Ontological Engineering
Barry Smith
http://ontology.buffalo.edu
Computers and Information in Engineering Conference,
Buffalo
August 19, 2014
1
http://ncorwiki.buffalo.edu/#Courses
2
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
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
W. Hughes and M.Moskal: Unmanned
Aerial Vehicle Ontology
5
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
Biomedical initiatives
•
•
•
•
•
•
•
•
•
•
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
7
http://ncor.us
9
Some uses of ontologies
• Communication
– between agencies, disciplines, people, machines
10
11
US DoD Civil Affairs strategy for non-classified
information sharing
12
Some uses of ontologies
• Communication
– between agencies, disciplines, people, machines
• Data and resource management
– between agencies, disciplines, people, things,
money
13
14
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/ Based on FY11 Defense Information Technology
Repository (DITPR) data
15
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
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
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
Distributed Common Ground System – Army
(DCGS-A)
Semantic Enhancement
of the Dataspace
on the Cloud
http://x.co/5HLRQ
Sources
• Source database Db1, with tables Person and Skill, containing
person data and data pertaining to skills of different kinds,
respectively.
PersonID
SkillID
111
222
SkillID Name
222
Java
Description
Programming
• Source database Db2, with the table Person, containing data
about IT personnel and their skills:
ID
333
SkillDescr
SQL
• Source database Db3, with the table ProgrSkill, containing data
about programmers’ skills:
EmplID
444
SkillName
Java
20
Ontology vs. Data Model
Single Ontology
Multiple Data models
Person
Person
Skill
Person
Name
Computer
Skill
PersonName
NetworkSkill
ProgrammingSkill
PersonSkill
Last Name
Last
Name
Programming Network
Skill
Skill
Is-a
Bearer-of
First
Name
First Name
Skill
Skill
Person Name
Computer Skill
• The ontology provides a single synoptic view of the domain as
opposed to the multiple flat and partial representations
provided by the data models
21
Sources
• Source database Db1, with tables Person and Skill, containing
person data and data pertaining to skills of different kinds,
respectively.
PersonID
SkillID
111
222
SkillID Name
222
Java
Description
Programming
• Source database Db2, with the table Person, containing data
about IT personnel and their skills:
ID
333
SkillDescr
SQL
• Source database Db3, with the table ProgrSkill, containing data
about programmers’ skills:
EmplID
444
SkillName
Java
22
Index Contents without the ontology
Index entries based on native vocabularies
Index Entry
111, PersonID
Associated Field-Value
Name: Java
Description: Programming
333, ID
SkillDescr: SQL
444, EmplID
SkillName: Java
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
23
Indexed Contents with the Ontology
Index entries based on the SE and native (blue) vocabularies
Index Entry
Associated Field-Value
111, PersonID Type: Person
Skill: Java
333, PersonID Type: Person
ComputerSkill: SQL
444, PersonID Type: Person
ProgrammingSkill: Java
24
and then immediately
PersonID
111
333
444
Skill
Java
SQL
Java
25
Data Models enhanced through Ontologies
Education
Skill
Technical
Education
ComputerSkill
ProgrammingSkill
SQL
Java
C++
PersonID
Name
Description
111
Java
Programming
222
SQL
Database
26
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
Targeting
Maneuver
&
Blue
Force
Tracking
Intelligence
Fire
Support
Air Operations
Civil-Military
Operations
Logistics
27
27
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
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
How to do this right?
29
Aristotle (384 – 322 BCE)
30
Aristotle (384 – 322 BCE)
Metaphysics
31
Aristotle (384 – 322 BCE)
Metaphysics
– the lectures he gave after the physics
Categories
32
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
Aristotle's Constitutions
34
Hierarchy from Porphyry’s Introduction to
Aristotle’s Categories
35
36
37
Linnaean Hierarchy
38
39
Linnaean Hierarchy
40
Ontological dark ages
• Galileo, Bacon …
– rise of empirical-quantitative vs.
rational-qualitative science
• Darwin
– against the fixity of species
41
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
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
Rediscovery 2: Semantic Web (2001)
44
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
Semantic web stack 2006
Netcentricity and Linked Open Data
47
Rediscovery 3: Biology




1990: Human Genome Project
1999: The Gene Ontology (GO)
2005: Open Biomedical Ontologies (OBO)
2007: National Center for Biomedical Ontology
(NCBO)
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Old biology data
49/
New biology data
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GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRL
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ATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQ
WLGLESDYHCSFSSTRNAEDVDISRIVLYSYMFLNTAKGCLVEYA
TFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYE
50
SATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWI
How to do biology across the genome?
MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVIS
VMVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLER
CHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERL
KRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVC
KLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGIS
LLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWM
DVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSR
FETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVM
KVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISV
MVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERC
HEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLK
RDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCK
LRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLL
AFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMD
VVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRF
ETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVMK
VSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVM
VGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCH
EIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKR
DLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKL
RSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLL
AFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMD
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VSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVM
VGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCH
51
EIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKR
how to link the kinds of phenomena
represented here
52
MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRK
RSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSL
FYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLL
HVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNF
GAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLD
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to this?
or this?
54
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
The Gene Ontology
(fragment)
56
The ontology is a directed graph
Nodes are terms
Edges are relations such as subtype, partof, regulates …
Each term in the ontology has a logical
definition to allow reasoning across the
data tagged with that term
57
annotation using common ontologies allows
navigation between databases
GlyProt
MouseEcotope
sphingolipid
transporter
activity
DiabetInGene
GluChem
58
this allows discovery and integration of
databases
GlyProt
MouseEcotope
Holliday junction
helicase complex
DiabetInGene
GluChem
59
Number of abstracts mentioning "ontology" or
"ontologies" in PubMed/MEDLINE
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Number of abstracts mentioning "ontology" or
"ontologies" in PubMed/MEDLINE
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
GO
2005
2006
2007
others
2008
2009
2010
2011
2012
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
Gene products involved in cardiac muscle
development in humans
63
64
> $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
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
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
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
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
379 Ontologies
70
http://bioportal.bioontology.org/search?q=obesity
71
72
73
74
75
76
77
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
Ontology success stories, and
some reasons for failure
•
A fragment of the Linked Open
Data (dated 2009)
79
•
What does ‘linked’ mean?’
80
Ontology success stories, and
some reasons for failure
•
81
Divided we fail
82
United we also fail
83
Obesity, again
84
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
Number of abstracts mentioning "ontology" or
"ontologies" in PubMed/MEDLINE
2000
1800
1600
1400
1200
1000
800
600
400
200
0
1996
1997
1998
1999
2000
2001
2002
2003
2004
GO
2005
2006
2007
others
2008
2009
2010
2011
2012
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
RELATION
TO TIME
CONTINUANT
INDEPENDENT
OCCURRENT
DEPENDENT
GRANULARITY
ORGAN AND
ORGANISM
Organism
(NCBI
Taxonomy)
CELL AND
CELLULAR
COMPONENT
Cell
(CL)
MOLECULE
Anatomical
Organ
Entity
Function
(FMA,
(FMP, CPRO) Phenotypic
CARO)
Quality
(PaTO)
Cellular
Cellular
Component Function
(FMA, GO)
(GO)
Molecule
(ChEBI, SO,
RnaO, PrO)
Molecular Function
(GO)
Biological
Process
(GO)
Molecular Process
(GO)
Original OBO Foundry ontologies
(Gene Ontology in yellow)
88
RELATION
TO TIME
CONTINUANT
INDEPENDENT
OCCURRENT
DEPENDENT
ORGAN AND
ORGANISM
CELL AND
CELLULAR
COMPONENT
MOLECULE
Organism Anatomical
(NCBI
Entity
Taxonomy) (FMA, CARO)
Cell
(CL)
Cellular
Component
(FMA, GO)
Molecule
(ChEBI, SO,
RnaO, PrO)
Environments
GRANULARITY
Organ
Function
(FMP, CPRO)
Phenotypic
Quality
(PaTO)
Biological
Process
(GO)
Cellular
Function
(GO)
Molecular Function
(GO)
Molecular Process
(GO)
Environment Ontology (EnvO)
89
domain ontologies created by specialization from BFO
top level
Basic Formal Ontology (BFO)
Information Artifact
Ontology
mid-level
(IAO)
Ontology for
Biomedical
Investigations
(OBI)
Spatial
Ontology
(BSPO)
Anatomy Ontology
(FMA*, CARO)
domain
level
Infectious
Disease
Environment
Ontology
Cellular
Ontology
Cell
(IDO*)
Component (ENVO)
Ontology
Ontology
(CL)
Phenotypic
(FMA*, GO*)
Quality
Subcellular Anatomy Ontology
Ontology
(SAO)
(PATO)
Sequence Ontology
(SO*)
Protein Ontology
(PRO*)
Molecular
Function
(GO*)
Biological
Process
Ontology
(GO*)
domain ontologies created by specialization from BFO
Basic Formal Ontology (BFO)
core nodes
Independent
continuants
Dependent
continuants
Occurrents
Classes
Object types
Attribute
types
Process
types
Particulars
Individual
objects
Individual
attributes
Individual
processes
BFO 2.0
92
http://obofoundry.org
– 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://www.ifomis.org/bfo/
93
94