From legacy KOS to full-fledged ontologies NKOS 2003-5-31
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Transcript of From legacy KOS to full-fledged ontologies NKOS 2003-5-31
From legacy KOS to full-fledged ontologies
NKOS 2003-5-31
Dagobert Soergel
Katy Newton
College of Information StudiesUniversity of Maryland
The problem
• AI and Semantic Web applications need full-fledged ontologies that support reasoning
• Constructing such ontologies is expensive
• While existing KOS do not provide the full set of precise concept relationships needed for reasoning,existing KOS, both large and small, represent much intellectual capital
• How can this intellectual capital be put to use in constructing full-fledged KOS
• Paper gives some examples and points for discussion
Steps in convertinga legacy KOS
1) Define the ontology structure
2) Fill in values from one or more legacy KOSto the extent possible
3) Edit manually using an ontology editor:
• make existing information more precise
• add new information
Pioneer: MedIndex by Susanne Humphrey
• Defined ontology structure through frames
• Created preliminary frame hierarchy by importing the MeSH hierarchy
• Used own ontology editor to
• enter slot fillers (some based on Related Term relationships) and
• refine hierarchical inheritance specifications
Example 1
Assume the rules
• Rule 1If X isa (type of) instruction and X has domain Zand Y isa ability and Y has domain ZThen X should consider Y
• Rule 2If X should consider Yand Y is supported by WThen X should consider W
Example 1, continued
ERIC Thesaurus entries
Reading instructionBT InstructionRT ReadingRT Learning standards
Reading abilityBT AbilityRT ReadingRT Perception
Example 1, continued
To apply the rules, we need
Reading instruction isa InstructionReading instruction has domain ReadingReading instruction governed by Learning standards
Reading ability isa AbilityReading ability has domain ReadingReading ability supported by Perception
Example 2
In MeSH (Medical Subject Headings, NLM)
• Hierarchical relationships are isa relationships
• Except, in the Anatomy section hierarchical relationships are part of relationships
Discovering such regularities can save a lot of manual editing
The Semantic Code
Perry, J.W. and Kent, A. Tools for Machine Literature Searching. New York: Interscience Publishers; 1958
There are some old systems that are close to full-fledged ontologies
Can be expressed in RDF or OWL
Semantic code
Semantic Factors Relationships
c-ng Alterationc-rm Ceramic or Glassd-tc Detectionm-ch Devicef-sh Fishn-dc Indicatorm-gn Magnetm-pr Material Propertym-tl Metalp-ss Processp-tt Protectiont-mm Timeh-cl Vehicle
q Affective
y Attributive
a Categorical
o Comprehensive
i Inclusive
w Instrumental
e Intrinsic
x Negative
u Productive
z Simulative
Semantic code examples
Windshield, A part of a vehicle that is composed of ceramic or glass and is used for protection.
Semantic code:
cerm hicl putt
ceramic: intrinsic vehicle: inclusive protection: productive
Semantic code examples
Dip needleA device that is influenced by magnetism to be used as an indicator.
Semantic code:
mach mqgn nudc
device: categorical magnet:affective indicator:productive
Semantic code examples
ModernizationA process that produces an alteration, characterized by time
Semantic code:
tymm cung pass
time: attributive alteration: productive process: categorical
Semantic code examples
Seal Shares properties with fish.
Semantic code:
fzsh
fish: simulative
Semantic code
Semantic factor hierarchy
1 General Concepts1.5 Forces
optics, magnet
1.6 Classifications1.6.2 According to nature
metal, fish, color
2 Relationships2.2 Physical Relationships
indicator, connection
3 States
3.1 Psychological States
protection
4 Processes
process
4.1 Physical Processes
detection
5 Substances
5.2 Specific substances
5.2.2 Inorganic substances
ceramic, metal
6 Objects
6.2 Specific objects
6.2.2 Specific Products
indicator, vehicle, pipe
Semantic code class hierarchy
<owl:versionInfo>1.0</owl:versionInfo></owl:Ontology>
<owl:Class rdf:ID="GeneralConcepts"> <rdfs:label>1 General Concepts</rdfs:label></owl:Class><owl:Class rdf:ID="Forces"> <rdfs:label>1.5 Forces</rdfs:label> <rdfs:subClassOf rdf:resource="GeneralConcepts"/></owl:Class>
<owl:Class rdf:ID="Magnet"> <rdfs:label>Magnet: m-gn</rdfs:label> <rdfs:subClassOf rdf:resource="GeneralizedSubstances" /> <rdfs:subClassOf rdf:resource="PropertiesInvolvingStates" /> <rdfs:subClassOf rdf:resource="Forces"/></owl:Class>
Semantic code examples
<owl:ObjectProperty rdf:ID="categorical"> <rdfs:comment>is a</rdfs:comment> <rdfs:label>categorical: A</rdfs:label> <rdf:type rdf:resource="owl:TransitiveProperty" /></owl:ObjectProperty>
<owl:ObjectProperty rdf:ID="simulative"> <rdfs:comment>shares properties with (but is not an instance of)</rdfs:comment> <rdfs:label>simulative: Z</rdfs:label> <rdf:type rdf:resource="owl:SymmetricProperty" /></owl:ObjectProperty>
Semantic code examples
<rdf:Description rdf:about="#windshield">
<inclusive rdf:resource="perry1.owl#Vehicle"/>
<intrinsic rdf:resource="perry1.owl#CeramicOrGlass"/>
<productive
rdf:resource="perry1.owl#Protection"/>
</rdf:Description>
Semantic code examples
<rdf:Description rdf:about="#dipNeedle">
<affective rdf:resource="perry1.owl#Magnet"/>
<categorical rdf:resource="perry1.owl#Device"/>
<productive rdf:resource="/perry1.owl#Indicator"/>
</rdf:Description>
<rdf:Description rdf:about="#seal">
<simulative rdf:resource="perry1.owl#Fish"/>
</rdf:Description>
Semantic code inference
Inference:
Fish shares properties with seal.
Rationale:
Seal is defined by a simulative relationship with fish. In the ontology, the simulative relationship is defined as a symmetrical property. If A is in a simulative relationship with B, then B is in a simulative relationship with A.
Judgment:
Good inference.
Semantic code inference
Inference:
A dip needle is a child of the class, product.
Rationale:
A dip needle is an instance of a device. Device is a subclass of product.
Judgment:
Good inference.
Not much use of KOS for AI ontology development
• Most ontology development in the AI community appears to start from scratch
• In the medical world many people start from UMLS
Conclusion
Don’t reinvent the wheel, improve it
Discussion