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
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