1. Ontological Engineering Barry Smith http://ontology.buffalo.edu Computers and Information in Engineering Conference, Buffalo August 19, 2014 1

2. http://ncorwiki.buffalo.edu/#Courses 2

3. Student Projects from 2013 David Lominac: Customer Ontology Lucas Mesmer: Manufacturing Ontology Chad Stahl: Chemical Manufacturing Ontology Xinnan Peng: Manufacturing Ontology John Beverley: Thermodynamic Equilibrium Ontology W. Hughes and M.Moskal: Unmanned Aerial Vehicle Ontology http://x.co/5HKlL 3

4. Student Projects from 2013 Kanchan Karadkar: Supply Chain Management Ontology Travis Allen: Twitter Ontology Jordan Feenstra and Yonatan Schreiber: Music Ontology Brian Donohue and Neil Otte: Personality Ontology Paul Poenicke: Gettier Problem Ontology Adam Houser: Game Artifact Ontology 4

5. W. Hughes and M.Moskal: Unmanned Aerial Vehicle Ontology 5

6. Ontology in Buffalo Ontology for the Intelligence Community (OIC, now STIDS) conference series Ontology work for National Nuclear Security Administration, DoE Joint-Forces Command Joint Warfighting Center Army Net-Centric Data Strategy Center of Excellence Army Intelligence and Information Warfare Directorate (I2WD) 6

7. Biomedical initiatives 7 Stanford Medical School Mayo Clinic University of California at San Francisco Cleveland Clinic Semantic Database Duke University Health System University of Pittsburgh Medical Center German Federal Ministry of Health European Union eHealth Directorate Plant Genome Research Resource Protein Information Resource

9. http://ncor.us 9

10. Some uses of ontologies Communication – between agencies, disciplines, people, machines 10

11. 11

12. US DoD Civil Affairs strategy for non-classified information sharing 12

13. Some uses of ontologies Communication – between agencies, disciplines, people, machines Data and resource management – between agencies, disciplines, people, things, money 13

14. 14

15. A business problem: too many silos DoD spends more than $6B annually developing a portfolio of more than 2,000 business systems and Web services these systems are poorly integrated deliver redundant capabilities make data hard to access, foster error and waste prevent secondary uses of data https://ditpr.dod.mil/https://ditpr.dod.mil/ Based on FY11 Defense Information Technology Repository (DITPR) data 15

16. The problem of retrieval, integration and analysis of siloed data massive legacy of non-interoperable data models and data systems as new systems are created, the situation is constantly getting worse “Big (Military) Data” 16

17. Some questions How to find data? How to understand data when you find it? How to use data when you find it? How to compare and integrate with other data? How to avoid data silos in the future? 17

18. Some uses of ontologies Communication – between agencies, disciplines, people, machines Data management – between agencies, disciplines, people, machines Data retrieval – across multiple structured and unstructured sources 18

19. Distributed Common Ground System – Army (DCGS-A) Semantic Enhancement of the Dataspace on the Cloud http://x.co/5HLRQ

20. Sources Source database Db1, with tables Person and Skill, containing person data and data pertaining to skills of different kinds, respectively. Source database Db2, with the table Person, containing data about IT personnel and their skills: Source database Db3, with the table ProgrSkill, containing data about programmers’ skills: PersonIDSkillID 111222 SkillIDNameDescription 222JavaProgramming IDSkillDescr 333SQL EmplIDSkillName 444Java 20

21. Ontology vs. Data Model The ontology provides a single synoptic view of the domain as opposed to the multiple flat and partial representations provided by the data models Computer Skill Single OntologyMultiple Data models Person Person Name First Name Last Name PersonSkill PersonName NetworkSkillProgrammingSkill Is-a Bearer-of Skill Last Name First Name Skill Person NameComputer Skill Programming Skill Network Skill 21

22. Sources Source database Db1, with tables Person and Skill, containing person data and data pertaining to skills of different kinds, respectively. Source database Db2, with the table Person, containing data about IT personnel and their skills: Source database Db3, with the table ProgrSkill, containing data about programmers’ skills: PersonIDSkillID 111222 SkillIDNameDescription 222JavaProgramming IDSkillDescr 333SQL EmplIDSkillName 444Java 22

23. Index Contents without the ontology Index EntryAssociated Field-Value 111, PersonIDName: Java Description: Programming 333, IDSkillDescr: SQL 444, EmplIDSkillName: Java Index entries based on native vocabularies 23 If an analyst is familiar with the labels used in Db1 and thus knows to enter Name = Java, his query will still return only: person 111. Salient information will be missed

24. Indexed Contents with the Ontology Index entries based on the SE and native (blue) vocabularies Index EntryAssociated Field-Value 111, PersonIDType: Person Skill: Java 333, PersonIDType: Person ComputerSkill: SQL 444, PersonIDType: Person ProgrammingSkill: Java 24

25. and then immediately PersonIDSkill 111Java 333SQL 444Java 25

26. Data Models enhanced through Ontologies PersonIDNameDescription 111JavaProgramming 222SQLDatabase SQLJavaC++ ProgrammingSkill ComputerSkill Skill Education Technical Education 26

27. How to ensure consistency? For this to be leveraged by different communities, persons, and applications it needs to be constructed in accordance with common, teachable principles 27 Fire Support LogisticsAir Operations Intelligence Civil-Military Operations Targeting Maneuver & Blue Force Tracking 27

28. To realize horizontal integration (HI) of intelligence data through ontology tagging HI =Def. the ability to exploit multiple data sources as if they are one  Problem: the data coming onstream are out of our control  Any strategy for HI must be agile = it can be quickly extended to new zones of emerging data according to need  Ontology can provide the needed agility and (incremental approach to) comprehensiveness 28

29. Benefits of the ontology tagging approach Does not interfere with the source content Enables the content to evolve in a cumulative fashion as it accommodates new kinds of data Can be developed in an incremental and distributed fashion Makes management and exploitation of the content more cost-effective 29 How to do this right?

30. Aristotle (384 – 322 BCE) 30

31. Aristotle (384 – 322 BCE) Metaphysics 31

32. Aristotle (384 – 322 BCE) Metaphysics – the lectures he gave after the physics Categories 32

33. Aristotle (384 – 322 BCE) Metaphysics – the lectures he gave after the physics Categories History of Animals, Generation of Animals, and Parts of Animals – earliest empirical biology Constitution of Athens – part of a (lost) database of 158 constitutions 33

34. 34 Aristotle's Constitutions

35. Hierarchy from Porphyry’s Introduction to Aristotle’s Categories 35

36. 36

37. 37

38. Linnaean Hierarchy 38

39. 39

40. Linnaean Hierarchy 40

41. Ontological dark ages Galileo, Bacon … – rise of empirical-quantitative vs. rational-qualitative science Darwin – against the fixity of species 41

42. Rediscovery of Ontology  1970: AI, Robotics: John McCarthy, Pat Hayes  1980:KIF: Knowledge Interchange Format, Tom Gruber … Watson … SIRI  2001: Semantic Web (OWL)  1990: Human Genome Project  1999: The Gene Ontology (GO)  2005: Open Biomedical Ontologies (OBO)  2007: National Center for Biomedical Ontology (NCBO) 42

43. Rediscovery 1: AI  Logic codes ‘ontological commitment’  1970: AI, Robotics: John McCarthy, Pat Hayes  1980:KIF: Knowledge Interchange Format, Tom Gruber … Watson … SIRI What would a robot have to believe / know in order to simulate human common sense (for example as involved in buying a salad in a restaurant)? Can we axiomatize human common sense? Can we create a qualitative physics? 43

44. Rediscovery 2: Semantic Web (2001) 44

45. Rediscovery 2: Semantic Web Knowledge representation and reasoning ‘Description logics’ DAML (DARPA Agent Markup Language) OWL (Web Ontology Language) – HTLM, XML, RDF, RDF(S), OWL … – RDF Triplestores + SPARQL query engines vs. traditional relational databases 45

46. Semantic web stack 2006

47. Netcentricity and Linked Open Data 47

48. Rediscovery 3: Biology  1990: Human Genome Project  1999: The Gene Ontology (GO)  2005: Open Biomedical Ontologies (OBO)  2007: National Center for Biomedical Ontology (NCBO) 48

49. Old biology data 49/

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52. how to link the kinds of phenomena represented here 52

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54. 54 or this?

55. Answer Create an ontology: a controlled logically structured consensus classification of the types of entities in the relevant domain All scientists in the domain use the same ontology aggressively to tag their data 55

56. 56 The Gene Ontology (fragment)

57. The ontology is a directed graph Nodes are terms Edges are relations such as subtype, part- of, regulates … Each term in the ontology has a logical definition to allow reasoning across the data tagged with that term 57

58. MouseEcotope GlyProt DiabetInGene GluChem sphingolipid transporter activity annotation using common ontologies allows navigation between databases 58

59. this allows discovery and integration of databases MouseEcotope GlyProt DiabetInGene GluChem Holliday junction helicase complex 59

62. GO provides a controlled system of terms for use in tagging experimental data multi-species, multi-disciplinary, open source contributing to the cumulativity of scientific results obtained by distinct research communities (human, mouse, fish, fly, …) compare: use of kilograms, meters, seconds … in formulating experimental results 62

63. Gene products involved in cardiac muscle development in humans 63

64. 64

65. > $100 mill. invested in literature curation using GO over 200 million annotations relating gene products described in the UniProt, Ensembl and other databases to terms in the GO (Gigascience 3/1/4) experimental results reported in 52,000 scientific journal articles manually annoted by expert biologists using GO ontologies provide the basis for capturing biological theories in computable form allows a new kind of biological research 65

66. GO Term Enrichment high-throughput experiments return sets of genes that are over- or underexpressed. We can functionally profile such sets of genes by determining which GO terms appear more frequently than would be expected by chance – e.g. in healthy vs cancer cells A new golden age of classification 66

67. GO originally developed by biologists It used its own flat-file format and its own ontology editing software Since ~2010 GO and the Semantic Web have moved more closely together Semantic web software tools, and editing environments such as Protégé and TopBraid make ontology creation easy 67

68. The problem of Big Data in biomedicine: Multiple kinds of data in multiple kinds of silos Lab / pathology data Electronic Health Record data Clinical trial data Patient histories Medical imaging Microarray data Protein chip data Flow cytometry Mass spec Genotype / SNP data each lab, each hospital, each agency has its own terminology for describing this data 68

69. Unifying goal: integration of biological and clinical data through tagging with ontologies – within and across domains – across different species – across levels of granularity (organ, organism, cell, molecule) – across different perspectives (physical, biological, clinical) What could go wrong? 69

70. 70 379 Ontologies

71. 71 http://bioportal.bioontology.org/search?q=obesity

72. 72

73. 73

74. 74

75. 75

76. 76

77. 77

78. Why the success of ontology still too often brings failure Ontologies are supposed to break down data silos … Unfortunately this very success is leading to the creation of multiple new silos, because multiple ontologies are being created in ad hoc ways (people do not get paid for re-using already existing ontologies) 78

79. Ontology success stories, and some reasons for failure A fragment of the Linked Open Data (dated 2009) 79

80. What does ‘linked’ mean?’ 80

81. Ontology success stories, and some reasons for failure 81

82. Divided we fail 82

83. United we also fail 83

84. Obesity, again 84

85. Can we save the day with mappings between terminologies? Mappings are fragile – since both sides of the mapping will change independently and expensive to maintain The goal should be to minimize the need for mappings By finding out how to create a good, robust ontology, and by creating one ontology module for each domain 85

87. GO is amazingly successful in overcoming problems of balkanization, especially for retrieval of data but it covers only generic biological entities of three sorts: – cellular components – molecular functions – biological processes and it does not provide representations of diseases, symptoms, anatomy, pathways, … 87

88. RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) Original OBO Foundry ontologies (Gene Ontology in yellow) 88

89. RELATION TO TIME GRANULARITY CONTINUANTOCCURRENT INDEPENDENTDEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Process (GO) Environment Ontology (EnvO) Environments 89

90. Anatomy Ontology (FMA*, CARO) Environment Ontology (ENVO) Infectious Disease Ontology (IDO*) Biological Process Ontology (GO*) Cell Ontology (CL) Cellular Component Ontology (FMA*, GO*) Phenotypic Quality Ontology (PATO) Subcellular Anatomy Ontology (SAO) Sequence Ontology (SO*) Molecular Function (GO*) Protein Ontology (PRO*) top level mid-level domain level Information Artifact Ontology (IAO) Ontology for Biomedical Investigations (OBI) Spatial Ontology (BSPO) Basic Formal Ontology (BFO) domain ontologies created by specialization from BFO

91. Basic Formal Ontology (BFO) core nodes domain ontologies created by specialization from BFO Independent continuants Dependent continuants Occurrents Classes Object types Attribute types Process types Particulars Individual objects Individual attributes Individual processes

92. BFO 2.0 92

93. – CHEBI: Chemical Entities of Biological Interest – GO: Gene Ontology – OBI: Ontology for Biomedical Investigations – PATO: Phenotypic Quality Ontology – PO: Plant Ontology – PATO: Phenotypic Quality Ontology – PRO: Protein Ontology – XAO: Xenopus Anatomy Ontology – ZFA: Zebrafish Anatomy Ontology http://obofoundry.org 93 http://www.ifomis.org/bfo/

94. 94