Resource description framework technologies in chemistry
Available from
Egon Willighagen's profile on Mendeley.
Page 1
Resource description framework technologies in chemistry
EDITORIAL Open Access
Resource description framework technologies in
chemistry
Egon L Willighagen1* and Martin P Brändle2
Editorial
The Resource Description Framework (RDF) is provid-
ing the life sciences with new standards around data
and knowledge management. The uptake in the life
sciences is significantly higher than the uptake of the
eXtensible Markup Language (XML) and even relational
databases, as was recently shown by Splendiani et al. [1]
Chemistry is adopting these methods too. For example,
Murray-Rust and co-workers used RDF already in 2004
to distribute news items where chemical structures were
embedded using RDF Site Summary 1.0 [2]. Frey imple-
mented a system which would now be referred to as an
electronic lab notebook (ELN) [3]. The use of the
SPARQL query language goes back to 2007 where it
was used in a system to annotate crystal structures [4].
The American Chemical Society (ACS) Division of
Chemical Information (CINF) invited scientists from
around the world to present their use of RDF technolo-
gies in chemistry on 22nd-23rd August 2010 at the
240th ACS National Meeting in Boston, USA. During
three half-day sessions, the speakers demonstrated a mix
of smaller and larger initiatives where RDF and related
technologies are used in cheminformatics and bioinfor-
matics as Open Standards for data exchange, common
languages (ontologies), and problem solving. The fifteen
presentations were grouped in the themes computation,
ontologies, and chemical applications. Figures 1, 2 and 3
display the most important keywords reflecting the
abstracts of the talks in each session as word clouds [5].
The goal of the meeting was to make more chemists
aware of what the RDF Open Standard has to offer to
chemistry. We are delighted to continue this effort with
this Thematic Series, for which the speakers (and
others) were invited to present their work in more detail
to a wider chemistry community. The choice of an
Open Access journal follows this goal. At this place, we
would like to thank Pfizer, Inc., who had partially
funded the article processing charges for this Thematic
Series. Pfizer, Inc. has had no input into the content of
the publication or the articles themselves. All articles in
the series were independently prepared by the authors
and were subjected to the journal’s standard peer review
process.
In the remainder of this editorial, we will briefly out-
line the various RDF technologies and how they have
been used in chemistry so far.
1 Concepts
The core RDF specification was introduced by the
World Wide Web Consortium (W3C) in 1999 [6] and
defines the foundation of the RDF technologies. It has
evolved into a set of recommendations by the W3C
published in 2004 (See Table 1). RDF specifies a very
simple data structure linking a subject to an object or a
value (literal) using a predicate. Cheminformaticians will
recognize this data structure as an edge from graph the-
ory. This structure allows us to represent facts like
“vanillin dissolves in methyl alcohol” [7]. RDF uses Uni-
form Resource Identifiers (URIs) to identify things.
Therefore, the RDF equivalent of the solution statement
could be like this so-called triple:
http://dbpedia.org/resource/Vanillinhttp://example.
com/dissolvesInhttp://dbpedia.org/resource/Methanol.
Since URIs may be used to reference resources on any
server worldwide, RDF triples allow to span a global
graph data structure. This is not surprising, since RDF
is the core technology behind the proposed Semantic
Web [8]. In fact, the Web nature is clear here, as one
can follow both the URIs for vanillin and methanol to
obtain further information on those two chemicals.
These molecules’ URIs are said to be dereferencable,
allowing agents to spider the Web for information fol-
lowing the hyperlinks, quite like how you follow hyper-
links on websites. Hence, the term Semantic Web.
Recent projects such as Bio2RDF [9], Chem2Bio2RDF
[10], and OpenTox [11] have brought genomic, chemical
* Correspondence: egon.willighagen@gmail.com
1Division of Molecular Toxicology, Institute of Environmental Medicine,
Karolinska Institutet, SE-17177 Stockholm, Sweden
Full list of author information is available at the end of the article
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
© 2011 Willighagen and Brändle; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Resource description framework technologies in
chemistry
Egon L Willighagen1* and Martin P Brändle2
Editorial
The Resource Description Framework (RDF) is provid-
ing the life sciences with new standards around data
and knowledge management. The uptake in the life
sciences is significantly higher than the uptake of the
eXtensible Markup Language (XML) and even relational
databases, as was recently shown by Splendiani et al. [1]
Chemistry is adopting these methods too. For example,
Murray-Rust and co-workers used RDF already in 2004
to distribute news items where chemical structures were
embedded using RDF Site Summary 1.0 [2]. Frey imple-
mented a system which would now be referred to as an
electronic lab notebook (ELN) [3]. The use of the
SPARQL query language goes back to 2007 where it
was used in a system to annotate crystal structures [4].
The American Chemical Society (ACS) Division of
Chemical Information (CINF) invited scientists from
around the world to present their use of RDF technolo-
gies in chemistry on 22nd-23rd August 2010 at the
240th ACS National Meeting in Boston, USA. During
three half-day sessions, the speakers demonstrated a mix
of smaller and larger initiatives where RDF and related
technologies are used in cheminformatics and bioinfor-
matics as Open Standards for data exchange, common
languages (ontologies), and problem solving. The fifteen
presentations were grouped in the themes computation,
ontologies, and chemical applications. Figures 1, 2 and 3
display the most important keywords reflecting the
abstracts of the talks in each session as word clouds [5].
The goal of the meeting was to make more chemists
aware of what the RDF Open Standard has to offer to
chemistry. We are delighted to continue this effort with
this Thematic Series, for which the speakers (and
others) were invited to present their work in more detail
to a wider chemistry community. The choice of an
Open Access journal follows this goal. At this place, we
would like to thank Pfizer, Inc., who had partially
funded the article processing charges for this Thematic
Series. Pfizer, Inc. has had no input into the content of
the publication or the articles themselves. All articles in
the series were independently prepared by the authors
and were subjected to the journal’s standard peer review
process.
In the remainder of this editorial, we will briefly out-
line the various RDF technologies and how they have
been used in chemistry so far.
1 Concepts
The core RDF specification was introduced by the
World Wide Web Consortium (W3C) in 1999 [6] and
defines the foundation of the RDF technologies. It has
evolved into a set of recommendations by the W3C
published in 2004 (See Table 1). RDF specifies a very
simple data structure linking a subject to an object or a
value (literal) using a predicate. Cheminformaticians will
recognize this data structure as an edge from graph the-
ory. This structure allows us to represent facts like
“vanillin dissolves in methyl alcohol” [7]. RDF uses Uni-
form Resource Identifiers (URIs) to identify things.
Therefore, the RDF equivalent of the solution statement
could be like this so-called triple:
http://dbpedia.org/resource/Vanillinhttp://example.
com/dissolvesInhttp://dbpedia.org/resource/Methanol.
Since URIs may be used to reference resources on any
server worldwide, RDF triples allow to span a global
graph data structure. This is not surprising, since RDF
is the core technology behind the proposed Semantic
Web [8]. In fact, the Web nature is clear here, as one
can follow both the URIs for vanillin and methanol to
obtain further information on those two chemicals.
These molecules’ URIs are said to be dereferencable,
allowing agents to spider the Web for information fol-
lowing the hyperlinks, quite like how you follow hyper-
links on websites. Hence, the term Semantic Web.
Recent projects such as Bio2RDF [9], Chem2Bio2RDF
[10], and OpenTox [11] have brought genomic, chemical
* Correspondence: egon.willighagen@gmail.com
1Division of Molecular Toxicology, Institute of Environmental Medicine,
Karolinska Institutet, SE-17177 Stockholm, Sweden
Full list of author information is available at the end of the article
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
© 2011 Willighagen and Brändle; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Page 2
Figure 1 Keyword cloud for the RDF and Computation session.
Figure 2 Keyword cloud for the RDF and Ontologies session.
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 2 of 6
Figure 2 Keyword cloud for the RDF and Ontologies session.
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 2 of 6
Page 3
and pharmaceutical knowledge to the Semantic Web by
expressing it in RDF. These three projects aim at mak-
ing databases with chemical knowledge available from a
central access point, interlinking the individual data sets.
Smaller data sets are also becoming available as RDF,
such as the Open Notebook Science Solubility data [12].
2 Formats
The actual use of RDF depends on various further stan-
dards. For example, standards were required that
describe how RDF statements are exchanged. Several
standards serve this purpose: RDF/XML is an XML-
based serialization [13], while simpler formats exist with
N-Triples [14] and Notation3 [15]. For integration with
current web practices, RDFa has been defined to allow
RDF triples to be embedded in HTML pages [16]. Addi-
tionally, a proposal has been written that describes how
RDF can be serialized as Javascript Object Notation
(JSON) [17], and while this is not a formal specification
yet, a new RDF working group will formalize this into a
new standard [18]. Several of these serialization stan-
dards are used in the papers in this Series.
Using these serializations, RDF can be downloaded
directly from pure RDF documents (RDF/XML, Nota-
tion3), or extracted from RDFa-based web pages using
online RDF extraction web services, like http://www.w3.
org/2007/08/pyRdfa/. These approaches make it simple
to aggregate chemical data from web pages.
3 Querying the World Wide Web
The most promising technology in the RDF family is the
SPARQL Protocol and RDF Query Language (SPARQL)
[19], which has been applied by Chen et al. in three che-
mogenomics use cases [10]. One of the use cases shows
how SPARQL queries are used to find compounds that
are active in bioassay for genes related to proteins to
which the chemical dexamethasone binds, using infor-
mation from PubChem, Uniprot, and DrugBank, all
made available as RDF in the Chem2Bio2RDF database.
The other use cases in this paper use the same approach
by aggregating data sources before querying them. As
such, it is similar to querying data stored in a relational
database. However, an important difference between
SPARQL and SQL query engines is the underlying data
they act on: a graph of triples for RDF data, and rectan-
gular tables in relational databases. This difference
implies that RDF resources must have at least some
common elements, whereas a relational DBMS assumes
an identical data structure for all records of a table.
Figure 3 Keyword cloud for the RDF and Chemical Applications session.
Table 1 Key W3C Specifications
Year Technology Description
1999 RDF Resource Description Framework (RDF) Model and
Syntax Specification [6]
2004 RDF/XML RDF/XML Syntax Specification (Revised) [13]
RDF Resource Description Framework (RDF): Concepts
and Abstract Syntax [32]
OWL OWL Web Ontology Language Overview [33]
2007 OWL2 OWL 2 Web Ontology Language Document
Overview [33]
2008 RDFa RDFa in XHTML: Syntax and Processing [16]
SPARQL SPARQL Query Language for RDF [19]
Several of the key specifications and when they were recommended by the
World Wide Web Consortium.
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 3 of 6
expressing it in RDF. These three projects aim at mak-
ing databases with chemical knowledge available from a
central access point, interlinking the individual data sets.
Smaller data sets are also becoming available as RDF,
such as the Open Notebook Science Solubility data [12].
2 Formats
The actual use of RDF depends on various further stan-
dards. For example, standards were required that
describe how RDF statements are exchanged. Several
standards serve this purpose: RDF/XML is an XML-
based serialization [13], while simpler formats exist with
N-Triples [14] and Notation3 [15]. For integration with
current web practices, RDFa has been defined to allow
RDF triples to be embedded in HTML pages [16]. Addi-
tionally, a proposal has been written that describes how
RDF can be serialized as Javascript Object Notation
(JSON) [17], and while this is not a formal specification
yet, a new RDF working group will formalize this into a
new standard [18]. Several of these serialization stan-
dards are used in the papers in this Series.
Using these serializations, RDF can be downloaded
directly from pure RDF documents (RDF/XML, Nota-
tion3), or extracted from RDFa-based web pages using
online RDF extraction web services, like http://www.w3.
org/2007/08/pyRdfa/. These approaches make it simple
to aggregate chemical data from web pages.
3 Querying the World Wide Web
The most promising technology in the RDF family is the
SPARQL Protocol and RDF Query Language (SPARQL)
[19], which has been applied by Chen et al. in three che-
mogenomics use cases [10]. One of the use cases shows
how SPARQL queries are used to find compounds that
are active in bioassay for genes related to proteins to
which the chemical dexamethasone binds, using infor-
mation from PubChem, Uniprot, and DrugBank, all
made available as RDF in the Chem2Bio2RDF database.
The other use cases in this paper use the same approach
by aggregating data sources before querying them. As
such, it is similar to querying data stored in a relational
database. However, an important difference between
SPARQL and SQL query engines is the underlying data
they act on: a graph of triples for RDF data, and rectan-
gular tables in relational databases. This difference
implies that RDF resources must have at least some
common elements, whereas a relational DBMS assumes
an identical data structure for all records of a table.
Figure 3 Keyword cloud for the RDF and Chemical Applications session.
Table 1 Key W3C Specifications
Year Technology Description
1999 RDF Resource Description Framework (RDF) Model and
Syntax Specification [6]
2004 RDF/XML RDF/XML Syntax Specification (Revised) [13]
RDF Resource Description Framework (RDF): Concepts
and Abstract Syntax [32]
OWL OWL Web Ontology Language Overview [33]
2007 OWL2 OWL 2 Web Ontology Language Document
Overview [33]
2008 RDFa RDFa in XHTML: Syntax and Processing [16]
SPARQL SPARQL Query Language for RDF [19]
Several of the key specifications and when they were recommended by the
World Wide Web Consortium.
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 3 of 6
Page 4
For example, Jankowski used a public SPARQL service
to extract boiling points of a series of alkanes from an
XHTML webpage with the data made machine readable
with RDFa, and visualized that using Javascript in another
web page dynamically [20] (see Figure 4). A second impor-
tant difference is that SPARQL queries can be federated
[21]. Federated SPARQL allows one to query various RDF
providers in one query. This has been used recently in the
Receptor Explorer tool to help translational research by
connecting basic neuroscience research with clinical trials
[22]. Being able to query resources in this manner, brings
us a step closer to systems biology approaches.
4 Ontologies
With RDF we have a data structure to link resources
and provide details about those resources, and SPARQL
provides us with the tools to query and aggregate that
data. The next standard we will discuss now is the Web
Ontology Language (OWL) which brought the RDF
technology to the ontology community [23]. Ontologies
are most certainly not new to chemistry [24] nor biology
or life sciences, but the OWL standard makes it much
easier to use ontologies, partly because they are formu-
lated in RDF themselves. Ontologies, like controlled
vocabularies and thesauri, describe what things mean,
by linking terms to a human-readable definition. As
such, ontologies are used for sharing knowledge in a
common language, as well as to organize that knowl-
edge. While linking resources is not new either, expres-
sing the content of resources in explicit terms allows
humans and software to reason formally on the content
and to find possible sources of error. For example,
Figure 4 Web page using SPARQL to visualize alkane boiling points extracted from another web page. Web page with JavaScript by
Jankowski visualizing the boiling point of a series of alkanes from Wiener [31] extracted with SPARQL from a second, XHTML+RDFa web page at
http://egonw.github.com/cheminformatics.classics/classic1.html
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 4 of 6
to extract boiling points of a series of alkanes from an
XHTML webpage with the data made machine readable
with RDFa, and visualized that using Javascript in another
web page dynamically [20] (see Figure 4). A second impor-
tant difference is that SPARQL queries can be federated
[21]. Federated SPARQL allows one to query various RDF
providers in one query. This has been used recently in the
Receptor Explorer tool to help translational research by
connecting basic neuroscience research with clinical trials
[22]. Being able to query resources in this manner, brings
us a step closer to systems biology approaches.
4 Ontologies
With RDF we have a data structure to link resources
and provide details about those resources, and SPARQL
provides us with the tools to query and aggregate that
data. The next standard we will discuss now is the Web
Ontology Language (OWL) which brought the RDF
technology to the ontology community [23]. Ontologies
are most certainly not new to chemistry [24] nor biology
or life sciences, but the OWL standard makes it much
easier to use ontologies, partly because they are formu-
lated in RDF themselves. Ontologies, like controlled
vocabularies and thesauri, describe what things mean,
by linking terms to a human-readable definition. As
such, ontologies are used for sharing knowledge in a
common language, as well as to organize that knowl-
edge. While linking resources is not new either, expres-
sing the content of resources in explicit terms allows
humans and software to reason formally on the content
and to find possible sources of error. For example,
Figure 4 Web page using SPARQL to visualize alkane boiling points extracted from another web page. Web page with JavaScript by
Jankowski visualizing the boiling point of a series of alkanes from Wiener [31] extracted with SPARQL from a second, XHTML+RDFa web page at
http://egonw.github.com/cheminformatics.classics/classic1.html
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 4 of 6
Page 5
Konyk et al. have used OWL to link PubChem, Drug-
Bank, and DBPedia, noting that it offers new ways to
discover knowledge [25].
There are currently not many ontologies in chemistry,
but many OBO Foundry-based ontologies can be reused
using an OBO to OWL mapping [26]. This makes avail-
able chemical ontologies like the CO ontology [27], the
ontology of Chemical Entities of Biological Interest
(ChEBI) [28,29], and the Chemical Information Ontol-
ogy http://code.google.com/p/semanticchemistry/, but
also other ontologies in the life sciences, such as the
Gene Ontology [30]. This way, OWL provides a univer-
sal standard to link data sources in life sciences, trans-
cending traditional boundaries between the various
domains.
The current state is that different RDF resources are
using different ontologies. This does not necessarily
have to be a problem, because the ontologies can be
explicitly mapped to each other. This way, equivalent
terms from two ontologies can be formally defined as
equivalent, using the OWL predicates owl:equivalent-
Class and owl:equivalentProperty for classes, and owl:
sameAs for instance. Making the equivalence explicit
this way helps to illustrate the provenance of data inte-
gration efforts.
5 Discussion
This Thematic Series shows the current state of the use
of RDF in chemistry, as presented at the ACS RDF 2010
meeting in Boston, and provides an insight into the pro-
gress of these methods. Much of the research is cur-
rently explorative, rather than formative, though
standards are being proposed. It may very well turn out
that some aspects of chemistry will never be expressed
in RDF, and some computation will be done without
ontology-based reasoning. It is important to realize here
where RDF is positioned, namely for linking resources.
However, the use of RDF for already well-defined data
structures in chemistry is not obvious. Data types like
connection tables and various matrices are possible, but
the use of URIs makes such structures needlessly ver-
bose. Moreover, there is no need to format already well-
formalized data structures into RDF, such as the various
uses of matrices in computational chemistry as RDF tri-
ples. In fact, several papers in this series outline how to
combine knowledge expressed with RDF with computa-
tional services. This shows that RDF is not an isolated
framework, but one that can be integrated into existing
cheminformatics workflows.
What RDF does not solve, are the following issues that
remain in cheminformatics. RDF is about knowledge
representation, and while ontologies take care of mean-
ing and provide requirements to verify formal data con-
sistency, it does not enforce any data quality, data
structure, or data availability. This is, in fact, similar to
other ways of providing data. For example, a data set
with boiling points may or may not include information
about experimental error. Metabolomics data may name
the molecules for which concentration profiles have
been measured, or the original accurate masses from
which the identity was deduced.
It must be clear, therefore, that the RDF technologies
are not the solution to everything. Their use does not
guarantee an impressive scientific scenario. Instead, it
can help simplify data analysis and particularly data
integration, making it easier to handle large volumes of
data accurately, or at least, with an explicitly defined
accuracy.
As such, the use of explicit, semantic formats can be
considered a gold standard of scientific practise. It is
about adding as much detail to your lab notebook as
you need. But, it does not inhibit you from writing non-
sense in your notebook.
6 Outlook
The future of the use of RDF technologies as open stan-
dards in chemistry looks bright, and fills the needs in
chemistry for semantically linking chemical data to
other data sources. RDF technologies provide a domain-
independent way for representing knowledge and their
open nature assures many alternative approaches for
making data available as RDF. This Thematic Series
shows a few novel and creative applications of these
RDF technologies, and we hope they may serve as semi-
nal work in cheminformatics for future years.
Author details
1Division of Molecular Toxicology, Institute of Environmental Medicine,
Karolinska Institutet, SE-17177 Stockholm, Sweden. 2Chemistry Biology
Pharmacy Information Center, ETH Zürich, Wolfgang-Pauli-Str. 10, 8093
Zürich, Switzerland.
Received: 19 April 2011 Accepted: 13 May 2011 Published: 13 May 2011
References
1. Splendiani A, Burger A, Paschke A, Romano P, Marshall M: Biomedical
semantics in the Semantic Web. J Biomed Semantics 2011, 2(Suppl 1):S1.
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XML, and the World Wide Web. 5. Applications of chemical metadata in
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3. Frey JG: Dark Lab or Smart Lab: The Challenges for 21st Century
Laboratory Software. Org Process Res Dev 2004, 8(6):1024-1035.
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Crystallographic Models. J Chem Inf Model 2007, 47(6):2475-2484.
5. Wordle. [http://www.wordle.net/].
6. Lassila O, Swick RR: Resource Description Framework(RDF) Model and
Syntax Specification. 1999 [http://www.w3.org/TR/1999/REC-rdf-syntax-
19990222].
7. Bradley JC, Neylon C, Guha R, Williams A, Hooker B, Lang ASID, Friesen B,
Bohinski T, Bulger D, Federici M, Hale J, Mancinelli J, Mirza KB, Moritz MJ,
Rein D, Tchakounte C, Truong HT: Open Notebook Science Challenge:
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2010 [http://dx.doi.org/10.1038/npre.2010.4243.2].
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 5 of 6
Bank, and DBPedia, noting that it offers new ways to
discover knowledge [25].
There are currently not many ontologies in chemistry,
but many OBO Foundry-based ontologies can be reused
using an OBO to OWL mapping [26]. This makes avail-
able chemical ontologies like the CO ontology [27], the
ontology of Chemical Entities of Biological Interest
(ChEBI) [28,29], and the Chemical Information Ontol-
ogy http://code.google.com/p/semanticchemistry/, but
also other ontologies in the life sciences, such as the
Gene Ontology [30]. This way, OWL provides a univer-
sal standard to link data sources in life sciences, trans-
cending traditional boundaries between the various
domains.
The current state is that different RDF resources are
using different ontologies. This does not necessarily
have to be a problem, because the ontologies can be
explicitly mapped to each other. This way, equivalent
terms from two ontologies can be formally defined as
equivalent, using the OWL predicates owl:equivalent-
Class and owl:equivalentProperty for classes, and owl:
sameAs for instance. Making the equivalence explicit
this way helps to illustrate the provenance of data inte-
gration efforts.
5 Discussion
This Thematic Series shows the current state of the use
of RDF in chemistry, as presented at the ACS RDF 2010
meeting in Boston, and provides an insight into the pro-
gress of these methods. Much of the research is cur-
rently explorative, rather than formative, though
standards are being proposed. It may very well turn out
that some aspects of chemistry will never be expressed
in RDF, and some computation will be done without
ontology-based reasoning. It is important to realize here
where RDF is positioned, namely for linking resources.
However, the use of RDF for already well-defined data
structures in chemistry is not obvious. Data types like
connection tables and various matrices are possible, but
the use of URIs makes such structures needlessly ver-
bose. Moreover, there is no need to format already well-
formalized data structures into RDF, such as the various
uses of matrices in computational chemistry as RDF tri-
ples. In fact, several papers in this series outline how to
combine knowledge expressed with RDF with computa-
tional services. This shows that RDF is not an isolated
framework, but one that can be integrated into existing
cheminformatics workflows.
What RDF does not solve, are the following issues that
remain in cheminformatics. RDF is about knowledge
representation, and while ontologies take care of mean-
ing and provide requirements to verify formal data con-
sistency, it does not enforce any data quality, data
structure, or data availability. This is, in fact, similar to
other ways of providing data. For example, a data set
with boiling points may or may not include information
about experimental error. Metabolomics data may name
the molecules for which concentration profiles have
been measured, or the original accurate masses from
which the identity was deduced.
It must be clear, therefore, that the RDF technologies
are not the solution to everything. Their use does not
guarantee an impressive scientific scenario. Instead, it
can help simplify data analysis and particularly data
integration, making it easier to handle large volumes of
data accurately, or at least, with an explicitly defined
accuracy.
As such, the use of explicit, semantic formats can be
considered a gold standard of scientific practise. It is
about adding as much detail to your lab notebook as
you need. But, it does not inhibit you from writing non-
sense in your notebook.
6 Outlook
The future of the use of RDF technologies as open stan-
dards in chemistry looks bright, and fills the needs in
chemistry for semantically linking chemical data to
other data sources. RDF technologies provide a domain-
independent way for representing knowledge and their
open nature assures many alternative approaches for
making data available as RDF. This Thematic Series
shows a few novel and creative applications of these
RDF technologies, and we hope they may serve as semi-
nal work in cheminformatics for future years.
Author details
1Division of Molecular Toxicology, Institute of Environmental Medicine,
Karolinska Institutet, SE-17177 Stockholm, Sweden. 2Chemistry Biology
Pharmacy Information Center, ETH Zürich, Wolfgang-Pauli-Str. 10, 8093
Zürich, Switzerland.
Received: 19 April 2011 Accepted: 13 May 2011 Published: 13 May 2011
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doi:10.1186/1758-2946-3-15
Cite this article as: Willighagen and Brändle: Resource description
framework technologies in chemistry. Journal of Cheminformatics 2011
3:15.
Open access provides opportunities to our
colleagues in other parts of the globe, by allowing
anyone to view the content free of charge.
Publish with ChemistryCentral and every
scientist can read your work free of charge
W. Jeffery Hurst, The Hershey Company.
available free of charge to the entire scientific community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours you keep the copyright
Submit your manuscript here:
http://www.chemistrycentral.com/manuscript/
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 6 of 6
284(5):34-43.
9. Belleau F, Nolin M, Tourigny N, Rigault P, Morissette J: Bio2RDF: Towards a
mashup to build bioinformatics knowledge systems. J Biomed Inf 2008,
41(5):706-716.
10. Chen B, Dong X, Jiao D, Wang H, Zhu Q, Ding Y, Wild D: Chem2Bio2RDF:
a semantic framework for linking and data mining chemogenomic and
systems chemical biology data. BMC Bioinf 2010, 11:255.
11. Hardy B, Douglas N, Helma C, Rautenberg M, Jeliazkova N, Jeliazkov V,
Nikolova I, Benigni R, Tcheremenskaia O, Kramer S, Girschick T, Buchwald F,
Wicker J, Karwath A, Gutlein M, Maunz A, Sarimveis H, Melagraki G,
Afantitis A, Sopasakis P, Gallagher D, Poroikov V, Filimonov D, Zakharov A,
Lagunin A, Gloriozova T, Novikov S, Skvortsova N, Druzhilovsky D, Chawla S,
Ghosh I, Ray S, Patel H, Escher S: Collaborative development of predictive
toxicology applications. J Cheminf 2010, 2:7.
12. Bradley JC, Guha R, Lang A, Lindenbaum P, Neylon C, Williams A,
Willighagen EL: Beautifying Data in the Real World, Sebastopol, US: O’Reilly
Media, Inc 2009, chap 16.
13. Beckett D: RDF/XML Syntax Specification (Revised). 2004 [http://www.w3.
org/TR/2004/REC-rdf-syntax-grammar-20040210/].
14. Beckett D, Grant J: RDF Test Cases. 2004 [http://www.w3.org/TR/2004/REC-
rdf-testcases-20040210/].
15. Berners-Lee T: Notation 3 - An readable language for data on the Web.
2006 [http://www.w3.org/DesignIssues/Notation3.html].
16. Adida B, Birbeck M, McCarron S, Pemberton S: RDFa in XHTML: Syntax and
Processing. 2008 [http://www.w3.org/TR/2008/REC-rdfa-syntax-20081014/],
W3C.
17. Alexander K: RDF in JSON. Proceedings of the 4th Workshop on Scripting for
the Semantic Web, Tenerife, Spain, June 02, 2008, Volume 368 of CEUR
Workshop Proceedings 2008.
18. Herman I: New RDF Working Group, RDF/JSON, RDF API... 2010 [http://
www.w3.org/QA/2010/12/new_rdf_working_group_rdfjson.html].
19. Prud’hommeaux E, Seaborne A: SPARQL Query Language for RDF. 2008
[http://www.w3.org/TR/2008/REC-rdf-sparql-query-20080115/].
20. Jankowski O: SPARQL to Chart. 2010 [http://www.pharmash.com/posts/
2010-09-27-sparql-to-chart.html].
21. Prud’hommeaux E: Federated SPARQL. 2007 [http://www.w3.org/2007/05/
SPARQLfed/].
22. Cheung KH, Frost HR, Marshall MS, Prud’hommeaux E, Samwald M, Zhao J,
Paschke A: A journey to Semantic Web query federation in the life
sciences. BMC Bioinf 2009, 10(Suppl 10):S10.
23. McGuinness DL, Van Harmelen F: OWL Web Ontology Language
Overview. 2004 [http://www.w3.org/TR/2004/REC-owl-features-20040210/].
24. Gordon JE: Chemical inference. 3. Formalization of the language of
relational chemistry: ontology and algebra. J Chem Inf Comput Sci 1988,
28(2):100-115.
25. Konyk M, De Leon A, Dumontier M: Chemical Knowledge for the
Semantic Web. J Chem Inf Comput Sci 2008, 169-176.
26. Moreira DA, Musen MA: OBO to OWL: a protege OWL tab to read/save
OBO ontologies. Bioinformatics (Oxford, England) 2007, 23(14):1868-1870.
27. Feldman HJ, Dumontier M, Ling S, Haider N, Hogue CWV: CO: A chemical
ontology for identification of functional groups and semantic
comparison of small molecules. FEBS Lett 2005, 579(21):4685-4691.
28. Degtyarenko K, de Matos P, Ennis M, Hastings J, Zbinden M, McNaught A,
Alcántara R, Darsow M, Guedj M, Ashburner M: ChEBI: a database and
ontology for chemical entities of biological interest. Nucleic Acids Res
2008, 36(suppl 1):D344-D350.
29. Hull D: GO faster ChEBI with Reasonable Biochemistry. Nature Precedings
2008 [http://dx.doi.org/10.1038/npre.2008.2435.1].
30. Aranguren M, Bechhofer S, Lord P, Sattler U, Stevens R: Understanding and
using the meaning of statements in a bio-ontology: recasting the Gene
Ontology in OWL. BMC Bioinf 2007, 8:57.
31. Wiener H: Structural Determination of Paraffin Boiling Points. J Am Chem
Soc 1947, 69:17-20.
32. Carroll JJ, Klyne G: Resource Description Framework (RDF): Concepts and
Abstract Syntax. 2004 [http://www.w3.org/TR/2004/REC-rdf-concepts-
20040210/].
33. W3C OWL Working Group: OWL 2 Web Ontology Language Document
Overview. 2009 [http://www.w3.org/TR/2009/REC-owl2-overview-20091027/].
doi:10.1186/1758-2946-3-15
Cite this article as: Willighagen and Brändle: Resource description
framework technologies in chemistry. Journal of Cheminformatics 2011
3:15.
Open access provides opportunities to our
colleagues in other parts of the globe, by allowing
anyone to view the content free of charge.
Publish with ChemistryCentral and every
scientist can read your work free of charge
W. Jeffery Hurst, The Hershey Company.
available free of charge to the entire scientific community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours you keep the copyright
Submit your manuscript here:
http://www.chemistrycentral.com/manuscript/
Willighagen and Brändle Journal of Cheminformatics 2011, 3:15
http://www.jcheminf.com/content/3/1/15
Page 6 of 6
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