An Overview of Ontologies and their Practical Applications

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An Overview of Ontologies and their Practical
Applications

• Gianluca Correndo
• correndo_at_di.unito.it
• http//www.di.unito.it/correndo

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What is an Ontology?
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Ontology

• Semantics the meaning of meaning.
• Philosophical discipline, branch of philosophy
  that deals with the nature and the organisation
  of reality.

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In Computer Science

• An ontology is an explicit specification of a
  conceptualization Gruber
• Defines
• A common vocabulary of terms
• Some specification of the meaning of the terms
• A shared understanding for people and machines

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Why develop an ontology?

• To make domain assumptions explicit
• Easier to change domain assumptions
• Easier to understand and update legacy data
• To separate domain knowledge from operational
  knowledge
• Re-use domain and operational knowledge
  separately
• A community reference for applications
  (standards)
• To share a consistent understanding of what
  information means

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Communication
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A Specification of a Conceptualization

• Concepts (class, set, type, predicate)
• Event, gene,molecule, cat
• Properties of concepts and relationships between
  them (slot)
• Taxonomy generalisation ordering among concepts
  isA, partOf, subProcess
• Relationship, role or attribute functionOf,
  hasActivity location, eats, size

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What is a concept?

• Different communities have different notions on
  what a concept means
• Formal concept analysis talk about formal
  concepts
• Description Logics talk about concept labels
• ISO-7042000 Terminology Work
• Often the classical notion of a frame in AI or a
  class in OO modeling is seen as equivalent to a
  concept.

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An explicit description of a domain

• Constraints or axioms on properties and concepts
  
• value integer
• domain cat
• cardinality at most 1
• range 0 lt X lt 100
• oligonucleotides lt 20 base pairs
• cows are larger than dogs
• cats cannot eat only vegetation
• cats and dogs are disjoint
• Values or concrete domains
• integer, strings
• 20, tryptophan

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An explicit description of a domain

• Individuals or Instances
• sulphur, trpA Gene, felix
• Nominals
• Concepts that cannot have instances
• Instances that are used in conceptual definitions
• ItalianDog Dog bornIn Italy
• Instances
• An ontology concepts properties axioms
  values nominals
• A knowledge base ontologyinstances

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Light and Heavy expressivity
A matter of rigour and representational
expressivity

• Lightweight
• Concepts, atomic types
• Is-a hierarchy
• Relationships between concepts

• Heavyweight
• Metaclasses
• Type constraints on relations
• Cardinality constraints
• Taxonomy of relations
• Reified statements
• Axioms
• Semantic entailments
• Expressiveness
• Inference systems

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Carl von Linné (1707-1778)
Aristotele (384 b.C. 322 b.C. )
Regno Animalia Tipo Chordata   Classe
Mammalia   Ordine Primates  
Famiglia Hominidae  
Genere Homo   Specie sapiens

• Science of Being (Metaphysics, IV,1)
• What is being?
• What are the features common to all beings?

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So what is an ontology?
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Things in Common

• They are approaches to help structure, classify,
  model, and/or represent the concepts and
  relationships pertaining to some subject matter
  of interest to some community.
• They are intended to enable a community to come
  to agreement and to commit to use the same terms
  in the same way.
• The meaning of the terms is specified in some way
  and to some degree.

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Thesauri
similarTo
Vegetable
Fruit
Example
NarrowerTerm
Orange
Apfelsine (german)
synonymWith

• Graph with labels edges (similar, nt, bt,
  synonym)
• Fixed set of edge labels (aka relations)
• Use of lexical stem
• no instances
• Well known in library science
• cf. terminologies / classifications (Dewey)

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WordNet
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UMLS (Unified Medical Language System)
http//umlsks.nlm.nih.gov/

• National Library of Medicine (NLM) database of
  medical terminology. Terms from several medical
  databases (MEDLINE, SNOMED International, MeSH,
  etc.) are unified so that different terms are
  identified as the same medical concept.
• Metathesaurus provides the concordance of medical
  concepts 730.000 concepts, 1.5 million concept
  names in different source vocabularies
• Specialist Lexicon provides word synonyms,
  derivations, lexical variants, and grammatical
  forms of words used in MetaThesaurus terms
  130.000 entries.
• Semantic Network codifies the relationships (e.g.
  causality, "is a", etc.) among medical terms 134
  semantic types, 54 relationships.
• Used for patient data creation, curriculum
  analysis, natural language processing, and
  information retrieval

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UMLS Metathesaurus
Information System
DB
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UMLS Metathesaurus
Information System 1
Information System 2
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Formal Ontologies
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Frames, SDM, OO models

• Frames
• Rich set of language constructs frames, slots,
  facets, defaults
• Impose restrictive constraints on how they are
  combined or used to define a class
• All frames asserted into taxonomy by hand
• All concepts are primitive
• Octet/GKB, Protégé, OCML, Ontolingua
• OKBC Open Knowledge Base Connectivity
• OKBC Lite
• OO / Semantic Data Models (EER, UML)
• Taxonomy/inheritance semantics
• Intuitive, lots of tools, widely used

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Frame Data Model

• Frames
• Classes genes, reactions
• Instances lr10
• Relationships
• Slots chromosome, map-position, citations,
  reactants, products, Keq
• Facets chromosome is single-valued, instance of
  class chromosomes Citations is multiple valued,
  set of strings

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Description Logics

• A family of logic based knowledge representation
  formalisms
• Descendants of semantic networks and KL-ONE
• Describe domain in terms of concepts (set of
  individuals), roles (relationships) and
  individuals
• Distinguished by
• Formal semantics (typically model theoretic)
• Decidable fragments of FOL
• Closely related to propositional modal dynamic
  logics
• Provision of inference services
• Sound and complete decision procedures for key
  problems
• Implemented systems (highly optimised)

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Description Logic Family

• DLs are a family of logic based KR formalisms
• Particular languages mainly characterised by
• Set of constructors for building complex concepts
  and roles from simpler ones
• Set of axioms for asserting facts about concepts,
  roles and individuals
• ALC is the smallest DL that is propositionally
  closed
• Constructors include booleans (and, or, not), and
• Restrictions on role successors
• E.G., Concept describing happy fathers could be
  written
• Man ?? ?hasChild.Female ?? ?hasChild.Male
• ?? ?hasChild.(Rich ?? happy)

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DL Concept and Role Constructors

• Range of other constructors found in DLs,
  including
• Number restrictions (cardinality constraints) on
  roles, e.g., ?3 hasChild, ?1 hasMother
• Qualified number restrictions, e.g., ?2
  hasChild.Female, ?1 hasParent.Male
• Nominals (singleton concepts), e.g., Italy
• Concrete domains (datatypes), e.g., hasAge.(?21),
  earns spends.lt
• Inverse roles, e.g., hasChild (hasParent)
• Transitive roles, e.g., hasChild (descendant)
• Role composition, e.g., hasParent o hasBrother
  (uncle)

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Whats in a Logic based ontology?

• Primitive concepts - in a hierarchy
• Described but not defined
• Properties - relations between concepts, also in
  a hierarchy
• Constructors on concepts and properties
• Some, only, at least, at most, and, or,
  not
• Defined concepts
• Made from primitive concepts, constructors and
  descriptors
• Enzyme ? protein and catalyses reaction
• Reason that enzyme is a kind of protein

• Is-kind-of implies
• Dog is a kind of wolf mean all dogs are
  wolves
• Axioms
• disjointness, further description of defined
  concepts
• A Reasoner
• To organise it for you. Consistency taxonomy
  for defined concepts established though logical
  reasoning

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Reasoning support in DL

• Consistency check if knowledge is meaningful
• Subsumption structure knowledge, compute
  taxonomy
• Equivalence check if two classes denote same
  set of instances
• Instantiation check if individual i instance of
  class C
• Retrieval retrieve set of individuals that
  instantiate C
• Problems all reducible to consistency
  (satisfiability) FACT, racer, cerebra

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Pratical Session
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Pratical Session
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Formal Ontology Applications
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Formal Ontology Applications

• Ontology engineering support
• Semantic web
• Intelligent information retrieval
• E-Commerce
• Intelligent web-services
• Agent technologies

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Problems with Information Retrieval

• Working with the Web is currently done at a very
  low level
• Clicking on links and using keyword search for
  links is the main (if not only) navigation
  technique
• Keyword-based search engines
• (Alta Vista, Infoseek, Yahoo, MetaCrawler,
  Google)

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Problems with Information Retrieval

• Main burden of information retrieval is that it
  is only information retrieval.
• It helps to retrieve information sources but the
  human user has to manually extract and interpret
  the information.
• Information presentation and maintenance is not
  supported.

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Semantic Web Vision

• Express explicitly a high level description of
  resources accessible via Web
• More processable data availabe
• Information more directly available
• Enabling intelligent Web features

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DAML-S Ontology language

• Build upon the well-defined semantics of DAMLOIL
• Is expected to provide a common understanding of
  the semantic in a web-service
• By specifing an Upper Ontology for Services

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An Upper Ontology for Services

• Three essential types of knowledge about a
  service, each characterized by the question it
  answers
• What does the service require of the user(s),and
  provide for them?
• How does it work?
• How is it used?

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Backup Slides
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Ontology for data interoperability

• Ontology-based Information Integration (TAMBIS)
• Spread a query over different and heterogeneous
  data sources
• Quite used in gene ontology applications but not
  only

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Thesauri Classification

• UNSPSC United Nations Standard Products and
  Services Code
• Provides structrue and a unique identification of
  terms
• Thesauri act as a good starting point for
  developing an ontology