How to Build an Ontology

How to Build an Ontology Barry Smith http://ontology.buffalo.edu/smith 1 Mission of the NCBO To create software and support services for science-based ontology development and use in the biomedical domain Science-based = ontologies for support of scientific research (taken as encompassing evidence-based medicine) Science-based = using the scientific method as part of the process of ontology development and testing 2 Scientific ontologies have special features Every term in a scientific ontology must be such that the developers of the ontology believe it to refer to some entity on the basis of the best current evidence 5 For scientific ontologies reusability is crucial compatibility with neighboring scientific ontologies is crucial  it should not be too easy to add new terms to an ontology we want to introduce these features in clinical medicine ... 6 An Ontological Square Upper-level integrating ontologies Domain ontologies 10 An Ontological Square Upper-level integrating ontologies Ontologies in support of science Administrative ontologies 11 Domain ontologies An Ontological Square Upper-level integrating ontologies Ontologies in support of science Administrative ontologies (for ecommerce, etc.) Domain ontologies BFO (Basic Formal SNOMED Ontology) SwissProt DOLCE FMA FOAF top level: person, topic, document, primary topic ... Amazon.com ontology Library of Congress Catalog 12 Problem of ensuring sensible cooperation in a massively interdisciplinary community concept type instance model representation data 13 from Handbook of Ontology (Semantic Web approach) RetailPrice hasA Denomination InstanceOf Dollar (p. 101) SI-Unit instanceof System-of-Units (p. 40) 14 from: Ontological Engineering (Semantic Web approach) location =def. a spatial point identified by a name (p. 12) arrivalPlace =def. a journey ends at a location (p. 13) facet =def. ternary relation that holds between a frame, a slot, and the facet (p. 51) 15 Entity =def anything which exists, including things and processes, functions and qualities, beliefs and actions, documents and software (Levels 1, 2 and 3) 16 First basic distinction universal vs. instance (science text vs. diary) (man vs. Maximilian) 17 Instances  databases For scientific ontologies it is generalizations that are important = universals, types, kinds, species 18 Catalog vs. inventory A B C 515287 521683 521682 DC3300 Dust Collector Fan Gilmer Belt Motor Drive Belt 19 Catalog vs. inventory 20 Catalog of Universals/Types Ontology Universals Instances 22 Ontology = A Representation of Universals 23 Each node of an ontology consists of: • preferred term (aka term) • term identifier (TUI, aka CUI) • synonyms • definition, glosses, comments Ontology = A representation of universals 24 An ontology is a representation of universals We learn about universals in reality from looking at the results of scientific experiments in the form of scientific theories – which describe not what is particular in reality but what is general 25 universals animal mammal substance organism cat leaf class siamese frog instances Domain =def a portion of reality that forms the subjectmatter of a single science or technology or mode of study or administrative practice ...; proteomics HIV epidemiology 27 Representation =def an image, idea, map, picture, name or description ... of some entity or entities. 28 Ontologies are representational artifacts comparable to science texts 29 The Periodic Table Periodic Table 33 Ontologies are here 34 or here 35 What do ontologies represent? 36 Ontologies do not represent concepts in people‟s heads 37 They represent universals in reality 38 “leg” is not the name of a concept concepts do not stand in part_of connectedness causes treats ... relations to each other 39 instances A B C 515287 521683 521682 DC3300 Dust Collector Fan Gilmer Belt Motor Drive Belt universals Inventory vs. Catalog: Two kinds of composite representational artifacts Databases represent instances Ontologies represent universals 41 How do we know which general terms designate universals? Roughly: terms used by scientists to designate entities about which we have a plurality of different kinds of testable proposition (cell, electron ...) 42 Problem: fiat demarcations male over 30 years of age with family history of diabetes abnormal curvature of spine participant in trial #2030 43 Problem: roles fist patient FDA-approved drug 44 Administrative ontologies often need to go beyond universals Fall on stairs or ladders in water transport injuring occupant of small boat, unpowered Railway accident involving collision with rolling stock and injuring pedal cyclist Nontraffic accident involving motor-driven snow vehicle injuring pedestrian 45 universals vs. classes universals {a,b,c,...} classes 46 Class =def a maximal collection of particulars determined by a general term („cell‟. „electron‟), („ „restaurant in Palo Alto‟, „Italian‟) the class A = the collection of all particulars x for which „x is A’ is true 47 Problem The same general term can be used to refer both to universals and to collections of particulars. Consider: HIV is an infectious retrovirus HIV is spreading very rapidly through Asia 48 universals vs. classes universals {c,d,e,...} classes 49 Extension =def The extension of a universal A is the class: instance of the universal A (it is the class of A’s instances) (the class of all entities to which the term „A‟ applies) 50 universals vs. classes universals defined classes 51 universals vs. classes universals populations, ... 52 Defined class =def a class defined by a general term which does not designate a universal the class of all diabetic patients in Leipzig on 4 June 1952 53 OWL is a good representation of defined classes • sibling of Finnish spy • member of Abba aged > 50 years 54 Terminology =def. a representational artifact whose representational units are natural language terms (with IDs, synonyms, comments, etc.) which are intended to designate universals together with defined classes. 55 universals, classes, concepts universals defined classes „concepts‟ 56 universals < defined classes < „concepts‟ „concepts‟ which do not correspond to defined classes: „Surgical or other procedure not carried out because of patient's decision‟ „Absent nipple‟ 57 (Scientific) Ontology =def. a representational artifact whose representational units (which may be drawn from a natural or from some formalized language) are intended to represent 1. universals in reality 2. those relations between these universals which obtain universally (= for all instances) lung is_a anatomical structure lobe of lung part_of lung 58 Part II How to Build an Ontology 59 How to build an ontology work with scientists to create an initial top-level classification find ~50 most commonly used terms corresponding to universals in reality arrange these terms into an informal is_a hierarchy according to this Universality principle A is_a B  every instance of A is an instance of B fill in missing terms to give a complete hierarchy (leave it to domain scientists to populate the lower levels of the hierarchy) 60 Principle of Low Hanging Fruit Include even absolutely trivial assertions (assertions you know to be universally true) pneumococcal virus is_a virus Computers need to be led by the hand 61 MeSH MeSH Descriptors Index Medicus Descriptor Anthropology, Education, Sociology and Social Phenomena (MeSH Category) Social Sciences Political Systems National Socialism National Socialism is_a Political Systems National Socialism is_a Anthropology ... 62 Principle Use singular nouns Terms in ontologies represent universals 63 Goal: Each term in an ontology represents exactly one universal there are universals also of collectivities: population complex of cells 64 the use-mention confusion Conceptual Entities =Def. An organizational header for concepts representing mostly abstract entities. swimming is healthy and has eight letters 65 Principle Avoid confusing between words and things Avoid confusing between concepts in our minds and entities in reality Recommendation: avoid the word „concept‟ entirely 66 Trialbank „information‟ = def. „a written or spoken designation of a concept‟ 67 Trialbank „Heparin therapy‟ is an instance of „written or spoken designation of a concept‟ What are the problems here? 1. misuse of quotation marks 2. confusion of instances and universals 3. confusion of concept and reality 68 Plant Ontology cell = def. plant cell, consisting of protoplast and cell wall; ... 69 Principle For the sake of interoperability with other ontologies, do not give special meanings to terms with established general meanings (Don‟t use „cell‟ when you mean „plant cell‟) 70 ICNP: International Classification of Nursing Procedures water =def. a type of Nursing Phenomenon of Physical Environment with the specific characteristics: clear liquid compound of hydrogen and oxygen that is essential for most plant and animal life influencing life and development of human beings. 71 Principle Supply definitions wherever possible (both human-understandable natural language definitions, and equivalent formal definitions) 72 Principle Each term should have at most one definition* *which may have both natural-language and formal versions 73 The Problem of Circularity A Person = def. A person with an identity document cell = def. plant cell, consisting of protoplast and cell wall; ... 74 Principle Avoid circular definitions (The term defined should not appear in its own definition) 75 HL7 „stopping a medication‟ = def. change of state in the record of a Substance Administration Act from Active to Aborted 76 Principle A definition should use terms which are easier to understand than the term defined (HL7 creates a topsy turvy world, in which simple things are made difficult) 77 Principle Use Aristotelian definitions An A is a B which C‟s. 78 Principle Do not seek to define everything 79 In every ontology some terms and some relations are primitive = they cannot be defined (on pain of infinite regress) Examples of primitive relations: identity instance_of 80 Rules for formatting terms • Avoid abbreviations even when it is clear in context what they mean („breast‟ for „breast tumor‟) • Avoid acronyms • Avoid mass terms („tissue‟, „brain mapping‟, „clinical research‟ ...) • Treat each term „A‟ in an ontology is shorthand for a term of the form „the universal A‟ 83 Univocity Terms should have the same meanings on every occasion of use. (They should refer to the same universals) Basic ontological relations such as is_a and part_of should be used in the same way by all ontologies 84 Universality Ontologies should include only those relational assertions which hold universally pneumococcal virus causes pneumonia 85 Universality Often, order will matter: We can assert adult transformation_of child but not child transforms_into adult 86 Universality viral pneumonia caused by virus but not virus causes pneumonia pneumococcal virus causes pneumonia 87 Universality protocol-design earlier_than results analysis but not results analysis later_than protocol-design 88 Positivity Complements of universals are not themselves universals. Terms such as non-mammal non-membrane other metalworker in New Zealand do not designate universals in reality 89 Ontology of universals  logic of terms There are no conjunctive and disjunctive universals: anatomic structure, system, or substance musculoskeletal and connective tissue disorder rheumatism, excluding the back 90 Objectivity Which universals exist in reality is not a function of our knowledge. Terms such as unknown unclassified unlocalized arthropathies not otherwise specified do not designate universals in reality. 91 Keep Epistemology Separate from Ontology If you want to say that We do not know where A’s are located do not invent a new class of A’s with unknown locations (A well-constructed ontology should grow linearly; it should not need to delete classes or relations because of increases in knowledge) 92 Keep Sentences Separate from Terms If you want to say I surmise that this is a case of pneumonia do not invent a new class of surmised pneumonias 93 Single Inheritance No kind in a classificatory hierarchy should have more than one is_a parent on the immediate higher level 94 Multiple Inheritance thing blue thing is_a blue car car is_a 95 Multiple Inheritance is a source of errors encourages laziness serves as obstacle to integration with neighboring ontologies hampers use of Aristotelian methodology for defining terms hampers use of statistical search tools 96 Multiple Inheritance thing blue thing is_a1 blue car car is_a2 97 is_a Overloading The success of ontology alignment demands that ontological relations (is_a, part_of, ...) have the same meanings in the different ontologies to be aligned. 98 Compositionality The meanings of compound terms should be determined 1. by the meanings of component terms together with 2. the rules governing syntax 99 Why do we need rules/standards for good ontology? Ontologies must be intelligible both to humans (for annotation and curation) and to machines (for reasoning and error-checking): the lack of rules for classification leads to human error and blocks automatic reasoning and error-checking Intuitive rules facilitate training of curators and annotators Common rules allow alignment with other ontologies 100 ontologies are legends for cartoons Randomized controlled trials http://rctbank.ucsf.edu/ontology/outline/index.htm 102 Top-Level Class Hierarchy for RCT Root Secondary-study Trial-details Trial Concept • • • • • • • Generic-concept Population-concept Protocol-concept Design-concept Outcome-concept Administrative-concept Intervention-concept 103 Trial Details Root Secondary-study Trial-details • • • • Erratum Publication-details Trial-entry-details Administrative-details – Secondary-administrative-details – Primary-administrative-details » Executed-administrative-details » Intended-administrative-details • Conclusion-details • Background-details – Intended-background-details – Executed-background-details • • • • Stopping-details Retraction-details Correction-details Fraud-details 104 Top-Most Class Hierarchy for RCT Root Secondary-study Trial-details Trial Concept • • • • • • • Generic-concept Population-concept Protocol-concept Design-concept Outcome-concept Administrative-concept Intervention-concept 105 Concept • Generic-concept – – – – – – – – – – – – – – – – – – Term-information Time-entity Rule-concept Situation Subgroup Recruitment-flowchart Population Recruitment Site-enrollment Follow-up-compliance Follow-up-activity Follow-up Protocol-change Treatment-assignment Protocol Reason Outcomes-followup Secondary-study-protocol 106 • Population-concept • Protocol-concept Concept • Design-concept – – – – – – – – – – – – – – – Survival-analysis-and-results Statistical-analysis-and-results Sample-size-calculation Trial-design Hypothesis-concept Study-objective Study-monitoring Regression-analysis-and-results Stopping-rule Special-variable-information Outcome-assessment Miscellaneous-outcome-entity Result-entity Outcome-value-entity Outcome 107 • Outcome-concept Concept • Administrative-concept – – – – – – – – Publication-concept Study-site Person Ethics Study-committee Funder Institution Registry-id • Intervention-concept – – – – – – – – Blinding-concept Compliance-details Intervention-step Intervention-arm Co-intervention Intervention Compliance-result Intervention-logic 108 Top-Level Class Hierarchy for RCT Root Secondary-study Trial-details Trial Concept • • • • • • • Generic-concept Population-concept Protocol-concept Design-concept Outcome-concept Administrative-concept Intervention-concept 109 What the top level should look like 110 Two kinds of entities occurrents (processes, events, happenings) continuants (objects, qualities, states...) 111 Continuants (aka endurants) have continuous existence in time preserve their identity through change exist in toto whenever they exist at all Occurrents (aka processes) have temporal parts unfold themselves in successive phases exist only in their phases 112 You are a continuant Your life is an occurrent You are 3-dimensional Your life is 4-dimensional 113 Dependent entities require independent continuants as their bearers There is no run without a runner There is no grin without a cat 114 Dependent vs. independent continuants Independent continuants (organisms, buildings, environments) Dependent continuants (quality, shape, role, propensity, function, status, power, right) 115 All occurrents are dependent entities They are dependent on those independent continuants which are their participants (agents, patients, media ...) 116 BFO Top-Level Ontology Continuant Occurrent (always dependent on one or more independent continuants) Independent Continuant Dependent Continuant 117 = A representation of top-level types Continuant Occurrent biological process Independent Continuant Dependent Continuant cell component molecular function 118 Top-Level Ontology Continuant Occurrent Independent Continuant Dependent Continuant Side-Effect, Stochastic Process, ... Functioning Function 119 Top-Level Ontology Continuant Occurrent Independent Continuant Dependent Continuant Functioning Side-Effect, Stochastic Process, ... Function 120 Top-Level Ontology Continuant Occurrent Independent Continuant Dependent Continuant Functioning Side-Effect, Stochastic Process, ... Quality Function Spatial Region instances (in space and time) 121 122 123 CTO will be part of OBI Ontology of Biomedical Investigations http://obi.sourceforge.net which is in turn part of the OBO Foundry http://obofoundry.org 124 125 126 127 128 129 Top-Level Class Hierarchy for RCT Root Secondary-study Trial-details Trial Concept • • • • • • • Generic-concept Population-concept Protocol-concept Design-concept Outcome-concept Administrative-concept Intervention-concept 132 Amended Top-Level Class Hierarchy for RCT Entity Continuant Population Protocol Design Occurrent Trial Secondary-study Intervention ?? Trial-details ?? Outcome-concept ?? Administrative-concept 133 Concept • Generic-concept – Term-information – Time-entity – Rule-concept » Clinical-rule Exclusion-rule Inclusion-rule » Rule-entity Recursive-rule Base-rule » Ethnicity-language-rule » Age-gender-rule » Situation 134 135 136 Concept • Protocol-concept – – – – – – – – – Follow-up-compliance Follow-up-activity Follow-up Protocol-change Treatment-assignment Protocol Reason Outcomes-followup Secondary-study-protocol 137 Amended Top-Level Class Hierarchy for RCT Entity Continuant Protocol • Secondary-study-protocol Reason Occurrent • Treatment-assignment • Follow-up – Follow-up-activity – Outcomes-follow-up • Protocol-change 138 Concept • Population-concept – – – – – Subgroup Recruitment-flowchart Population Recruitment Site-enrollment 139 Amended Top-Level Class Hierarchy for RCT Entity Continuant Protocol • Secondary-study-protocol Recruitment-flowchart Reason Population • Subgroup Occurrent • Priors – Recruitment – Site-enrollment – Treatment-assignment • Follow-up – Follow-up-activity – Outcomes-follow-up • Protocol-change 140 Concept • Administrative-concept – – – – – – – – Publication-concept Study-site Person Ethics Study-committee Funder Institution Registry-id 141 Continuant • Information object – Publication – Registry-ID • Study-site • Person • Institution – Study-committee – Funder ???Ethics 142 Concept • Intervention-concept – – – – – – – – Blinding-concept Compliance-details Intervention-step Intervention-arm Co-intervention Intervention Compliance-result Intervention-logic 143 Occurrent • Intervention – – – – Blinding Intervention-step Intervention-arm Co-intervention • ??? Intervention-logic • ??? Compliance-result • ??? Compliance-details 144 END 167