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An Introduction to Ontologies

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Title: An Introduction to Ontologies


1
An Introduction to Ontologies
  • Tim Finin
  • University of Maryland Baltimore County

2
What is an ontology
  • The subject of ontologyis the study of
    thecategories of things thatexist or mayexist
    in some domain.
  • The word ontology isfrom the Greek ontos
    forbeing and logos for word.
  • Aristotle offered an ontology which included 10
    categories, shown as the leaves in this tree
    (from Sowa, after Brentano)

3
Tree ofPorphyry
  • The oldest knowntree diagram is the3rd century
    AD work by Greek philosopherPorphyry in
    commentary on Aristotle.
  • Substance was identified as the supreme genus or
    the most general supertype.

4
Top down vs. bottom up
  • Philosophers build fromthe top down and
    areinterested in capturingthe most
    generalconcepts.
  • Programmers tend towork from the bottomup,
    supporting a set ofapplications, with a little
    generality to help reuse and future development.
  • Ex CHAT-80 system (Periera and Warren, 1982)
    which answered NL questions about a geographic
    database.
  • Example of a microworld ontology supported NLP,
    query answering, and generation

5
Blocks world
6
Blocks world
  • The blocks world is another microworld used
    often for NLP, vision, planning.
  • It consists of a table, a set of blocks or
    different shapes, sizes and colors and a robot
    hand.
  • Some typical domain constraints
  • Only one block can be on another block.
  • Any number of blocks can be on the table.
  • The hand can only hold one block.
  • Typical representation
  • ontable(a) ontable(c)
  • on(b,a) handempty
  • clear(b clear(c)

7
Trees, Lattices, and Other Hierarchies
  • Most systems for expressing ontologies make heavy
    use of familiar representation schemes, including
    trees, lattices, acyclic graphs and general
    graphs
  • A lattice has a TOP (everthing) and BOTTOM
    (nothing)

8
Ontologies in Computer Science
  • Ontology A common vocabulary and agreed upon
    meanings to describe a subject domain.

Ontol"ogy (?), n. Gr. the things which exist
(pl.neut. of , , being, p.pr. of to be) -logy
cf.F. ontologie. That department of the science
of metaphysics which investigates and explains
the nature and essential properties and relations
of all beings, as such, or the principles and
causes of being. Webster's Revised Unabridged
Dictionary (G C. Merriam Co., 1913, edited by
Noah Porter)
  • This is not a profoundly new idea
  • Vocabulary specification
  • Domain theory
  • Conceptual schema (for a data base)
  • Class-subclass taxonomy
  • Object schema

9
Importance of ontologies in communication
  • An example of the importance of ontologies in
    communication is the fate of NASAs Mars Climate
    Orbiter
  • It crashed into Mars on September 23, 1999
  • JPL used metric units in their program
    controlling the thrusters and Lockheed-Martin
    used imperial units.
  • Instead of establishing an orbit at an altitude
    of 140km, it did so at 60km, causing it to burn
    up in the Martian atmosphere.

10
Conceptual Schemas
  • A conceptual schema specifies the intended
    meaning of concepts used in a data base

Data Base
Table price stockNo integer cost float
Data Base Schema
Auto Product Ontology
price(x, y) gt ? (x, y) auto_part(x)
part_no(x) x
retail_price(x, y, Value-Inc)
magnitude(y, US_dollars) y
Product Ontology
Conceptual Schema
Units Measures Ontology
11
Implicit vs. Explicit Ontologies
  • Systems which communicate and work together must
    share an ontology.
  • The shared ontology can be implicit or explicit.
  • Implicit ontology are typically represented only
    by procedures
  • Explicit ontologies are (ideally) given a
    declarative representation in a well defined
    knowledge representation language.

12
Conceptualizations, Vocabularies and
Axiomitization
  • Three important aspects to explicit ontologies
  • Conceptualization involves the underlying model
    of the domain in terms of objects, attributes and
    relations.
  • Vocabulary involves assigning symbols or terms to
    refer to those objects, attributes and relations.
  • Axiomitization involves encoding rules and
    constraints which capture significant aspects of
    the domain model.
  • Two ontologies may
  • be based on different conceptualizations
  • be based on the same conceptualization but use
    different vocabularies
  • differ in how much they attempt to axiomitize the
    ontologies

13
Simple examples
fruit
tropical
temperate
14
Ontologies vs. KBs
  • Ontologies are distinguished from KBs not by
    their form, but by the role they play in
    representing knowledge
  • Consensus models for a domain
  • Emphasis on properties that hold in all
    situations
  • Emphasis on classes rather than instances
  • Intended to support multiple tasks and methods
  • Dont change during problem solving and are
    suited for compiling into tools
  • Need to satisfy a community of use
  • Emphasis on collaborative development
  • Emphasis on translation to multiple logical
    formalisms
  • Useful for education

15
Ontology Library and Editing Tools
  • Ontolingua is a language for building,
    publishing, and sharing ontologies.
  • A web-based interface to a browser/editor server
    at http//ontolingua.stanford.edu/ and mirror
    sites.
  • Ontologies can betranslated into a number of
    content languages, including KIF, LOOM, Prolog,
    CLIPS, etc.
  • Chimera is a tool for merging existing ontologies

16
Big Ontologies
  • There are several large, general ontologies that
    are freely available.
  • Some examples are
  • Cyc - Original general purpose ontology
  • WordNet - a large, on-line lexical reference
    system
  • World Fact Book -- 5Meg of KIF sentences!
  • UMLS - NLMs Unified Medical Language System
  • See http//www.cs.utexas.edu/users/mfkb/related.ht
    ml for more

17
WordNet
  • WordNet is an on-line lexical referencesystem
    whose design is inspired bypsycholinguistic
    theories of human lexicalmemory.
  • English nouns, verbs, adjectives and adverbs are
    organized into synonym sets, each representing
    one underlying lexical concept.
  • Synsets board,plankboard,committee
  • Different relations link the synonym sets (e.g.
    antonyms, generalizations, etc)
  • 140K words
  • Developed by the Cognitive Science Laboratory at
    Princeton and available online
  • Although linguistically motivated, many groups
    have used it as a general ontology of concepts.
  • http//www.cogsci.princeton.edu/wn/

18
EDR Electronic Dictionary
  • http//www.iijnet.or.jp/edr/
  • a dictionary with over 400,000 concepts, with
    their mappings to both English and Japanese
    words.

19
Cyc
  • CYC is a large KB which has beenunder continual
    development sinceabout 1985.
  • The CYC KB is a formalized representation a vast
    quantity of fundamental human knowledge facts,
    rules of thumb, and heuristics for reasoning
    about the objects and events of everyday life.
  • CYC is encoded in the KR language CYCL
  • The Upper CYC Ontology contains approximately
    3,000 terms capturing the most general concepts
    of human consensus reality.
  • http//www.cyc.com/cyc-2-1/cover.html

20
Cycs top level concepts
21
openCyc
  • http//www.opencyc.org/
  • OpenCyc 1.0 (summer 2002?) will include the
    following.
  • 6,000 concepts an upper ontology for all of
    human consensus reality.
  • 60,000 assertions about the 6,000 concepts,
    interrelating them, constraining them, in effect
    (partially) defining them.
  • A compiled version of the Cyc Inference Engine
    and the Cyc Knowledge Base Browser.
  • A specification of CycL, the language in which
    Cyc (and hence OpenCyc) is written. There are
    CycL-to-Lisp,CycL-to-C, etc. translators.
  • A specification of the Cyc API
  • A few sample programs that demonstrate use of the
    Cyc API for application development.

22
IEEE Standard Upper Ontology
  • An IEEE standards working group
  • This standard will specify an upperontology
    that will enable computers to utilize it for
    applications such as data interoperability,
    information search and retrieval, automated
    inferencing, and natural language processing.
  • http//suo.ieee.org/
  • See site for documents and archives of mailing
    list discussions
  • Two starter documents for SUOs SUMO, IFF

23
World Fact Book
  • Stanfords WFB aims to semi-automatically
    construct a substantial KB of basic geographic,
    economic, political, and demographic knowledge
    about the world's nations.
  • Source CIA World Fact Book
  • 5.2 MB 5K classes 64K facts and rules encoded
    in KIF
  • Available from http//www-ksl-svc.stanford.edu591
    5/doc/wfb/ in several forms
  • Example resources, industries, commodities
  • Interrelated crude-oil reserves, production,
    exports
  • Coal mining,computer industry,auto parts
    industry,
  • Specify basic definitions
  • A natural resource is a deposit of stuff an
    industry is a collection of businesses a
    commodity is an item whose sales can be measured
    as a continuous quantity
  • Examine related classes identify key factors
  • E.g., material, process, product, customer,
    location, task
  • Define each industry as a conjunction of factors
  • 6 generative factors discriminate 500 industries
  • Organize values of factors (miningltextraction)

24
Unified Medical Language System
  • Under development since 1986 by the National
    Library of Medicine
  • Supports standardize medical terminology via a
    central dictionary thesaurus semantic
    network search engine
  • Purpose is to aid the development of systems
    that help health professionals and researchers
    retrieve and integrate electronic biomedical
    information from a variety of sources and to make
    it easy for users to link disparate information
    systems, including computer-based patient
    records,bibliographic databases, factual
    databases, and expert systems.
  • There are four UMLS knowledge sources
  • UMLS Metathesaurus
  • SPECIALIST Lexicon
  • UMLS Semantic Network
  • UMLS Information Sources Map

25
Ontology Conclusions
  • Shared ontologies are essential for agent
    communication and knowledge sharing
  • Ontology tools and standards are important
  • Ontolingua and OKBC are good examples
  • XML and RDF may be a next step
  • Some large general ontologies are available
  • Cyc, WFB, WordNet,
  • For more information
  • http//www.kr.org/top describes projects
    addressing major ontology construction issues
  • Ontology mailing list send mail to
    majordomo_at_cs.umbc.edu with info ontology in
    message body for information.
  • ANSI Ad Hoc Group on Ontology Standards
    http//WWW-KSL.Stanford.EDU/onto-std/
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