surprising late bugs

82
Domain Range Constraints

• Domain and range constraints are axioms too
• Property P range( RangeClass) means
• owlThing restriction(P allValuesFrom
  RangeClass)
• Property P domain( DomainClass )means
• owlThing restriction(inverse(P)
  allValuesFrom DomainClass)

83
What happens if violated

• Property eats range( LivingThing) means
• owlThing restriction(P allValuesFrom
  LivingThing)
• Bird eats some Rock
• All StoneEater eats some rocks
• What does this imply about rocks?
• Some rocks are living things
• because only living things can be eaten
• What does this say about all rocks?

84
Domain Range Constraints

• Property eats domain( LivingThing )means
• owlThing restriction(inverse(eats)
  allValuesFrom LivingThing)
• Only living things eat anything
• StoneEater eats some Stone
• All StoneEaters eat some Stone
• Therefore All StoneEaters are living things
• If StoneEaters are not already classified as
  living things, the classifier will reclassify
  (coerce) them
• If StoneEaters is disjoint from LivingThing it
  will be found disjoint

85
Example of Coercion by Domain violation

• has_topping domain(Pizza) range(Pizza_topping)cl
  ass Ice_cream_cone has_topping some Ice_cream
• If Ice_cream_cone and Pizza are not disjoint
• Ice_cream_cone is classified as a kind of Pizza
• but Ice_cream is not classified as a kind of
  Pizza_topping
• Have shown that
  all Ice_cream_cones are a kinds of Pizzas,but
  only that some
  Ice_cream is a kind of Pizza_topping
• Only domain constraints can cause
  reclassification

86
ReminderSubsumption means necessary implication

• B is a kind of A means All Bs
  are As
• Ice_cream_cone is a kind of Pizza means
  All ice_cream_cones are pizzas
• From Some Bs are As we can deduce very little
  of interest in DL terms
• some ice_creams are pizza_toppings says
  nothing about all ice creams

87
SummaryDomain Range ConstraintsNon-Obvious
Consequences

• Range constraint violations unsatisfiable or
  ignored
• If filler and RangeClass are disjoint
  unsatisfiable
• Otherwise nothing happens!
• Domain constraint violations unsatisfiable or
  coerced
• If subject and DomainClass are disjoint
  unsatisfiable
• Otherwise, subject reclassified (coerced) to
  kind of DomainClass!
• Furthermore cannot be fully checked before
  classification
• although tools can issue warnings.

88
What to do when Its all turned red
Dont Panic!

• Unsatisfiability propagates so trace it to its
  source
• Any class with an unsatisfiable filler in a
  someValuesFor (existential) restriction is
  unsatisfiable
• Any subclass of an unsatisfiable class is
  unsatisfiable
• Therefore errors propagate, trace them back to
  their source
• Only a few possible sources
• Violation of disjoint axioms
• Unsatisfiable expressions in some restrictions
• Confusion of and and or
• Violation of a universal (allValuesFrom)
  constraint(including range and domain
  constraints)
• Unsatisfiable domain or range constraints

89
Saying something about a restriction

• Not just
• that an animal is dangerous,
• but why
• And how dangerous
• And how to avoid
• But can say nothing about properties
• except special thing
• Super and subproperties
• Functional, transitive, symmetric

90
Re-representing properties as classes

• To say something about a property it must be
  re-represented as a class
• propertyhas_danger ? Class Risk
• plus property Thing has_quality Risk
• plus properties Risk has_reason
  has_risk_type
  has_avoidance_measure
• Sometimes called reification
• But reification is used differently in
  different communities

91
Re-representing the property has_danger asthe
class Risk
Animal
Dangerous
has_danger
92
Lions are dangerous

• All lions pose a deadly risk of physical attack
  that can be avoided by physical separation
• All lions have the quality risk that is
• of type some physical attack
• of seriousness some deadly
• has avoidance means some physical separation

93
Can add a second definition of Dangerous Animal

• A dangerous animal is any animal that has the
  quality Risk that is Deadly
• or
• Dangerous_animal
• Animalhas_quality some (Risk AND
  has_seriousness some Deadly )
• NB that paraphrases as AND

94
In the tool

• Dangerous_animal
• Animalhas_quality some (Risk AND
  has_seriousness some Deadly )

95
This says that

• Any animal that is Dangerous is also An
  animal that has the quality Risk with the
  seriousness Deadly

96
Anopheles Mosquitos now count as dangerous

• Because they have a deadly risk of carrying
  disease

97
Multiple definitions are dangerous

• Better to use one way or the other
• Otherwise keeping the two ways consistent is
  difficult
• but ontologies often evolve so that simple
  Properties are re-represented as Qualities
• Then throw away the simple property

98
Often have to re-analyse

• What do we mean by Dangerous
• How serious the danger?
• How probable the danger?
• Whether from individuals (Lions) or the presence
  or many (Mosquitos)?
• Moves to serious questions of ontology
• The information we really want to convey
• Often a sign that we have gone to far
• So we will stop

99
More PatternsN-ary relations
100
N-ary relations
from http//www.w3.org/TR/swbp-n-aryRelations/

• In OWL a property is a binary relation instances
  of properties link two individuals (or an
  individual and a value)
• However, sometimes the most intuitive way to
  represent certain concepts is to use relations to
  link an individual to more than just one
  individual or value. Such relations are called
  n-ary relations.
• Some issues
• If property instances can link only two
  individuals, how do we deal with cases where we
  need to describe the instances of relations ?
• If instances of properties can link only two
  individuals, how do we represent relations among
  more than two individuals? ("n-ary relations")
• Pattern 1
• If instances of properties can link only two
  individuals, how do we represent relations in
  which one of the participants is an ordered list
  of individuals rather than a single individual?
  Pattern 2

101
Examples

• Christine has breast tumor with high probability
• A relation initially thought to be binary, needs
  a further argument
• Steve has temperature, which is high, but falling
  
• Two binary properties turn out to always go
  together and should be represented as one n-ary
  relation
• John buys a "Lenny the Lion" book from
  books.example.com for 15 as a birthday gift
• From the beginning the relation is really amongst
  several things
• United Airlines flight 3177 visits the following
  airports LAX, DFW, and JFK
• One or more of the arguments is fundamentally a
  sequence rather than a single individual

Can you think of some more examples?
102
Pattern 1, N-ary relations

• Represent the relation as a class rather than a
  property
• Individual instances of such classes correspond
  to instances of the relation
• Additional properties provide binary links to
  each argument of the relation
• Basic idea create a new class and new properties
  to represent an n-ary relation then an instance
  of the relation linking the n individuals is then
  an instance of this class.
• The classes created in this way are often called
  "reified relations"

103
Pattern 1 case 1

• Additional attributes describing a relation
• In this case we need to represent an additional
  attribute that represents a relation instance
• Ex Christine has breast tumor with high
  probability
• The solution is to create an individual that
  represents the relation instance itself, with
  links from the subject of the relation to this
  instance, and with links from this instance to
  all participants that represent additional
  information about this instance

104
Pattern 1, Example 1
Example Christine has breast tumor with high
probability
The individual _Diagnosis_Relation_1here
represents a single object encapsulating both the
diagnosis (Breast_Tumor_Christine) and the
probability of the diagnosis (HIGH) - It
contains all the information held in the original
3 arguments who is being diagnosed, what the
diagnosis is, and what the probability is -
Blank nodes (rdfDescription element that does
not have an rdfabout attribute assigned to it)
in RDF are used to represent instances of a
relation. Class definitions
105
Pattern 1 case 2

• Different aspects of the same relation
• In this case we need to represent the relation
  between an individual, and an object that
  represents different aspects of a property
  (relation) about the individual
• Ex Steve has temperature which is high but
  falling
• This instance of a relation cannot be viewed as
  an instance of a binary relation with additional
  attributes attached to it.
• It is a relation instance relating the individual
  and the complex object representing different
  facts about the specific relation between the
  individual and the object.

106
Pattern 1, Example 2

• Example Steve has temperature, which is high,
  but falling
• This cannot be viewed as an instance of a binary
  relation with additional attributes attached to
  it, but rather it is a relation instance relating
  the individual Steve and the complex object
  representing different facts about his temp

• Such cases often come about in the course of
  evolution of an ontology when it is realized that
  two relations need to be collapsed.
• For example, initially, one might have had two
  properties (e.g. has_temperature_level and
  has_temperature_trend) both relating to people,
  and then it is realized that these properties
  really are inextricably intertwined because one
  needs to talk about "temperatures that are
  elevated but falling"

107
Pattern 1 case 3

• N-ary relation with no distinguished participant
• In some cases the n-ary relationship links
  individuals that play different roles in a
  structure without any single individual standing
  out as the owner or the relation
• Ex John buys a "Lenny the Lion" book from
  books.example.com for 15 as a birthday gift
• The solution is to create an individual that
  represents the relation instance with links to
  all participants

108
Pattern 1, Example 3

• Example John buys a "Lenny the Lion" book from
  books.example.com for 15 as a birthday gift
• The relation explicitly has more than one
  participant, and, in many contexts, none of them
  can be considered a primary one, thus an
  individual is created to represent the relation
  instance with links to all participants

109
Considerations in introducing a new class

• We did not give meaningful names to instances of
  properties or to the classes used to represent
  instances of n-ary relations, but merely label
  them.
• In most cases, these individuals do not stand on
  their own but merely function as auxiliaries to
  group together other objects. Hence a
  distinguishing name serves no purpose. Note that
  a similar approach is taken when reifying
  statements in RDF.
• Creating a class to represent an n-ary relation
  limits the use of many OWL constructs and creates
  a maintenance problem, especially when dealing
  with inverse relations.

110
Pattern 2

• Using lists for arguments in a relation
• Some n-ary relations do not naturally fall into
  either of the use cases above, but are more
  similar to a list or sequence of arguments.
• Example United Airlines flight 3177 visits the
  following airports LAX, DFW, and JFK
• The relation holds between the flight and the
  airports it visits, in the order of the arrival
  of the aircraft at each airport in turn.
• This relation might hold between many different
  numbers of arguments, and there is no natural way
  to break it up into a set of distinct properties
  relating the flight to each airport. The order of
  the arguments is highly meaningful.

111
Pattern 2, N-ary relations

• Example United Airlines flight 3177 visits the
  following airports LAX, DFW, and JFK
• Basic idea when all but one participant in a
  relation do not have a specific role and
  essentially form an ordered list, it is natural
  to connect these arguments into a sequence
  according to some relation, and to relate the one
  participant to this sequence (or the first
  element of the sequence)

• nextSegment is an ordering relation between
  instances of the FlightSegment class each flight
  segment has a property for the destination of
  that segment
• A special subclass of flight segment,
  FinalFlightSegment is added with a maximum
  cardinality of 0 on the nextSegment property, to
  indicate the end of the sequence.

112
Additional resources

• W3C Working Group Note -Defining N-ary Relations
  on the Semantic Web
• http//www.w3.org/TR/swbp-n-aryRelations
• W3C Semantic Web Best Practices and Deployment
  Working Group
• http//www.w3.org/2001/sw/BestPractices/
• General references on Semantic Web
• http//www.w3.org/2001/sw/
• many other resources/tutorials on the Web

113
More PatternsPart-whole relations
114
Part-whole relationsOne method NOT a SWBP draft

• How to represent part-whole relations in OWL is a
  commonly asked question
• SWBP has published a draft
• http//www.w3.org/2001/sw/BestPractices/OEP/Simple
  PartWhole
• This is one approach that will be proposed
• It has been used in teaching
• It has no official standing

115
Part Whole relations

• OWL has no special constructs
• But provides the building blocks
• Transitive relations
• Finger is_part_of Hand Hand is_part_of Arm
  Arm is_part_of Body
• ?
• Finger is_part_of Body

116
Implementation PatternTransitive properties with
non-transitive direct subproperties

• Transitive properties should have non-transitive
  children
• isPartOf transitive isPartOfDirectly
  non-transitive
• Split which is used in partial descriptions and
  complete definitions
• Necessary conditions use non-transitive version
• Definitions use transitive version
• Benefits
• Allows more restrictions in domain/range
  constraints and cardinality
• Allows the hierarchy along that axis to be traced
  one step at a time
• Allow a good approximation of pure trees
• Make the nontransitive subproperty functional
• Transitive properties can (almost) never be
  functional(by definition, a transitive property
  has more than one value in any non-trivial
  system)
• Constraints on transitive properties easily lead
  to unsatisfiability

117
Many kinds of part-whole relations

• Physical parts
• hand-arm
• Geographic regions
• Hiroshima - Japan
• Functional parts
• cpu computer
• See Winston Odell Artale Rosse

118
Simple version

• One property is_part_of
• transitive
• Finger is_part_of some HandHand is_part_of some
  ArmArm is_part_of some Body

119
Get a simple list

• Probe_part_of_body Domain_category
  is_part_of some Body

• Logically correct
• But may not be what we want to see

120
Injuries, Faults, Diseases, Etc.

• A hand is not a kind of a body
• but an injury to a hand is a kind of injury to
  a body
• A motor is not a kind of automobile
• but a fault in the motor is a kind of fault in
  the automobile
• And people often expect to see partonomy
  hierarchies

121
Using part-whole relations Defining injuries or
faults

• Injury_to_Hand Injury has_locus some
  Hand_or_part_of_hand
• Injury_to_Arm Injury has_locus some
  Arm_or_part_of_Arm
• Injury_to_Body Injury has_locus some
  Body_or_part_of_Body

• The expectedhierarchy frompoint of view
  ofanatomy

122
Parts wholesSome examples

• The leg is part of the chair
• The left side of the body is part of the body
• The liver cells are part of the liver
• The ignition of part of the electrical system of
  the car
• The goose is part of the flock
• Liverpool r is part of England
• Computer science is part of the University

123
Five families of relations

• Partonomic
• Parts and wholes
• The lid is part of the box
• Constitution
• The box is made of cardboard
• Membership?
• The box is part of the shipment
• Nonpartonomic
• Containment
• The gift is contained in the box
• Connection/branching/Adjacency
• The box is connected to the container by a strap

124
Some tests

• True kinds of part-of are transitive
• A fault to the part is a fault in the whole
• The finger nail is part of the finger is part of
  the hand is part of the upper extremity is part
  of the body
• Injury to the fingernail is injury to the body
• The tail-light is part of the electrical system
  is part of the car
• A fault in the tail light is a fault in the car
• Membership is not transitive
• The foot of the bird is part of the bird but not
  part of the flock of birds
• Damage to the foot of the bird is not damage to
  the flock of birds

125
Some tests

• Containment is transitive but things contained
  are not necessarily parts
• A fault (e.g. souring) to the milk contained in
  the bottle is not damage to the bottle
• Some kinds of part-whole relation are
  questionably transitive
• Is the cell that is part of the finger a part of
  the body?
• Is damage to the cell that is part of the finger
  damage to the body?
• Not necessarily, since the cells in my body die
  and re-grow constantly

126
Structural parts

• The leg is a component of of the table
• Discrete
• connected,
• clear boundary,
• specifically named
• may be differently constituted
• Can have metal legs on a wooden table or vice
  versa
• The left side is a subdivision of the table
• Side, Lobe, segment, region,
• Arbitrary, similarly constituted,
• components typically fall into one or another
  subdivision
• defined in relation to something else
• sensible to talk about what fraction it is half
  the table, a third of the table, etc.

127
Propagates_via / transitive_across

• Components of subdivisions are components of the
  whole, butsubdivisions of components are not
  subdivisions of the whole
• A the left side of the steering wheel of the car
  is not a subdivision of the left side of the car
  (at least not in the UK)
• No consistent name for this relation between
  properties
• We shall call it propagates_via or
  transitive_across
• Also known as right identities
• Not supported in most DLs or OWL directly
• Although an extension to FaCT to support it
  exists
• Heavily used in medical ontologies (GRAIL and
  SNOMED-CT)

128
No simple solutionHeres one of several nasty
kluges

• Component_of_table is defined as a component of
  table or any subdivision of table
• Must do it for each concept
• A Schema rather than an axiom
• No way to say same as
• No variables in OWL
• or most DLs
• SCHEMAComponents_of_X isComponentOf
  someValuesFrom (X or
  (someValuesfrom isSubDivisionOf X))
• Tedious to do
• Schemas to be built into new tools

129
Functional parts

• Structural parts form a contiguous whole
• May or may not contribute to function
• e.g. decorative parts, accidental lumps and bumps
• The remote control is part of the projection
  system
• May or may not be physically connected to it
• Part of a common function
• Biology examples
• The endocrine system
• The glands are not connected, but form part of a
  functioning system communicating via hormones and
  transmitters
• The blood-forming system
• Bone marrow in various places, the spleen, etc.

130
If something is both a structural and functional
part

• Must put in both restrictions explicitly
• Can create a common child property but this gets
  complicated with the different kinds of
  structural parts