1. From legacy KOS to full-fledged ontologies NKOS 2003-5-31 Dagobert Soergel Katy Newton College of Information Studies University 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 converting a legacy KOS 1)Define the ontology structure 2)Fill in values from one or more legacy KOS to the extent possible 3)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 1 If X isa (type of) instruction and X has domain Z and Y isa ability and Y has domain Z Then X should consider Y Rule 2 If X should consider Y and Y is supported by W Then X should consider W

6. Example 1, continued ERIC Thesaurus entries Reading instruction BT Instruction RT Reading RT Learning standards Reading ability BT Ability RT Reading RT Perception

7. Example 1, continued To apply the rules, we need Reading instructionisaInstruction Reading instructionhas domainReading Reading instructiongoverned byLearning standards Reading abilityisaAbility Reading abilityhas domainReading Reading abilitysupported byPerception

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 Semantic FactorsRelationships c-ngAlteration c-rmCeramic or Glass d-tcDetection m-chDevice f-shFish n-dcIndicator m-gnMagnet m-prMaterial Property m-tlMetal p-ssProcess p-ttProtection t-mmTime h-clVehicle qAffective yAttributive aCategorical oComprehensive iInclusive wInstrumental eIntrinsic xNegative uProductive zSimulative

11. Semantic code examples Windshield, A part of a vehicle that is composed of ceramic or glass and is used for protection. Semantic code: cermhiclputt ceramic: intrinsicvehicle: inclusiveprotection: productive

12. Semantic code examples Dip needle A device that is influenced by magnetism to be used as an indicator. Semantic code: machmqgnnudc device: categoricalmagnet:affectiveindicator:productive

13. Semantic code examples Modernization A process that produces an alteration, characterized by time Semantic code: tymmcungpass time: attributivealteration: productiveprocess: categorical

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

15. Semantic code Semantic factor hierarchy 1 General Concepts 1.5 Forces optics, magnet 1.6 Classifications 1.6.2 According to nature metal, fish, color 2 Relationships 2.2 Physical Relationships indicator, connection 3 States 3.1Psychological States protection 4 Processes process 4.1 Physical Processes detection 5Substances 5.2 Specific substances 5.2.2 Inorganic substances ceramic, metal 6Objects 6.2 Specific objects 6.2.2 Specific Products indicator, vehicle, pipe

16. Semantic code class hierarchy 1.0 1 General Concepts 1.5 Forces Magnet: m-gn

17. Semantic code examples is a categorical: A shares properties with (but is not an instance of) simulative: Z

18. Semantic code examples <productive rdf:resource="perry1.owl#Protection"/>

19. Semantic code examples

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