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The Structure of Ontologies


I grew up in Rhode Island, a New England state which is largely Italian ... BUT: Hyena's printer and job management functions include. 7/24/09. Martin Volk. 25 ... – PowerPoint PPT presentation

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Title: The Structure of Ontologies

The Structure of Ontologies
  • Martin Volk
  • Stockholm University

  • What Are You?
  • I grew up in Rhode Island, a New England state
    which is largely Italian-American and
    French-Canadian, known chiefly for its small
    stature. When I was a kid in our neighborhood,
    the first thing you would ask on encountering a
    newcomer was "whats your name?" The second was
    "What are you" as an invitation to recite your
    ethnic composition in a kind of singsong voice.
  • 90 of the kids would say "Italian with a little
    bit of French," or "half-Portuguese, one-quarter
    Italian and one-quarter Armenian." When I would
    chime in with "half Jewish, one quarter Scottish
    and one quarter English," the range of responses
    went from very puzzled looks to "does that mean
    youre not Catholic?" Wherein, I guess, began my
    fascination with classification, and especially
    with the problem of residual categories, or, the
    Other, or not elsewhere classified.
  • --Leigh Star
  • (quoted from http//

Overview of todays lecture
  • What is an Ontology?
  • How are ontologies structured? A closer look
  • Why do Computational Linguists work with / on
  • Some terminology with respect to the Semantic
  • Your next task!

Remarks about your ontologies
  • Protégé is a complex system!
  • not suited for synonyms nor for translations.
  • Options
  • Use of the documentation field
  • For translations build a parallel hierarchy
  • Trade-off between
  • Stockholm-specific ontology
  • General university ontology
  • Test for classes
  • Is the mother node a good answer to What is a X?

Remarks about SUIS
  • SUIS will use your University ontology for query
  • If your query is Who is Päivi Juvonen? then
    SUIS will search for Päivi Juvonen in
    combination with any person function (teacher,
    librarian, cook, rector, director of studies, )
  • If your query is Where is the lecture on
    Semiotics? then SUIS will search for semiotics,
    some teaching event and a date.

Remarks about SUIS
  • SUIS will use your ontology for answering
    questions directly.
  • If your query is What is a lecturer? then SUIS
    will use the ontology for replying
  • A lecturer is a type of teacher working at a
  • A lecturer is called universitetslektor or
    högskoleadjunkt in Swedish.

What is an ontology?
  • An ontology holds information about what
    categories exist in the domain, what properties
    they have, and how they are related to one
    another. (Chandrasekaran et al. 1999)

Types of ontologies
  • Top-level ontology
  • Lexical ontology (e.g. WordNet)
  • WordNet is a lexical database for English (with
    synonyms and hyperonyms)
  • General ontology (e.g. Cyc)
  • Cyc is a formalized representation of fundamental
    human knowledge facts, rules of thumb, and
    heuristics for reasoning about the objects and
    events of everyday life
  • Domain ontology (e.g. UMLS)
  • The Unified Medical Language System
  • Task ontology (e.g. CPV)
  • The Common Procurement Vocabulary

Kinds of Ontologies
  • Ontologies may vary not only in their content,
    but also in their structure and implementation.
  • Level of description
  • An ontology may mean different things
  • from simple lexicons or controlled vocabularies,
  • categorically organized thesauri, to
  • taxonomies where terms are related hierarchically
    and can be given distinguishing properties, to
  • full-blown ontologies where these properties can
    define new concepts and where concepts have named
    relationships with other concepts, like "changes
    the effect of" or "buys from".

Kinds of Ontologies
  • Conceptual scope
  • Ontologies differ in respect to the scope and
    purpose of their content. The most prominent
    distinction is between
  • the domain ontologies describing specific fields
    of endeavor, like medicine, and
  • upper level ontologies describing the basic
    concepts and relationships invoked when
    information about any domain is expressed in
    natural language.
  • The synergy among ontologies springs from the
    cross-referencing between upper level ontologies
    and various domain ontologies.

Kinds of Ontologies
  • Specification language
  • To build ontologies a number of possible
    languages can be used, including
  • general logic programming languages like Prolog.
  • More common are languages that have evolved
    specifically for ontology construction. The Open
    Knowledge Base Connectivity (OKBC) model and
    languages like KIF (and its successor CL --
    Common Logic) have become the bases of other
    ontology languages.
  • There are also several languages based on a form
    of logic known as description logics. These
    include Loom and DAMLOIL, which is currently
    being evolved into the Web Ontology Language
    (OWL) standard.
  • When comparing ontology languages, language
    expressiveness is usually given up for
    computability and simplicity.

Building Ontologies
  • Acquire domain knowledge
  • Assemble appropriate information resources and
    expertise that will define, with consensus and
    consistency, the terms used formally to describe
    things in the domain of interest.
  • Organize the ontology
  • Design the overall conceptual structure of the
    domain. This involves identifying the domain's
    principal concrete concepts and their properties,
    identifying the relationships among the concepts,
    creating abstract concepts as organizing
    features, referencing or including supporting
    ontologies, distinguishing which concepts have
    instances, and applying other guidelines of your
    chosen methodology.
  • Flesh out the ontology
  • Add concepts, relations, and individuals to the
    level of detail necessary to satisfy the purposes
    of the ontology.

Ontology (Noy  McGuinness)
  • An ontology defines a common vocabulary for
    researchers who need to share information in a
    domain. It includes machine-interpretable
    definitions of basic concepts in the domain and
    relations among them.

Why ontologies? (Noy  McGuinness)
  • Why would someone want to develop an ontology?
    Some of the reasons are
  • To share common understanding of the structure of
    information among people or software agents
  • To enable reuse of domain knowledge
  • To make domain assumptions explicit
  • To separate domain knowledge from the operational
  • To analyze domain knowledge

(No Transcript)
WordNet relations
Other relations (Navigli Velardi. 2004.
CL-Journal, p.168)
Ontologies(acc. to John Sowa. 2000. Knowledge
  • (p.51) Logic has no vocabulary for describing
    the things that exist. Ontology fills that gap
    It is the study of existence, of all the kinds of
    entities abstract or concrete that make up
    the world.
  • (p. 52) AI systems start with limited ontologies
    (microworlds), a small number of concepts that
    are tailored for a single application.
  • Example Chat-80 geographical categories

  • Sowa (p. 68) All perception begins with
    contrasts light-dark, up-down, hard-soft,
    loud-quiet, sweet-sour. Such contrasts are the
    source of distinctions for generating the
    categories of existence.

Examples of Ontologies
  • The categories in the Yellow Pages (telephone
  • The Yahoo categories.
  • Library cataloging system.
  • Good storing in a department store.
  • The faculties at a university.
  • The departments in a municipal or national
    administration or in a large enterprise.

Natural Language Processing (NLP) and Ontologies
  • Building ontologies
  • extracting terms from corpora as concept
  • suggesting synonyms from context analysis.
  • suggesting hyperonyms from compound analysis
    (e.g. cruciate ligament is a ligament).
  • extending ontologies.
  • merging ontologies.
  • of two companies / two administrative units.

NLP and Ontologies
  • Applying ontologies
  • mapping concepts found in text to nodes in an
  • e.g. Riesling ? white wine
  • disambiguating word senses
  • e.g. SV lag ? EN law or team
  • reasoning with the help of ontologies
  • e.g. If Riesling is a white wine, then it is an
    alcoholic beverage.
  • allowing for cross-language searches
  • e.g. head of department ? prefekt

Automatic ontology construction
  • Find is_a patterns in a corpus
  • A computer printer is a computer peripheral
  • the HP OfficeJet 4215 is a multitalented,
    multifunction printer
  • Use compounds
  • laser printer ? printer

Automatic ontology construction
  • Use appositive NPs
  • Your Basic Laser Printer the Brother 1240
  • lets you choose your active printer (the printer
    you are about to use)
  • Canon launches A4 Photo Printer - The Bubble Jet
  • Use coordination
  • The PhotoSmart printer and scanner are 399 each
  • Printer and Photocopier Troubleshooting
  • BUT Hyena's printer and job management functions

Merging Ontologies
  • (Sowa) Different systems may use different names
    for the same kinds of entities
  • even worse, they may use the same names for
    different kinds.

Merging Ontologies
  • Before two ontologies can be merged it might be
    necessary to introduce new classes to accommodate
    the different structures.
  • Often ontologies get aligned rather than merged.

The Semantic Web
  • a vision by Tim Berners-Lee
  • to facilitate the use of web pages by computers
  • Ideally specification of content in a formal
  • Example
  • father(carl_gustav, victoria).
  • father(gustav_adolf, carl_gustav).
  • grandfather(A,C) -
  • father(A,B),
  • father(B,C).

What is XML?
  • XML eXtended Markup Language
  • a language for structuring text and data
  • the XML structure of a document can be checked
    against a grammar (DTD document type definition)

URI Uniform Resource Identifier
provide names
RDF Resource Description Framework
Dublin Core a set of core elements
simple statements
OWL Web Ontology Language
complex logical statements
Uniform Resource Identifier (URI)
  • If you want to discuss something, you must first
    identify it.
  • Example Absolute URIs (from http//en.wikipedia.or
    g )
  • http//somehost/absolute/URI/with/absolute/path/to
  • ftp//somehost/resource.txt
  • urna-rose-by-any-other-name (hmm... unregistered
    URN namespace)

Uniform Resource Identifier (URI)
  • A URL (Uniform Resource Locator web address) is
    one type of a URI.
  • Everybody can create a URI.
  • A URI is not a set of directions telling your
    computer how to get to a specific file on the
  • A URI is a name for a "resource" (a thing). This
    resource may or may not be accessible over the

RDF (Resource Description Framework)
  • RDF is a language for representing information
    about resources in the World Wide Web,
  • e.g. representing metadata about Web resources,
    such as the title, author, and modification date
    of a Web page.
  • An RDF statement is a lot like a simple sentence,
    except that almost all the words are URIs.
  • Each RDF statement has three parts a subject, a
    predicate and an object.

RDF example
  • Example a Wikipedia page about Tony Benn
  • To say that the title of a page is "Tony Benn"
    and its publisher is "Wikipedia" in RDF
  • lthttp//
    lthttp// "Tony
    Benn" .
  • lthttp//
    "Wikipedia" .
  • and in RDF/XML
  • ltrdfRDF
  • xmlnsrdfhttp//
  • xmlnsdc"http//"gt
  • ltrdfDescription rdfabout"http//en.wikipedia.o
  • ltdctitlegtTony Bennlt/dctitlegt
  • ltdcpublishergtWikipedialt/dcpublishergt
  • lt/rdfDescriptiongt
  • lt/rdfRDFgt

URI vs Ontologies
  • The use of all these URIs is useless if we never
    describe what they mean. This is where schemas
    and ontologies come in. A schema and an ontology
    are ways to describe the meaning and
    relationships of terms.

Richer schema capabilities
  • RDF Schema provides basic capabilities. Other
    richer schema capabilities that have been
    identified as useful
  • cardinality constraints on properties,
  • e.g., that a Person has exactly one biological
  • specifying that a given property (such as
    exhasAncestor) is transitive,
  • e.g., that if A exhasAncestor B, and B
    exhasAncestor C, then A exhasAncestor C.
  • specifying that a given property is a unique
    identifier (or key) for instances of a particular
  • specifying constraints on the range or
    cardinality of a property that depend on the
    class of resource to which a property is applied,
  • e.g., for a soccer team the exhasPlayers
    property has 11 values, while for a basketball
    team the same property should have only 5 values.
  • the ability to describe new classes in terms of
    combinations (e.g., unions and intersections) of
    other classes, or to say that two classes are

OWL (Web Ontology Language)
  • is a powerful ontology language.
  • is based on RDF and RDF Schema.
  • The intent of OWL is to provide additional
    machine-processable semantics for resources.
  • is a semantic markup language for publishing and
    sharing ontologies on the Web.
  • can be used to explicitly represent the meaning
    of terms in vocabularies and the relationships
    between those terms. ? i.e. ontologies

  • OWL Lite supports those users needing a
    classification hierarchy and simple constraints.
  • For example, it only permits cardinality values
    of 0 or 1. ? simple to provide tool support for
    OWL Lite, and ? quick migration path for thesauri
    and other taxonomies.
  • OWL DL (description logics) supports maximum
    expressiveness while retaining computational
    completeness (all conclusions are guaranteed to
    be computable) and decidability (all computations
    will finish in finite time). OWL DL includes all
    OWL language constructs, but with certain
  • For example, while a class may be a subclass of
    many classes, a class cannot be an instance of
    another class.
  • OWL Full gives maximum expressiveness and the
    syntactic freedom of RDF with no computational
  • For example, in OWL Full a class can be treated
    simultaneously as a collection of individuals and
    as an individual in its own right.

The Dublin Core
  • The Dublin Core is a set of "elements"
    (properties) for describing documents (and hence,
    for recording metadata).
  • The set was originally developed in 1995 at the
    Metadata Workshop in Dublin, Ohio.
  • The goal is to provide a minimal set of
    descriptive elements that facilitate the
    description and the automated indexing of
    documents, similar to a library card catalog.
  • is meant to be sufficiently simple to be
    understood and used by a wide range of authors.

The Dublin Core
  • Title A name given to the resource.
  • Creator An entity primarily responsible for
    making the content of the resource.
  • Subject The topic of the content of the
  • Description An account of the content of the
  • Publisher An entity responsible for making the
    resource available
  • Contributor An entity responsible for making
    contributions to the content of the resource.

The Dublin Core
  • Date A date associated with an event in the life
    cycle of the resource.
  • Type The nature or genre of the content of the
  • Format The physical or digital manifestation of
    the resource.
  • Identifier An unambiguous reference to the
    resource within a given context.
  • Source A reference to a resource from which the
    present resource is derived.
  • Language A language of the intellectual content
    of the resource.
  • Relation A reference to a related resource.
  • Coverage The extent or scope of the content of
    the resource.
  • Rights Information about rights held in and over
    the resource.

The Dublin Core
  • Information using the Dublin Core elements may be
    represented in any suitable language (e.g., in
    HTML meta elements).
  • However, RDF is an ideal representation for
    Dublin Core information.

URI Uniform Resource Identifier
provide names
RDF Resource Description Framework
Dublin Core a set of core elements
simple statements
OWL Web Ontology Language
complex logical statements
  • There are many uses for Natural Language
    Processing with respect to ontologies.
  • Ontologies play an important role in the vision
    of the Semantic Web.