ontology training examples. what does linked mean? strategy serves retrieval, but not reasoning
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Ontology Training Examples Slide 2 What does linked mean? Strategy serves retrieval, but not reasoning Slide 3 poisoning of wells no global governance poor treatment of time data and objects confused uncontrolled proliferation of links 3 Slide 4 What you get with mappings All in Human Phenotype Ontology (= all phenotypes: excess hair loss, splayed feet...) mapped to all organisms in NCBI organism classification allose in ChEBI chemistry ontology Acute Lymphoblastic Leukemia in National Cancer Institute Thesaurus 4 Slide 5 What you get with mappings all phenotypes (excess hair loss, splayed feet...) all organisms allose (a form of sugar) Acute Lymphoblastic Leukemia 5 Slide 6 Mappings are hard They are fragile, and expensive to maintain The goal should be to minimize the need for mappings 6 Slide 7 Pistoia Alliance Open standards for data and technology interfaces in the life science research industry consortium of major pharmaceutical companies working to address the data silo problems created by multiplicity of proprietary terminologies declare terminology pre-competitive require shared use of OBO Foundry ontologies in presentation of information http://pistoiaalliance.org/ 7 Slide 8 An Example UCore SL Training Session ANSER Conference Center 2900 Quincy Street, Arlington, Virginia Wednesday, March 17, 2010 Barry Smith and Lowell Vizenor starts with Continental Breakfast 8am Slide 9 0900 Administrative Comments (Jim Schoening) 0910 Opening Remarks (Cliff Daus, Clay Robinson DoD CIO) 0930 UCore, the Net-Centric Data Strategy and the Ontological Perspective (BS) Ontology success stories, and some reasons for failure The promise of the Universal Core Realizing the Net-Centric Data Strategy UCore SL history / team / acknowledgements UCore SL benefits Slide 10 1030 UCore Semantic Layer: A Logically Enhanced (OWL) Version of the UCore Taxonomy (LV) Overview of UCore 2.0 Taxonomy Overview of UCore SL UCore SL Taxonomy UCore SL Relations Equivalence Relations Disjointness Axioms Restriction Classes Slide 11 1130 Effecting Successful Data Coordination (BS) The human factors: traffic rules for ontologists Top Down / Bottom Up (TDBU) methology Dealing with vocabulary conflicts across communities Registration of metadata Traffic rules for definitions Traffic rules for relations 1220 Lunch Slide 12 1330 Applications of UCore SL (BS) Using semantics for quality assurance of UCore Preamble on BFO: Role The change management process Creation of coherent extensions of UCore UCore SL and external resources NIEM, C2 Core 1430 Developing Ontologies with UCore SL (LV) How to extend UCore SL How to validate extensions of UCore SL How to represent temporal qualification of relations 1530 A Strategy for the Future (BS) 1630 Fin Slide 13 http://ontologist.com Slide 14 Lecture 7. Towards a Standard Upper Level Ontology Video Slides VideoSlides Scientific ontologies have special features Building scientific ontologies which work together demands a common set of ontological relations Basic Formal Ontology: benefits of coordination Users of BFO Continuants, occurrents, realizables Specific dependence, generic dependence, information artifacts Dispositions, roles, functions Diseases and disorders: the Ontology of General Medical Science Slide 15 Slide 16 HighFleet Training Sample Our approach is to introduce the ideas and syntax for Classes Relations Functions Rules and Integrity Constraints with hands-on exercises performed by trainees as a single group between sections 2010 HighFleet Inc. Slide 17 Taxonomy - how it works Mammal Cat Classes are the building blocks of taxonomies The subsumption relation organizes classes by generalization When we say that Mammal subsumes Cat, we mean: a) All Cats are Mammals b) Some Mammals are not Cats In the ULO, subsumption is called sup, for super-property sup Instances of classes are (also) instances of every super-class Slide 18 Reflexive Symmetric Transitive Irreflexive Anti-symmetric Asymmetric If R(x,y) then not R(y,x) If Steve is better than Bill, than Bill cant be better than Steve betterThan(x,y) If Steve is located in Kansas, then Kansas is not located in Steve locatedIn(x,y) tallerThan(x,y) properPartOf(x,y) Kinds of Binary Relations 2010 HighFleet Inc. Slide 19 ( and (Dog ?x) (Cat ?y) (chases ?x ?y) (Pet ?y) ) ( or (nextTo ?x John) (runningFrom ?x Pete) ) ( => (Person ?x) (Happy ?x) ) ( not (between ?x Mary ?y) ) ( exists (?x) (knows John ?x) ) ( forall (?x) (=> (knows John ?x) (Horse ?x)) ) Rules & Constraints 2010 HighFleet Inc. Slide 20 Training 3: The Pet Store Your client would like you to create an ontology for the entities described below. The instructor will provide any Subject Matter Expert information you request. Create a "Training 3 -- Solution.kfl" file and make an ontology to meet the client's needs. Use only Properties - no Relations or Functions should be used. Use the Training context ------------------------------ CLASSES Dogs, Cats, Birds, Snakes, Fish, Frogs, Lizards, Mice, Mammals, Reptiles, Amphibians, Birds, Fish, Species, Pets, Food, Guard Dogs, People, Customers, Employees, Banned Persons, VIP's, Managers, Suppliers, Mannequins, Plastic Dogs, Plastic Birds, Swedish Fish Candy 2010 HighFleet Inc. Slide 21 Training 6: Real Estate Your client would like you to create an ontology for the entities described below. The instructor will provide any Subject Matter Expert information you request. Create a "Training 6 -- Solution.kfl" file and make an ontology to meet the client's needs. Properties, Relations, and Functions may be used. Use the Training context ------------------------------ CLASSES Parcels of Land, Buildings, Houses, Factories, Shops Owned, Leased, and Rented Real Estate (if leased, for how many months?) RELATIONS "owner of (real estate)", "renter of (real estate) ", "groundskeeper of (parcel of land)" (?) locatedOn (?)", (?) has price (?)" (land parcel) has zoning (Residential, Commercial, Industrial) (real estate) leased for (N-many) months 2010 HighFleet Inc. Slide 22 Training 9: Car Manufacturing Your client would like you to create an ontology for the entities described below. The instructor will provide any Subject Matter Expert information you request. Create a "Training 9 -- Solution.kfl" file and make an ontology to meet the client's needs. Properties, Relations, and Functions, Rules, and Integrity Constraints may be used. Use the Training context ------------------------------ CLASSES: Manufacturing Companies, Cars, Engines, Tires, Seats RELATIONS "mass of", "max pressure of (tires)", "date of manufacture", "supplied by", "volume of (engines)", "color of (seats, cars [assume monochrome]) RULES AND CONSTRAINTS A car should have an engine. Cars that weigh 2500 kg must have an engine with a volume of 80 cubic cm. The tires of blue cars shouldn't be less than 30 psi All Boeing cars are blue with red seats. Every blue car that has all yellow seats was made in 1977. 2010 HighFleet Inc.