From legacy KOS to full-fledged ontologies NKOS 2003-5-31

1. From legacy KOS to full-fledged ontologiesNKOS 2003-5-31 Dagobert Soergel Katy Newton College of Information StudiesUniversity of Maryland dsoergel@umd.edu

2. 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

3. Steps in convertinga legacy KOS • Define the ontology structure • Fill in values from one or more legacy KOSto the extent possible • Edit manually using an ontology editor: • make existing information more precise • add new information

4. 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

5. 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 considerYand Y is supported byWThen X should consider W

6. Example 1, continued ERIC Thesaurus entries Reading instructionBT InstructionRT ReadingRT Learning standards Reading abilityBT AbilityRT ReadingRT Perception

7. 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

8. 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

9. 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

10. Semantic code

11. 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

12. 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

13. Semantic code examples ModernizationA process that produces an alteration, characterized by time Semantic code: tymm cung pass time: attributive alteration: productive process: categorical

14. Semantic code examples Seal Shares properties with fish. Semantic code: fzsh fish: simulative

15. Semantic code

16. 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>

17. 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>

18. 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>

19. 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>

20. 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.

21. 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.

22. 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

23. Conclusion • Don’t reinvent the wheel, improve it • Discussion