Semantic web at Novartis

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

2
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 use3
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
4
Context
Activities of TMS:
§  Text mining
§  Ontology development
§  Ontology provision
§  Data curation

5
Topics

Metastore: a central repository for ontologies
6
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
7
Approximately >2M accesses per month
March 2013

Metastore data model
8

Metastore technology I
9

Metastore technology II
10
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
D
A
T
A
-
S
e
r
v
i
c
e
s
RDF/XML
files

Metastore Widgets (suggest example)
11

Metastore applications (Metastore viewer: summary)
12

Metastore applications (Metastore viewer: links)
13

Metastore applications (Metastore viewer: explorer)
14

15
Topics
-  Query federation
-  Visualization/interaction
-  Other projects

Query federation: why and how
16
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
17
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
18
Assays
UNIPROT
Metastore

Federated queries: logical model
19

RDF virtualization via OnTop
20

21
Topics
-  Other projects

Visualization: why and how
22
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
23
§  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
24

Interaction: query builder + suggest
25

Interaction: path suggestions
26
Assisted query formulation

Visulization and graph navigation
27
Detail, Augmentation, Filtering, query re-formulation

Exploration, layouts, graphic clues
28
Detail, Augmentation, Filtering, query re-formulation

Multiple exports, sharing
29
§  “queries” can be saved and shared
as files or links
§  Query history
§  Download of partial or total datasets

30
Topics
-  Other projects

31
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:someValuesFrom rdf:resource="http://purl.obolibrary.org/obo/HP_0001629"/>
</owl:Restriction>
<owl:Restriction>
</owl:Restriction>
…
What systems can understand:
HP_0001636 hasPart HP_0001629

32
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: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:Restriction>
<owl:Restriction>
</owl:Restriction>
What systems can understand:
HP_0001636 hasPart HP_0001629
Imports closure
Classification
Extraction

33
Topics
-  Other projects

CTMF: Collaborative Terminology Management
34
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)
35

CTMF: integration in applications
36

CTMF: term status page and discussion
37

CTMF: process (use of temporary ID)
38

Under the hood
39
§  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.

40
Topics

41
Topics
-  Other projects

Semantic Web in Real Life: Open questions
42
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.

43
Industry (or real life) is big
§ Areas that look nearby on paper may be very distant
organization-wise.
•  Bench-to-bedside data integration

44
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

45
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 ?

46
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
47
Semantic Web technologies
experiences in Novartis

Semantic web at Novartis

Recommended

Recommended

More Related Content

Similar to Semantic web at Novartis

Similar to Semantic web at Novartis (20)

More from Novartis Institutes for BioMedical Research

More from Novartis Institutes for BioMedical Research (6)

Recently uploaded

Recently uploaded (20)

Semantic web at Novartis