Shared Ontologies
Christoph F. Eick
www.cs.uh.edu/~ceick/ceick.html
University of Houston
Organization
1. What are Ontologies?
2. What are they good for?
3. Ontologies and Brokering
4. Critical Problems with Respect to Shared Ontologies
What are Ontologies?
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“Ontologies are content theories about sorts of objects, properties of objects, and
relationship between objects that are possible in a specified domain of knowledge”
(Chandrasekaran)
“We consider ontologies to be domain theories that specify a domain-specific
vocabulary of entities, classes, properties, predicates, and functions, and a set of
relationships that necessarily hold among those vocabulary items” (Fikes)
“Shared ontologies form the basis for domain specific knowledge representation
languages” (Chandrasekaran)
“If we could develop ontologies that could be used as the basis of multiple systems,
they would share a common terminology that would facilitate sharing and reuse”
(W. Swartout)
“Ontologies play an important role for the standardization of terminology in
medicine (e.g. UMLS) and other domains”
“Ontologies can serve as the glue between knowledge that is represented at
different, usually heterogeneous information sources.”
What are Ontologies good for?
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As a shared conceptual model of a particular application domain that describes the
semantics of the objects that are part of the domain, and captures knowledge that is
inherent to the particular domain --- idea: knowledge base .
Ontologies provide a vocabulary for representing knowledge about a domain and
for describing specific situations in a domain (tool for defining and describing
domain-specific vocabularies) --- idea: language for communication
For data/knowledge translation and transformation (provide a solution to the
translation problem between different terminologies); for fusion and refinement of
existing knowledge --- idea: interoperation
 For matchmaking between users, agents, and information resources in agent-based
systems --- idea: collaboration, brokering
focus of next slides
 As reusable building blocks to build systems that solve particular problems in the
application domain --- idea: model reuse
Summary: “Ontologies can be used as building block components of knowledge bases,
object schema for object-oriented systems, conceptual schema for data bases,
structured glossaries for human collaborations, vocabularies for communication
between agents, class definitions for conventional software system, etc.” (Fikes)
Key Ideas Agent-based Technologies
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“Agents operate independently and anticipate user needs” (P. Maes)
“Agent help users suffering from information overload” (O. Etzioni) rather to mimic
human intelligence
“Agents are important because the allow users to interoperate with modern
applications such as electronic commerce and information retrieval. Most of these
applications assume that components are added dynamically and that they will be
autonomous (serve different users and providers to fill different goals) and
heterogeneous.” (M. Singh)
“Essentially, agent-based architectures are characterized by three key features:
autonomy, adaptation, and cooperation. Agent-based systems are computational
systems in which several agents interact for their own good and for the good of the
overall system.
“In an agent-based architecture services are provided in the context of a community
of loosely coupled agents of various types in a distributed environment.”
“Agents are aware of their environment and capable of communicating with other
agents that belong to the same agent community”.
Ontologies and Brokering
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Service providers describe their capabilities in terms of a domain (or task)
ontology
Agents that seek services describe their needs in terms of a domain (or task)
ontology
Broker agents server as matchmakers between service providers and service
seekers by finding suitable agents and by evaluating the extent to which they can
provide those services relying on a semantic brokering approach.
Various languages have been advocated in the recent years to specify ontologies:
OKBC, CKML/OML, ONTOLINGUA, XML, UMLS, SNOMED, GALEN...
Service
Provider
Agents
A “Traditional” Approach
End User
Agents
Specify keywords
with respect to the
documents they are
looking for
Search Engine
Abstract
Clinical Trial Report
Summary
Clinical
Trial Report
Semantic Brokering Approach
Service
Provider
Agents
End User
Agents
Semantic Brokering
Specify subset of
ontology
:= matchmaking
Subset of an
Ontology
Summary
Clinical
Trial Report
Example Semantic Brokering
Data Analyst’s Information Requirement
Patient
Age>40
weight
Intensive-CarePatient
Hours-in-intensive-care
Data Collection1
Result Semantic Brokering:
((DataCollection1 nil ((missing slot weight)
(contradictory (< age 15) (> age 40))
(DataCollection2 t)
(DataCollection3 t ((> age 60)(> weight 300)))
Data Collection2
Data Collection3
Patient
Patient
Patient
Age<15
age
Age>60
weight
Intensive-CarePatient
Hours-in-intensive-care
Intensive-CarePatient
Hours-in-intensive-care
Weight>300
Intensive-CarePatient
Hours-in-intensive-care
Critical Problems
with Respect to Shared Ontologies
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Scientific communities have to agree on ontologies; otherwise, the whole approach
is flawed.
Development of ontologies for a particular domain is a difficult task (see Digital
Anatomist project at UW, development of UMLS). The development of user
friendly, and intelligent knowledge acquisition tools is very important for the
successful development of shared ontologies.
Expressiveness of languages that are used to define ontologies limits what can be
done with domain ontologies.
Reasoning capabilities are important for systems that use shared ontologies (we
need a language to specify ontologies and an inference engine that can reason with
the given ontologies)
– finding inconsistencies in knowledge bases, for finding errors at data entry
– semantic brokering
– more intelligent mappings between terms
– ...
References
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WWW-Links:
– http://ksl-web.stanford.edu/Reusable-ontol/P001.html (Richard Fikes’ (Stanford
University) Slide Show on “Reusable Ontologies”
– http://www.cs.cmu.edu/~softagents/ (CMU Intelligent Software Agents Page)
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Papers:
– Special Issue IEEE Intelligent Systems on “Coming to Terms with Ontologies”,
Jan./Feb. 1999.
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