20141112 courtot big_datasemwebontologies

Big data, Seman-c Web and Ontologies

Mélanie Courtot, PhD Nov 12th 2014

[email protected]

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About me

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Overview

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•  Big Data –  Big Data is BIG –  Issues in research

•  SemanHc Web –  Standards: URIs, RDF, SPARQL, OWL –  Linked data

•  Ontologies –  DefiniHon and reasoning –  OBO Foundry –  Example of exisHng ontologies –  Pharmacovigilance –  Publishing ontologies on the SemanHc Web

•  IRIDA –  The IRIDA plaXorm –  Adding standards to IRIDA

•  Take home message

Overview

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Big data

Big data is data that is too large and complex to process for any convenHonal data tools.

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2005

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2013

What is a Ze^abyte?

1,000,000,000,000 gigabytes 1,000,000,000,000 terabytes 1,000,000,000,000 petabytes 1,000,000,000,000 exabytes 1,000,000,000,000 zeAabyte

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How big is big?

•  Facebook: 25 Terabytes of logged data per day, Google (2008): 20 Petabytes per day

•  Over 90% of all the data in the world was created in the past 2 years [1]

•  Today 3.2 ze^abytes. 2020: 40 zeAabytes.[2] •  Good news: jobs! [3]

1.  http://www-01.ibm.com/software/data/bigdata/ 2.  http://barnraisersllc.com/2012/12/38-big-facts-big-data-companies/ 3.  http://www.webopedia.com/quick_ref/important-big-data-facts-for-it-professionals.html

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11 h^ps://hbr.org/2012/10/data-‐scienHst-‐the-‐sexiest-‐job-‐of-‐the-‐21st-‐century

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Issues with research data (1): data availability

h^p://www.nature.com/news/scienHsts-‐losing-‐data-‐at-‐a-‐rapid-‐rate-‐1.14416

Issues with research data (2): data reproducibility

13 h^p://www.firstwordpharma.com/node/931605#axzz3IalL2lzU

Overview

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•  Seman-c Web –  Standards: URIs, RDF, SPARQL, OWL –  Linked data




A soluHon: the SemanHc Web

"The Seman*c Web is an ... extension of the current web in which ... informa*on is given well-‐defined meaning, ... be?er enabling computers and people to work in coopera*on.” The Seman)c Web Tim Berners-‐Lee, James Hendler and Ora Lassila ScienHfic American, May 2001

15 http://www.scientificamerican.com/article/the-semantic-web/

Adds to Web standards and prac*ces (currently only for documents and services) encouraging •  Unambiguous names for things, classes, and

relaHonships •  Well organized and documented in ontologies •  With data expressed using uniform knowledge

representaHon languages (e.g. OWL) •  To enable computaHonally assisted exploitaHon of

informaHon •  That can be easily integrated from different sources

The SemanHc Web in a nutshell

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Some SemanHc Web successes •  In February 2011, the Watson system by IBM made

internaHonal headlines for beaHng the best humans in the quiz show Jeopardy!

•  A significant number of very prominent websites are powered by Seman-c Web technologies, including the New York Times, Thomson Reuters, BBC, and Google's Freebase.

•  The Speech Interpreta-on and Recogni-on Interface Siri launched by Apple in 2011 as an intelligent personal assistant for the new generaHon of IPhone smartphones heavily draws from work on ontologies, knowledge representaHon, and reasoning.

17 h^p://130.108.5.60/faculty/pascal/pub/crc-‐handbook-‐13.pdf

Uniform Resource IdenHfiers (URIs)

•  Two different uses: – Unambiguous name for something – LocaHon of a document

•  Examples: – h^p://example.org/wiki/Main_Page – sp://example.org/resource.txt – mailto:[email protected]

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Resource DescripHon Framework (RDF)

• Resources (= nodes) •  Identified by Unique Resource Identifier (URI)

• Properties (= edges) •  Identified by Unique Resource Identifier (URI) •  Binary relations between 2 resources

20 h^p://elmonline.ca/sw/sparql/social.^l

<h^p://www.linkedin.com/in/mcourtot> a foaf:Person ; foaf:name "Melanie Courtot" ; foaf:knows <h^p://elmonline.ca/luke> ; foaf:knows <h^p://www.linkedin.com/pub/mark-‐wilkinson/1/674/665> .

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SPARQL

SELECT ?person WHERE { <h^p://www.linkedin.com/in/mcourtot> <h^p://xmlns.com/foaf/0.1/knows> ?person . } -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐ | person | ========================================================== | h^p://www.linkedin.com/pub/mark-‐wilkinson/1/674/665 | | <h^p://elmonline.ca/luke> | -‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐-‐

•  An excellent tutorial by Luke McCarthy: h^p://elmonline.ca/sw/sparql/

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A query language for RDF

The Web Ontology Language (OWL)

•  Knowledge representaHon language •  Based on DescripHon Logics: fragments of

First-‐Order logics with decidable and defined computaHonal properHes

•  Sound, complete, terminaHng reasoners available

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Overview

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•  Seman-c Web –  Standards: URIs, RDF, SPARQL, OWL –  Linked data




Linked open data cloud

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Biological resources in LOD

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Examples of issues in linking data incorrectly

•  h^p://dbpedia.org/resource/Welsh OWL:sameAs <h^p://sw.cyc.com/2006/07/27/cyc/EthnicGroupOfWelsh> <h^p://sw.cyc.com/2006/07/27/cyc/Welsh-‐TheWord> <h^p://sw.cyc.com/2006/07/27/cyc/WelshLanguage> <h^p://sw.cyc.com/2006/07/27/cyc/Welshing-‐Chea-ng>

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Overview

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•  Ontologies –  Defini-on and reasoning –  OBO Foundry –  Example of exisHng ontologies –  Pharmacovigilance –  Publishing ontologies on the SemanHc Web



Ontologies •  RepresentaHon of important things in a specific domain

–  Describes types of enHHes (e.g. cells) and relaHons between them (e.g. prokaryoHc cells and eukaryoHc cells are cells) and their instances (e.g. the specific cells in my sample)

•  An acHve computaHonal arHfact –  A mathemaHcal model based on a subset of first order logic –  Tools can automaHcally process ontologies

•  A communicaHon tool –  Provides a dicHonary for collaborators, a shared understanding –  Allows data sharing

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Reasoning is criHcal •  ProkaryoHc and EukaryoHc

cell are declared disjoints •  Fungal cell is a EukaryoHc

cell •  Spore is a Fungal cell and a

ProkaryoHc cell ⇒  InsaHsfiability ⇒  SoluHon: clarify spore

(sensu Mycetozoa) AND acHnomycete-‐type spore

h^p://www.plosone.org/arHcle/info:doi/10.1371/journal.pone.0022006 30

Logics

•  Simple example based on h^p://arxiv.org/pdf/1201.4089v1.pdf

•  Ontology file available from h^p://www.sfu.ca/~mcourtot/course/20141112BigDataSemWebOntologies/ontology.owl

•  ManipulaHon done using Protégé: h^p://protege.stanford.edu

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Family ontology

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Logics of a grandfather

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Reasoning

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Inferred class hierarchy

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Explana-ons

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A wrong asser-on

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Unsa-sfiability

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Overview

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•  Ontologies –  DefiniHon and reasoning –  OBO Foundry –  Example of exis-ng ontologies –  Pharmacovigilance –  Publishing ontologies on the SemanHc Web



OBO Foundry

A subset of biological and biomedical ontologies whose developers have agreed in advance to accept a common set of principles reflecHng best pracHce in ontology development designed to ensure

•  Hght connecHon to the biomedical basic sciences •  CompaHbility

•  interoperability, common relaHons •  formal robustness •  support for logic-‐based reasoning

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41 hAp://www.obofoundry.org

RELATION TO TIME

GRANULARITY

CONTINUANT OCCURRENT

INDEPENDENT DEPENDENT

ORGAN AND ORGANISM

Organism (NCBI

Taxonomy?)

Anatomical Entity (FMA, CARO)

Organ Function

(FMP, CPRO) Phenotypic Quality (PaTO)

Organism-‐‑Level Process (GO)

CELL AND CELLULAR

COMPONENT

Cell (CL)

Cellular Component (FMA, GO)

Cellular Function (GO)

Cellular Process (GO)

MOLECULE Molecule (ChEBI, SO, RnaO, PrO)

Molecular Function (GO)

Molecular Process (GO)

Slide credit: Barry Smith

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Minimum InformaHon to Reuse an External Ontology Term

•  OBO and SemaHc Web promote reuse of resources

•  Biological resources (e.g., FMA for anatomy), taken together, are too big for current tool support.

•  MIREOT used across the OBO library – OBI: 400 mireoted terms (140 GO, 55 ChEBI, 50 PATO) –  PR (Protein Ontology): 23,000 mireoted terms

•  h^p://ontofox.hegroup.org

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Example of OBO ontologies

•  OBI, Ontology for Biomedical invesHgaHons •  VO, the vaccine ontology •  AERO, the Adverse Event ReporHng Ontology

Ontology for Biomedical InvesHgaHons (OBI)

•  OBI is a mulH-‐community project driven by the pracHcal needs of its members with the goal to build a high quality, interoperable reference ontology

•  OBI high level classes are in place -‐ solidified over several years -‐ that cover all aspects of biomedical invesHgaHons

•  OBI is expanded to enable member applicaHons and based on term requests

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High level class hierarchy (parHal)

Slide credit: OBI Consor)um

Slide credit: Alan Ru=enberg 47

48 Slide credit: OBI Consor)um

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RepresenHng vaccine data – the Vaccine Ontology (VO)

Picture credit: Yongqun He

Overview

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RepresenHng pharmacovigilance data

•  The Adverse Event ReporHng Ontology (AERO)

•  Encodes exisHng clinical guidelines (Brighton CollaboraHon)

'found to exhibit' some 'generalized urticaria or generalized erythema finding''found to exhibit' some 'measured hypotension finding'

inferred to be of typeinferred to be of type

major dermatological criterion for anaphylaxis according to Brighton

major cardiovascular criterionfor anaphylaxis according to Brighton

Level 1 of certainty of anaphylaxis according to Brighton

has component has component

Patient examination

has specified outputhas participant

exam report of June 7has specified input

finding of rashPatient

rash

dermatologicalsystem

Medicallyrelevant entity

Anatomical system

Clinical Finding

about mre

Clinical Report

part oflocated in

Clinician

involves

has participant

is aboutfound to exhibit

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Background and problem statement

•  Surveillance of Adverse Events Following Immuniza-on is important –  DetecHon of issues with vaccine –  Importance of vaccine-‐risk communicaHon

•  Analysis of AE reports is a subjec-ve, -me-‐ and money costly process – Manual review of the textual reports

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Workflow •  Hypothesis: Use the AERO I developed to annotate

and classify a dataset •  VAERS dataset

– Vaccine Adverse Event ReporHng System – 6032 reports: ~5800 negaHve, ~230 posiHve – Post H1N1 immunizaHon 2009/2010 – Manually classified for anaphylaxis

•  MedDRA (Medical DicHonary of Regulatory AcHviHes) is used to represent clinical findings

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Automated Diagnosis workflow

ADVERSE EVENT REPORTING ONTOLOGY

(AERO)

OWL/RDFEXPORT

VAERS DATASET

MySQL

BRIGHTON ANNOTATIONS

ASCII files MySQL

~800 MedDRA terms mapped to 32 Brighton terms

REASONER

?

MANUALLY CURATEDDATASET

A

B

C

D

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Results

ADVERSE EVENT REPORTING ONTOLOGY

(AERO)

OWL/RDFEXPORT

VAERS DATASET

MySQL

BRIGHTON ANNOTATIONS

ASCII files MySQL

~800 MedDRA terms mapped to 32 Brighton terms

REASONER

?

MANUALLY CURATEDDATASET

A

B

C

D

At best cut-‐off point: Sensi-vity 57% Specificity 97%

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AE classificaHon can be improved through the use of ontologies

•  Manual analysis: 3 months for 12 medical officers •  Ontology-‐based analysis: once data collected (2 months), almost

instantaneous (2h on laptop) => Could allow for earlier detecHon of safety issues and be^er understanding of adverse events

November 2009 December 2009 January 2010

Time gain

Ability to detect signal

Time

6000reports

Manual analysisOntology-based

analysis

Legend

2h automated vs.

3 months manual

h^p://dx.doi.org/10.1371/journal.pone.0092632

Overview

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•  Ontologies –  DefiniHon and reasoning –  OBO Foundry –  Example of exisHng ontologies –  Pharmacovigilance –  Publishing ontologies on the Seman-c Web



IRI dereferencing

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Ontobee: publishing biomedical resources on the SemanHc Web

HTML for humans …

… RDF for machines

Ontobee: publishing biomedical resources on the SemanHc Web

Overview

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•  IRIDA –  The IRIDA plaborm –  Adding standards to IRIDA


The Integrated Rapid InfecHous Disease Analysis (IRIDA) project

•  Goal: automate infecHous disease outbreak detecHon and invesHgaHon

•  Issues: –  Integrate WGS, clinical and lab info –  Provide relevant tools and validate pipeline

•  Methods: – Data standards for informaHon exchange – Analysis pipeline (Galaxy based) – User interface – AddiHonal tools:

•  IslandViewer •  GenGIS

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Building the IRIDA data standards

•  Interview with key personnel at BCCDC •  Review of exisHng resources •  IdenHfy “holes”, i.e., missing bits •  Collect exisHng data •  Liaise with implementaHon team •  Generate cohesive resource •  Validate

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Relevant data standards •  TypON, the typing ontology •  OBI, the ontology for Biomedical InvesHgaHons •  NGSOnto, Next GeneraHon Sequencing Ontology •  NIAIS-‐GS-‐BRC core metadata •  TRANS, Pathogen Transmission ontology •  ExO, Exposure Ontology •  EPO, Epidemiology Ontology •  IDO, InfecHous Disease Ontology •  Food: USDA, EFSA?

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Relevant internaHonal efforts

•  MIxS standard •  Global Microbial IdenHfier •  Global Alliance for Genomics and Health •  NCBI BioSample •  European NucleoHde Archive •  …

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Remaining challenges

•  Trust, provenance – Ability to track origin of data to assess whether it is trustworthy

•  Data sharing, reuse, policy – Social and legal issues in ge�ng access to data

•  ConfidenHality – Privacy concerns when linking data

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Overview

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Take home message

Big data is a big challenge, but we can deal with it if done properly: that will be your responsibility DO NOT build a black box DO annotate and describe your data DO make your data openly available

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Acknowledgements

•  Drs. Fiona Brinkman, Will Hsiao, Ryan Brinkman •  The Brinkman^2 labs •  Alan Ru^enberg, Barry Smith, Chris Mungall &

OBO •  Colleagues at Public Health Agency Canada (Ms

Lafleche, Dr Law) •  The IRIDA consorHum and the IRIDA ontology

working group (Emma Griffiths and Damion Dooley)

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Mélanie Courtot, PhD [email protected]

@mcourtot h^p://purl.org/net/mcourtot

20141112 courtot big_datasemwebontologies

Health & Medicine