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

dsoergel@umd.edu

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