Semantic web at Novartis

Experiences in Novartis Andrea Splendiani, Sr Scientific KE Consultant Geneve, Dec 2nd 2015

Semantic Web @Novartis


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Topics

§ Semantic Web @Novartis • Context (Where in Novartis) • Semantic Web in production • Semantic Web in research • Semantic Web under the hood

§ Semantic Web in “Real Life”: open questions

| Semantic Web technologies: experiences in Novartis| Andrea Splendiani | 2nd December 2015| Technology | Public use

Semantic Web uptake in time

| Semantic Web technologies: experiences in Novartis| Andrea Splendiani | 2nd December 2015| Technology | Public use 3

Context

Metastore/RDF

prep. production

“Semantic Web in pubmed” preparation

prep

Query federation

Visualisation

Other semantic technologies

CTMF p. p.

Semantic Web usage within the organization

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Context

Activities of TMS:

§  Text mining

§  Ontology development

§  Ontology provision

§  Data curation



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Topics




Metastore: a central repository for ontologies

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Semantic Web in production: Metastore

§  Consists of a semantic data federation layer based on controlled terminologies extracted from scientific data repositories

§  Organized around scientific concepts: Genes, Proteins, Indications, Anatomy etc…; some hierarchically organized and classified

§  Complemented by referential knowledge (cross references to internal and external knowledge repositories)

§  Supports different use cases, including text mining, data curation, data integration, search

§  Accessible through SPARQL endpoint, dedicated service layer and reusable widgets; full integrated application (MS Viewer) released to visualize all Metastore content.

§  Based on an RDF data model


Metastore: content and usage

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Approximately >2M accesses per month

March 2013


Metastore data model

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Metastore technology I

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Metastore technology II

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Staging Table T_STABLE

RDF Triple store

Materialized Views

SPARQL end Point Joseki

Relational Tables •  Pointers •  History •  Versions •  Logs •  Reference

tables

Jena

Query SQL and PL/SQL APIs

DATA - Services

RDF/XML files

Metastore Widgets (suggest example)

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Metastore applications (Metastore viewer: summary)

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Metastore applications (Metastore viewer: links)

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Metastore applications (Metastore viewer: explorer)

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Topics

§ Semantic Web @Novartis • Context (Where in Novartis) • Semantic Web in production • Semantic Web in research

-  Query federation -  Visualization/interaction -  Other projects

• Semantic Web under the hood



Query federation: why and how

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Semantic Web in Research: query federation

•  Internal and external data already in RDF

•  Large datasets in relational systems

•  Proprietary datasets with license restrictions (e.g.: one server only)

•  Relational 2 RDF mapping (materialised and virtualised)

•  Bridge ontologies (work in progress)

•  Distributed queries (service)

Why ? How ?


Data and systems architecture: example

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Different arrangements possible (with caveats)

Export!triplest !

SERVICE!Dynamic translation!

Persist triples!

Ontop!SPARQL End Point!

NIBR!Data

Warehouse!!

Ontop!API!

Assay Repository!

RDBMS!

Allegrograph!!

Triplestore & End point!

UNIPROT/EBI SPARQL End

Point!METASTORE!

Oracle Spatial & graphs!

R2RML!+ reasoning!

Metastore!


Federated query example

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Assays

UNIPROT

Metastore


Federated queries: logical model

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RDF virtualization via OnTop

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Topics






Visualization: why and how

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Semantic Web in research: visulization and interaction

•  Accessibility of RDF data by end users

•  Complexity (or unfamiliarity) with SPARQL

•  General lack of knowledge on the structure of data, at query time

•  Visual, interactive environment

•  Pre-configuration to optimize interaction styles

•  Combination of tools and exploration paradigms

•  Data access through SPARQL endpoints

Why ? How ?


RDF data explorer configuration

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§  Visualisation features are tuned to the datasets via a semi-automatic configuration.

§  Structure discovery:

•  ontology

•  queries

•  sampling

•  manual specification/overriding

§  Manual tuning of the ontology and other interaction parameters


Data overview

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Interaction: query builder + suggest

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Interaction: path suggestions

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Assisted query formulation


Visulization and graph navigation

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Detail, Augmentation, Filtering, query re-formulation


Exploration, layouts, graphic clues

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Detail, Augmentation, Filtering, query re-formulation


Multiple exports, sharing

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§  “queries” can be saved and shared as files or links

§  Query history

§  Download of partial or total datasets



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Topics






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Example: provision of “phenotype ontologies” Semantic Web in Research: other projects


<owl:Class rdf:about="http://purl.obolibrary.org/obo/HP_0001636"> <rdfs:label rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Tetralogy of Fallot</rdfs:label> <owl:equivalentClass> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000051"/> <owl:someValuesFrom> <owl:Class> <owl:intersectionOf rdf:parseType="Collection"> <rdf:Description rdf:about="http://purl.obolibrary.org/obo/PATO_0000001"/> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000051"/> <owl:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/HP_0001629"/> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000051"/> <owl:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/HP_0001642"/> </owl:Restriction>

…

What systems can understand: HP_0001636 hasPart HP_0001629

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Example: provision of “phenotype ontologies” Semantic Web in Research: other projects


<owl:Class rdf:about="http://purl.obolibrary.org/obo/HP_0001636"> <rdfs:label rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Tetralogy of Fallot</rdfs:label> <owl:equivalentClass> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000051"/> <owl:someValuesFrom> <owl:Class> <owl:intersectionOf rdf:parseType="Collection"> <rdf:Description rdf:about="http://purl.obolibrary.org/obo/PATO_0000001"/> <owl:Restriction> <owl:onProperty rdfresource="http://purl.obolibrary.org/obo/BFO_0000051"/> <owl:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/HP_0001629"/> </owl:Restriction> <owl:Restriction> <owl:onProperty rdf:resource="http://purl.obolibrary.org/obo/BFO_0000051"/> <owl:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/HP_0001642"/> </owl:Restriction>

What systems can understand: HP_0001636 hasPart HP_0001629

Imports closure

Classification

Extraction


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Topics






CTMF: Collaborative Terminology Management

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Semantic web under the hood: CTMF

§ The CTMF is a system designed to allow a distributed “editing of ontologies”.

§ Users can request new “terms” via a web interface or within an application.

§  “Content owners” can “assess” whether the requested terms are new concepts or synonyms (or errors!) and update the ontologies.

§ Resolution is asynchronous and the term request is non-blocking for applications


CTMF web application (new request form)

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CTMF: integration in applications

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CTMF: term status page and discussion

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CTMF: process (use of temporary ID)

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Under the hood

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§  Basic principle of the Semantic Web: identity comes first. •  What “people can talk about” is give an URI, and information is built around it.

§  The CTMF adopts the same approach: •  a “term” request is in itself identifying a concept: what the requestor had in mind at the time of the

request. We give this idea a URI (the term status page) •  Information is built around this request (clarification). •  A “content owner” can assess whether the concept is identical to something already in metastore

(most likely what was requested for was a synonym), or whether a new concept should be introduced.



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Topics






Semantic Web in Real Life: Open questions

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Data trumps everything

§  If there is a choice between better technology to access data, and better data, the latter prevails. • Corollary: interest is often where there is little data, especially in the

public domain.



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Industry (or real life) is big

§ Areas that look nearby on paper may be very distant organization-wise. • Bench-to-bedside data integration



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You don’t know the semantics of your data

§ The semantic expressiveness of RDF may be too much for what is represented in your data. • You don’t always make your data



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Is data integration really a shared goal ?

§ Not all stakeholders have interest in “opening” their data. • When does a data producer gain in making its data more

accessible ?



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Many people are doing SemWeb without knowing it

§  “My project is not based on RDF, it is based on a graph with properties from controlled vocabularies.” • Why not RDF?

-  Too academic -  Need something that works -  URIs are too long


§ Therese Vachon

§ Pierre Parisot

§ Katia Vella

§ Frederic Sutter

§ Daniel Cronenberger

§ Fatma Oezdemir-Zaech

§ Anosha Siripala

§ Olivier Kreim

§ Gilles Hubert

§ Laurentiu Stanculescu

§ Marc Lieber

§ Martin Rezk (OnTop)

§ Andrea Splendiani

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Semantic Web technologies experiences in Novartis


Semantic web at Novartis

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