1. 1 Ontology Languages for Subject Matter Experts Conrad Bock Nov 3, 2005

2. 2 Knowledge Acquisition / Elicitation Bottleneck Marks comments Aug 25, 2005: –NCI infrastructure requires highly trained content specialists, wont scale, compare to Open Directory. –Arden adopted by vendors and academics, but failing due to lack of content. Key obstacle to success of ontologies is communication of knowledge from humans to machine. Same obstacle faced by expert systems, and never overcome.

3. 3 Knowledge Acquisition / Elicitation Bottleneck Observation: subject matter experts cannot easily adapt to existing knowledge languages. Diagnosis: –Existing knowledge languages are not well-suited for subject matter experts. –The problem is in the languages, not lack of SME training in the languages. –The concepts underlying knowledge languages originated in computer programming. –KLs are computer languages adapted for SMEs, without attention to SME mental models. –Not much advancement in KLs from SME viewpoint since expert system days (except some aspects of UML and a bit of OWL).

4. 4 Classes Originated in allocating and parsing blocks of memory. Not present in: –conventional logic (set and predicates dont have properties). –native subject matter expert discussion (classification doesnt necessarily mean a CS class, see default inheritance problems). SME use focus on instances and relations between sets of instances defined by navigating properties (roles).

5. 5 Power to wheels on different car than engine Example from Engineering No role model in class-based languages (OWL, UML class diagrams, Java, etc). Makes structured object modeling difficult. Boat EnginePropeller 1111 powers Car Wheel 21 powers 21 Wheels powered by engines in boats Propellers powered by engines in cars. Power to propellers on different boat than the engine

6. 6 Example from Engineering Property specialization complicated and and doesnt completely solve the problem. Can still have engines powering wheels in other cars, etc. Classes cant refer to results of property navigation from instances. PowerSource PowerTransmitter 1..*1 /transmitter 1..* {union} /source 1 {union} /powers Vehicle 1 1 /powerSource 1 {union} /inVehicle 1 {union} 1..* 1 /powerTransmitter 1..* {union} /inVehicle 1 {union} BackWheel EnginePropeller BoatEngine transmitter {redefines transmitter} source {redefines source} powers Wheel FrontWheel CarEngine 2 transmitter 2 {redefines transmitter} source {redefines source} powers

7. 7 Example from Engineering SMEs view (eg, UML 2 composite structure diagrams) Boat : Engine : Propeller 11 1 powers Car : Engine: Wheel 1 2 powers Engine as used in each Car Engine as used in each Boat Powers as used in each Boat Powers as used in each Car

8. 8 SME and CS Views SME view highlights relations between sets of instances (roles), CS view highlight classes Car 21 powers 21 Wheel 0..1 22 back front Car e: Engine 1 powers back : Wheel 2 front : Wheel 2 Engine 1 0..1 e Class Diagram (CS) Composite Structure Diagram (SME)

9. 9 SME and Logical Views Car e: Engine 1 powers back : Wheel 2 front : Wheel 2 Composite Structure Diagram (SME) (forall (?c) (implies/iff (Car ?c) (exists (?eng ?bw ?fw) (and (Engine ?eng) (Wheel ?bw) (Wheel ?fw) (e ?c ?eng) (front ?c ?fw) (back ?c ?bw) (powers ?eng ?bw))))) Cardinalities / Multiplicities cumbersome in FOL without extension. SME view closer to first-order logic.

10. 10 Rules Attempt to simplify procedural control structures. Not present in conventional logic (implication includes contrapositive) Not present in isolation in native SME discussion (SME rules specified in the context of procedures). Suggest looking at languages where rules and processes are comparable (eg, Planner-like languages that allow queries and assertions in any order, or process constraint languages).

11. 11 Prognosis Will grass roots movement around OWL overcome the obstacles? Will ontologies go the way of expert systems? Reduce risk by extending ontology and rule languages from SME viewpoint. Address SME need for contextualized modeling techniques.

12. 12 Some References Bock, UML 2 Composition Model, Journal of Object Technology, 3:10, 47-73, November- December, 2004. Bock, Goal-driven Modeling, Journal Of Object- Oriented Programming, 13:5, 25-28, September 2000. Krogh, et. al., Strictly Class-based Modeling Can Be Harmful, HICSS 29, 1996. Weber and Shanks ACM papers on conceptual modeling.