Semantic Web Technologies for Computational Chemistry Data

Hypercube
ChemicalSemantics, Inc.
Publication and Retrieval of Computational
Chemical-Physics Data via The Semantic Web
Applying the Semantic Web
to Computational Chemistry

HypercubeChemical Semantics, September 20132
What is this all about ?
The principal objective of our enterprise is to create a
testbed for comprehensive exploration of ideas behind
the practical application of the Semantic Web in
computational chemistry.
The aforementioned working testbed (Chemical
Semantics Portal) is initially limited to computational
chemistry and a limited class of users.
In addition, we will focus on the semi-empirical, ab-initio
and density functional (DFT) calculations of quantum
chemistry and their typical results.
The purpose of this talk is to present the ideas of the
Semantic Web and their possible application in
computational chemistry, and to present the working
prototype of the Chemical Semantic Portal.

3Dr Mirek
Sopek
INTRODUCTION
The Basics of Semantic Web

The evolution of the Web
WEB 1.0 - Web of documents
WEB 2.0 - Social, Read/Write Web
WEB 3.0 - Semantic Web = Web of Data
? WEB 4.0 - Intelligent Web ?
* AssumingChristmas1990asitsbeggining(http://en.wikipedia.org/wiki/History_of_the_World_Wide_Web)
The web is only 8287 days* (23 years) old !
Print – 203,800 days
Newspapers – 142,800 days
Radio – 41,200 days
TV – 28,000 days

Web 1.0 – Web of documents
1989-2000 - Web of Hyperlinked documents

Web 2.0 – Social/Read-Write Web
2000-2010 - The Web of Social Networks
and “Wisdom of the Crowds”

Web 3.0 – Semantic Web
2010-2020(?) - Web of Data, Linked Data Web
Link
Link
Link
Link
Link
Link
Link
Link
LinkLink
Resource
Resource
Resource
Resource
Resource
Resource
Resource
Resource
hasPeople
humanResources
hasServices
hasProducts
hasPeople
hasPeople
hasProduct
hasProduct
colleaguecolleague
Organization
HR
Services
Products
People
People
Product
Product

8Dr Mirek
Sopek
What is wrong with
today’s Web?

The WEB is TOO BIG to know
Web 1.0 & 2.0 major issues
The WEB is TOO BIG to know
Social Web dwells in isolated silos
Data Deluge - Scientific data stored in isolated silos
People look at the Web through Google’s Goggles

10Dr Mirek
Sopek
THE SOLUTION:
Semantic Web – Web 3.0

What is Semantic Web ?
The Semantic Web is a Web of data. It is an extension of
the current Web that provides an easier way to find, share,
reuse and combine information.
“The vision of the Semantic Web is to extend principles of the Web
from documents to data.(...) This also means creation of a common
framework that allows data to be shared and reused across
application, enterprise, and community boundaries, to be
processed automatically by tools as well as manually, including
revealing possible new relationships among pieces of data.”
http://www.w3.org/2001/sw/

Foundations of Semantic Web
―Semantic‖ in ―Semantic Web‖ is about MEANING of data, not
about the syntax it is expressed in.
Semantic Web = Web Full of Meaning = Web of meaningful
Data
Semantic Web is about representation of THINGS (OBJECTS
and CONCEPTS) and their properties on the Web, not just about
documents
Semantic Web uses global NAMING scheme to identify
THINGS, not just to address documents
Semantic Web links THINGS with TYPED LINKS, not with ―blind‖
hyperlinks
Semantic Web allows DISCOVERY of new FACTS about
THINGS,not just browsing through pages
* Picture by Roger Sayle (http://pubs.acs.org/doi/abs/10.1021/ci800243w)

Example
COC(=O)[C@H](C1=CC=CC=C1Cl)N2CCC3=C(C2)C=CS3
InChI=1S/C16H16ClNO2S/c1-20-16(19)15(12-4-2-3-5-
13(12)17)18-8-6-14-11(10-18)7-9-21-14/h2-
5,7,9,15H,6,8,10H2,1H3/t15-/m0/s1
InchI (Key)=GKTWGGQPFAXNFI-HNNXBMFYSA-N
“Plavix” (Clopidogrel)
* Based on “Foreign Language Translation of Chemical Nomenclature by Computer” by Roger Sayle (DOI: 10.1021/ci800243w)
http://www.chemspider.com/InChIKey=GKTWGGQPFAXNFI-HNNXBMFYSA-N

How do we represent THINGS on SW
On the Semantic WEB we represent THINGS using elementary UNITS
of data: TRIPLES.
We can create logical and structural relations between elements of the triple, build
taxonomies, vocabularies and classes and finally “reason” on large sets of triples.
The file format we store the triples in — is called RDF.
:H2O gnvc:hasInChIString “1S/H2O/h1H2”
For example:
Subject Predicate Object
Thing Property Value
Resource Description Framework
:hasMolecularMass “18.0153”
“RDF is for THINGS as HTML is for DOCUMENTS”

How do we Identify Things on the Semantic Web
For unambiguous identification of things (objects)on the Web
and their properties, Semantic Web uses URIs — Universal Resource
Identifiers, a generalization of URL i.e. Ordinary Web addresses:
Water
Molecular
Mass “18.0153”
http://www.chemicalsemantics.com/h2o
http://purl.org/chem/ns#MM A number

Chemical Semantics, September 201316 Hypercube
RDF/XML or
Turtle (Terse RDF Triple Language)
1 @prefix cs: <http://ChemicalSemantics.com/chem/dictionary/ns#> .
2 @prefix mol: <http://ChemicalSemantics.com/chem/molecules/simplewater.ttl#> .
3 @prefix xs: <http://www.w3.org/2001/XMLSchema#> .
4 mol:molecule_31 a cs:molecule ;
5 cs:name ―water" ;
6 cs:atom _:atom31_1 ;
9 cs:bond _:bond31_1 ;
10 cs:bond _:bond31_2 .
11 _:atom31_1 cs:atomType cs:O ;
12 cs:x3 "-0.381950"^^xs:double;
13 cs:y3 "0.243825"^^xs:double;
14 cs:z3 "0.000000"^^xs:double.
15 _:atom31_2 cs:atomType cs:H ;
16 cs:x3 "-0.381950"^^xs:double;
17 cs:y3 "1.203825"^^xs:double;
18 cs:z3 "0.000000"^^xs:double.
19 _:atom31_3 cs:atomType cs:H ;
20 cs:x3 "0.523148"^^xs:double;
(.....)
RDF Serialization – preliminary example

Semantic Web allows Discovery
Semantic Web tools for building ―inteligent‖
vocabularies – RDFS (RDFS Schema) and OWL
ontologies allow for simple logical INFERENCES
and discovery of IMPLICIT facts.
For example:
When a user searches for a molecule with
specific properties, it is possible to automatically
provide him with other molecules that belong to
the same ―class‖ of molecules. .

Semantic Web = GGG (Giant Global Graph)
Organization
HR
Services
Products
People
People
Product
Product
hasPeople
humanResources
hasServic
es
hasProducts
hasPeople
hasPeople
hasProduc
t
hasProduc
t
colleaguecolleague
GGG – term coined by Tim Berners Lee in 2007
Ooops… sorry, but it’s BIG 
Semantic Web = GGG (Giant Global Graph)

Core Semantic Web Technologies
RDF — ResourceDescriptionFramework
RDFa— RDF ―inattributes‖
RDFS— ResourceDescriptionFrameworkSchema Language
OWL — OntologyWeb Language
SPARQL— Semantic Protocol& RDF Query Language
RIF— Rule InterchangeFormat
RDF deals with THINGS
RDFa enablesto embed RDF into ordinaryHTML Web Pages
RDFS deals with SETS and CLASSES of THINGS
OWL  deals with intelligentVOCABULARIES(withlogical relationsbetween
concepts)
SPARQL allows for searchingthroughgraphsof triples storedin ―triple stores‖
RIF allows to expressand interchange generalizedIF...THENconstructs

AAA— Anyonecan say Anythingabout Any Topic.
... and one about Semantic Web Philosophy
OWA— Open WorldAssumption.
We mustassumethatat any time a new piece of informationmay come so
we can’t assumethatwe have ALL the informationat themomentof
informationconsumption.
It also means that not knowing something does not necessarily
imply falsity!
HendlerHypothesis:
“A Little SemanticsGoes A Long Way”

Link Data Four Principles:
• UseWEB ADDRESES (URLs) as namesfor things.
• UseADDRESSES THATWORK ON THE WEB
- sothat peoplecan lookup thosenames.
• Whensomeonelooksup a URL,PROVIDEUSEFUL
INFORMATION,USING THE STANDARDS
(likeRDF).
• IncludeLINKS TO OTHERURLs,so thatthey can
discovermore things.
Hendler Hypothesis in action...
The Semantic Web isn't just about putting data on the web. It is about making links,
so that a person or machine can explore the web of data. With linked data, when you
have some of it, you can find other, related, data. (Tim-Berners Lee)

Ontologies
“An ontology formally represents knowledge as a set of
concepts within a domain, and the relationships between
pairs of concepts. It can be used to model a domain and
support reasoning about concepts.” (Wikipedia)
The fundamental goals of ontologies:
Define concepts used in Semantic graphs (like RDF)
Enable terminological standardisation
Provide tools for building intelligent dictionaries with
synonyms and cross-references
Enable encoding of taxonomies (hierarchical definitions)
Enable reasoning and inferencing – discovering implicit
knowledge

Antoine Lavoisier “Traité élémentaire de chimie”
Early ideas in ontology
"We think only through the medium of words. --
Languages are true analytical methods. (…) The
art of reasoning is nothing more than a language
well arranged.
Thus, while I thought myself employed only in
forming a Nomenclature, and while I proposed
to myself nothing more than to improve the
chemical language, my work transformed itself
by degrees, without my being able to prevent it,
into a treatise upon the Elements of Chemistry.

Nivaldo J. Tro “Chemistry. A Molecular Approach”
Example of Ontology “Hello world”
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix chem: <http://purl.org/chem/simple_classification#> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix foo: <http://example.com/this/> .
## Classes
chem:Matter a rdfs:Class ;
rdfs:label "Matter"@en ;
rdfs:label "Matière"@fr ;
rdfs:label "Materia"@pl .
chem:PureSubstances a rdfs:Class ;
rdfs:label "Pure Substances"@en ;
rdfs:label "Substances Pures"@fr ;
rdfs:label "Substancja"@pl ;
rdfs:subClassOf chem:Matter .
chem:Mixture a rdfs:Class ;
rdfs:label "Mixture"@en ;
rdfs:label "Mélange "@fr ;
rdfs:label "Mieszanina"@pl ;
rdfs:subClassOf chem:Matter .
chem:Heterogeneous a rdfs:Class ;
rdfs:label "Heterogeneous"@en ;
rdfs:label "Hétérogène"@fr ;
rdfs:label "Heterogeniczny"@pl ;
rdfs:subClassOf chem:Mixture .
chem:Homogeneous a rdfs:Class ;
rdfs:label "Homogeneous"@en ;
rdfs:label "Homogène"@fr ;
rdfs:label "Jednorodny"@pl ;
rdfs:subClassOf chem:Mixture .
## Properties
chem:atomicNumber a rdf:Property ;
rdfs:domain chem:Element;
rdfs:range rdfs:Literal .
chem:moleculeName a rdf:Property ;
rdfs:domain chem:Compound;
rdfs:range rdfs:Literal .
chem:componentName a rdf:Property ;
rdfs:domain chem:Mixture ;
rdfs:range chem:Matter .

Non-Trivial Ontologies in Chemistry
ChEBI – Chemical Entities of Biological Interest
http://www.ebi.ac.uk/chebi/
Project of EMBL-EBI
European Bioinformatics Institute (Cambridge) of European
Molecular Biology Lab (Heidelberg)
OBO Foundry Ontology (http://www.obofoundry.org/ )
The Open Biological and Biomedical Ontologies
Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities
focused on „small‟ chemical compounds.
The term „molecular entity‟ refers to any constitutionally or isotopicaly distinct atom, molecule, ion, ion
pair, radical, radical ion, complex, conformer, etc., identifiable as a separately distinguishable entity.
The molecular entities in question are either products of nature or synthetic products used to intervene
in the processes of living organisms.
ChEBI incorporates an ontological classification, whereby the relationships between molecular entities
or classes of entities and their parents and/or children are specified.

Non-Trivial Ontologies in Chemistry
ChemINF – Chemical Information Ontology
https://code.google.com/p/semanticchemistry/
Janna Hastings, Nico Adams, Christoph Steinbeck (EBI)
Leonid Chepelev, Michel Dumontier,
Egon Willighagen, Nico Adams
OBO Foundry Candidate
ChemINF descibes:
• Chemical graphs, and various formats for encoding them.
• Chemical descriptors, with definitions and axioms describing what they are
specifically about.
• Specifications for certain descriptors.
• Algorithms and their software implementations and axioms describing their inputs
and outputs.
• Chemical data representation formalisms and formats.

Chemical Semantics Ontology
http://purl.org/gc/gc.owl
Gainesville Core (alpha edition)
Gainesville Core describes:
• Molecular Publications
• Molecular Systems
• Molecular Calculations
Molecular Systems contain Molecules
• The Molecules may have Residues (for
biopolymers and polymers)
• Molecular Calculations contain Initial Data
and Results
• The Initial Data may have Methods, Basis
Sets, Functionals, etc.
• The Results may have Energies, Wave
Functions and Spectra, etc.
GC aims at complete description of typical
Computational Chemistry experiment

Chemical Semantics Ontology
gc.owl
with
Protege

Related Ontologies ...
SIO – Semanticscience Integrated Ontology
OPB– Ontologyof Physicsfor Biology
RXNO – Name Reaction Ontology
CMO – Chemical Methods Ontology
MOP– Molecular Proocesses Ontology
SO – The Sequence OntologyProject

Importance of Structural Data Structures
CML – Chemical Markup Language
“CMLisnot'justanotherfileformat';itiscapableofholdingextremelycomplexinformation
structuresandsoactingasaninterchangemechanismorforarchival.Itinterfaceseasilywith
moderndatabasearchitecturessuchasrelationaldatabasesorobject-orienteddatabases.
Mostimportantly,italargeamountofgenericXMLsoftwaretoprocessandtransformitis
alreadyavailablefromthecommunity.”
P.Murray-Rust,H.S.Rzepa,2001
CML“pavedtheroad”toSemanticsinChemistry.
Extremelyusefulasaninterchangeformat betweenCCsoftwareandSemanticWeb
Ourposition:ChemicalSemanticswilluseCSX–similarstructuralformatenrichedbyexplicit
descriptionof molecularconstituents,enricheddescriptionofcomputationsinputsandresults
.

A timeline of Semantic Web
RDF–1999
CML-ChemicalMarkupLanguage-1999
FOAF-2000
RDFa-2004
DBPedia–2007
ChEBI-ChemicalEntitiesofBiologicalInterest-2007
GoodRelations(2008,Googleadoption:November2,2010)
Schema.org–June2011
Google’sKnowledgeGraph–May2012
FacebookGraphSearch-January2013

An emerging successor to the web, the
Semantic Web, will likely profoundly
change the very nature of how
scientific knowledge is produced and
shared, in ways that we can now barely
imagine.
Conclusion

33Dr Mirek
Sopek
Chemical Semantics Portal
http://portal.chemicalsemantics.com/cs

CS Portal main targets
Interoperable PUBLISHING of Computational
Chemistrycalculations
FEDERATIONof publisheddata with existing
web-based chemicaldatasets
Cloud-like ARCHIVING of Computational
Chemistrycalulations results, input/output
files etc.

Manualpublication(upload)
Automatedpublicationdirectly from
ModellingSoftware - via Web API

Automatedgeneration of permanent URIs

Permanent Chemical URIs
Automatedgeneration of permanent URIs
http://purl.org/chem/pub/2013-08-04-quercetin
Owned & controlled by
OCLC (Online
Computer Library
Center)
Is claimed to be
persistent and eternal.
Owned by OCLC
controlled by
Chemical
Semantics, Inc.
Generated by Chemical
Semantics, Inc. for the
user. Owned by the user.

URI naming scheme
Publication
http://purl.org/chem/pub/2013-08-05-betacyanin
http://purl.org/chem/pub/2013-08-05-betacyanin/mol-calc
Molecular Calculations
http://purl.org/chem/pub/2013-08-05-betacyanin/molSys
Molecular System
A Molecule of the system
http://purl.org/chem/pub/2013-08-05-betacyanin/molSys/m1
Bonds between atomsin the molecule
http://purl.org/chem/pub/2013-08-05-betacyanin/molSys/m1/a1a12

Dual nature of the URIs
Realizes Linked Data Principles
For Humans(i.e. as seen via web browser)
http://purl.org/chem/pub/2013-08-02-pyridine_base
Returns:

Dual nature of the URIs
Realizes Linked Data Principles
For Machines (i.e. as seen via Semantic Tools (rdfEditor,Fidler))
http://purl.org/chem/pub/2013-08-02-pyridine_base
Returns:
Content-
negotiations:
“Onegets
what one
asksfor”

More on “Human-oriented” views
“Results”– aprototypeforfuturepublication“digest”

“Molecules”– generic,webGLbasedmolecularviewer

“Wavefunction”– visualizationoforbitalenergies

“Graph”–exploretheknowledgestructureaboutyoursystem

“DataFederation”–exploreSemanticLinkstoeternalresources

“Datasets”–useCSPortalforarchivingpurposes

SPARQL queries on CS Portal
CountingnumberoftriplesinthegraphsoftheCSPortal
SELECT ?graph (count(*) as ?count)
WHERE {
GRAPH ?graph { ?s ?p ?o . }
}
group by ?graph
order by DESC(?count)

Countingnumberofelementsinallmolecularsystemson
theCSPortal
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX gc: <http://purl.org/gc/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT
?element (count(*) as ?count)
WHERE {
?atom gc:isElement ?element .
}
GROUP BY ?element
ORDER BY DESC(?count)

Numberofdifferentcalculationsinallmolecularsystemsof
theCSPortal
SELECT
?resultType (count(*) as ?count)
WHERE {
GRAPH ?graph {
?calc rdf:type gc:Calculation ;
gc:hasResult ?result .
?result rdf:type ?resultType .
}
}
group by ?resultType
order by DESC(?count)

NumberofmolecularsystemswithhalogenatomstheCSPortal
SELECT ?graph
WHERE {
GRAPH ?graph { {
?something gc:hasAtom ?atom1 ;
rdf:type ?somethingType ;
rdfs:label ?somethingLabel .
?atom1 gc:isElement "F" .
}
UNION
{
?atom2 gc:isElement "Cl" .
}
UNION
{
?atom3 gc:isElement "Br" .
}
UNION
{

Numberofinorganicmolecularsystems
## Show all molecules that contain atoms other than C,O,N,H
SELECT DISTINCT ?graph
WHERE {
{GRAPH ?graph { ?mol gc:hasAtom ?atom}}
MINUS
{GRAPH ?graph { ?a gc:isElement "C" }}
MINUS
{GRAPH ?graph { ?b gc:isElement "O" }}
MINUS
{GRAPH ?graph { ?b gc:isElement "N" }}
MINUS
{GRAPH ?graph { ?b gc:isElement "H" }}
}

Energyvaluescomputed of allofmolecularsystems
SELECT
?sysEnergy ?energyValue ?energyName
WHERE {
GRAPH ?graph {
?molSys rdf:type gc:MolecularSystem ;
gc:hasCalculationOn ?molCalc .
?molCalc rdf:type gc:Calculation ;
gc:hasResult ?sysEnergy .
?sysEnerg rdf:type gc:SystemEnergies ;
?p ?o .
?o gc:hasFloatValue ?energyValue;
rdfs:label ?energyName.
}
}
ORDER BY ?energyName

Hypercube
Stay tuned ...
If you want to work with us,
or just share your opinions,
Do not hesitate to notify us at:
info@chemicalsemantics.com

Hypercube
Thank you…
Neil Ostlund,
Hypercube, Inc.
1115 NW 4th St. Gainesville,
FL 32608, USA
Phone: (352) 371 7744
Web: www.hyper.com
eMail: ostlund@hyper.com
Mirek Sopek
MakoLab SA
Demokratyczna 46,
93-430 Lodz, Poland
Phone: +48 600 814 537
Web: www.makolab.com
eMail: sopek@makolab.com

Semantic Web Technologies for Computational Chemistry Data

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