=Paper=
{{Paper
|id=Vol-1114/Session4_Brenninkmeijer
|storemode=property
|title=Computing Identity Co-Reference Across Drug Discovery Datasets
|pdfUrl=https://ceur-ws.org/Vol-1114/Session4_Brenninkmeijer.pdf
|volume=Vol-1114
|dblpUrl=https://dblp.org/rec/conf/swat4ls/BrenninkmeijerDGGPS13
}}
==Computing Identity Co-Reference Across Drug Discovery Datasets==
https://ceur-ws.org/Vol-1114/Session4_Brenninkmeijer.pdf
Computing Identity Co-Reference Across Drug
Discovery Datasets
Christian Y A Brenninkmeijer1 , Ian Dunlop1 , Carole Goble1 ,
Alasdair J G Gray2 , Steve Pettifer1 , and Robert Stevens1
1
School of Computer Science, University of Manchester, UK.
2
Department of Computer Science, Heriot-Watt University, UK.
Abstract. This paper presents the rules used within the Open PHACTS
(http://www.openphacts.org) Identity Management Service to com-
pute co-reference chains across multiple datasets. The web of (linked)
data has encouraged a proliferation of identifiers for the concepts cap-
tured in datasets; with each dataset using their own identifier. A key data
integration challenge is linking the co-referent identifiers, i.e. identifying
and linking the equivalent concept in every dataset. Exacerbating this
challenge, the datasets model the data differently, so when is one repre-
sentation truly the same as another? Finally, different users have their
own task and domain specific notions of equivalence that are driven by
their operational knowledge. Consumers of the data need to be able to
choose the notion of operational equivalence to be applied for the con-
text of their application. We highlight the challenges of automatically
computing co-reference and the need for capturing the context of the
equivalence. This context is then used to control the co-reference com-
putation. Ultimately, the context will enable data consumers to decide
which co-references to include in their applications.
1 Introduction
Within the life sciences there has been a proliferation of databases published,
with the 2013 NAR database issue listing over 1,500 [8]. An increasing number
of these are being published as linked data – either directly, e.g. the recent
publication of RDF data by the EBI3 [17] or through projects such as Bio2RDF
[5] – forming a web of linked data. A key data integration challenge is identifying
the “same” concept across these datasets.
While there have been attempts to provide global identifiers for a concept,
e.g. with life sciences identifiers [7], these have not gained widespread use. Conse-
quently, there is no global, or life sciences, identifier scheme used by all datasets
to identify a given concept; each dataset uses their own identifier scheme, leading
to a proliferation of identifiers for (notionally) the same concept [12].
Then there is the problem of what does it mean to be the same; just repre-
senting the truth is not a tenable position. As demonstrated by Halpin et al [15]
3
http://www.ebi.ac.uk/rdf/ accessed November 2013
2 C.Y.A. Brenninkmeijer et al.
there are many interpretations of the owl:sameAs relationships that exist in the
data. Life sciences datasets contain complimentary and overlapping data, mod-
elled with different levels of granularity depending upon the purpose of the data
capture. Therefore, when we refer to the data being about the “same” concept,
we are not looking for identical representations but rather saying that these two
complimentary records can be treated as being operationally equivalent. How-
ever, the notion of operational equivalence depends upon the use to which the
data will be put and thus can only be decided by the user or the application
they use.
Linked data allows a user to navigate their way through the web of data by
following links from one resource to a related resource. Consequently not every
pair of datasets are linked since you can navigate your way through the web
of data. However there are many scenarios where you need to know all of the
equivalent URIs. For example, to power a linked data integration platform such
as the drug discovery platform being developed by the Open PHACTS project
[14]. Co-reference services such as sameas.org [11] and BridgeDb [16] provide a
look-up service for discovering equivalent identifiers. However, they are a melting
pot of equivalence as they do not consider the context of why two things are
equivalent. For example, a search on sameas.org with the URI for the UniProt
record for “Insulin Receptor (homo-sapien)” 4 results in 30,866 equivalent URIs;
the first 1,292 of which are for DBPedia gene entries. Clearly these cannot all
be equivalent, particularly since the UniProt record is a protein and not a gene.
In order for scientists to trust mappings, they need to understand the context
of the equivalence claim and who is making the claim. By providing the context
– in scientific terms – together with the provenance of the mapping – how it
was made and by whom – the scientist can understand the notion of equivalence
captured and make an informed decision about whether to include it in their
application.
In this paper we
– Identify the challenges of identity co-reference across datasets (Section 2);
– Discuss the metadata required to describe a dataset and capture the context
of its links to other datasets (Section 3);
– Present the rules used to control co-reference computation and their usage
in the Open PHACTS Identity Management Service (IMS) (Section 4).
2 Multiple Identifiers, but are they the same?
Information relevant for drug discovery research is sourced from a variety of
overlapping datasets. For example, information about drugs can be retrieved
from DrugBank [20], while data about the chemical compounds that compose
the drug are available from ChEMBL [10], ChemSpider [21] and DrugBank,
and details of the target – typically a protein – that the drug interacts with
are available from ChEMBL and UniProt [22]. Since each of these datasets is
4
http://www.uniprot.org/uniprot/P06213 accessed September 2013
Computing Identity Co-Reference 3
modelled with a different focus, and have their own identifier scheme, when can
we say that two records are truely equivalent? In some cases it is straightforward.
An entry in ChemSpider and an entry in ChEMBL that share the same InChI
will report about the same chemical, e.g. “imatinib mesylate”. However, when
we consider the drug entry in DrugBank, e.g. “Gleevec”, there can be multiple
InChI entries associated, e.g. the “gleevec” entry contains the InChI for both
“imatinib” and “imatinib mesylate”. In this case, are the records the “same”.
For a scientist interested in “gleevec” they would be, but for someone interested
only in “imatinib mesylate” perhaps not.
Many datasets contain links to other related datasets. For example, UniProt
includes links to several related datasets. However the nature of these links are
not captured; in the case of the RDF export of UniProt they are all stated as
rdfs:seeAlso. It is therefore hard to automatically reuse such links due to the
differing natures of the datasets and meaning of the link. A case in point would
be the relationships stated between UniProt – a protein sequence dataset – and
Protein Data Bank (PDB) [2] – a 3-dimensional protein structure dataset. Due
to the differences in the representations and the data gathering techniques the
concepts in these datasets are not, in the strictest sense, equivalent, i.e. there is
not a 1:1 isomorphism between the data instances. In particular, the UniProt
record for the insulin receptor protein (P062135 ) links to 18 PDB entries. These
in turn map back to six UniProt entries; one of which is the insulin receptor
protein we started with.
For users and applications to trust and reuse equivalence relationships, they
need to understand what notion of equivalence is being claimed. UniProt, in their
RDF export, weaken their links to rdfs:seeAlso to avoid making inaccurate
claims, but this reduces the knowledge conveyed. At the other extreme, the
datasets in the Linked Data Cloud tend to be very relaxed about their claims
of “equivalence” and widely use, or misuse, the predicate owl:sameAs; typically
they do not intend the strict semantics of owl:sameAs. As such, these links need
to be used with caution. Such context will enable applications to choose which
links to include. For example, for the vast majority of drug discovery research
– almost all – it would be acceptable to use “equivalence” links between gene
entries in Entrez Gene and protein entries in UniProt as genes are often used
as proxies for the protein that they encode. For example, when searching for
data about a target, it is common to enter the gene name as the search term.
However, there are those who would require that such links are not included,
e.g. working in very niche specialisms or on edge cases.
3 Describing Datasets and their Links
For effective linking between datasets, and to enable trust in their use in applica-
tions, it is essential to understand what has been linked and how. This requires
descriptions of the datasets and the links themselves.
5
http://www.uniprot.org/uniprot/P06213 accessed Sept. 2013
4 C.Y.A. Brenninkmeijer et al.
Fig. 1: Illustrating example links for a ChemSpider entry from three linksets
connecting ChemSpider with DrugBank. Each linkset has a separate publisher
using a different link predicate relationship.
A dataset description is essential for data discovery and to enable consumers
to use the data. It is a means to provide core metadata about a dataset, e.g. its
title, description, and license information. It can also convey information about
how to access the dataset, e.g. a SPARQL endpoint location, how the data is
modelled, i.e. which vocabularies have been used, and key statistics about the
data, e.g. number of triples, number of subjects, etc.
The Vocabulary of Interlinked Datasets (VoID) [1] provides a vocabulary of
terms and a deployment model for dataset descriptions. Where possible, VoID
recommends re-using Dublin Core terms, e.g. for providing the title (dct:title)
and license (dct:license). VoID itself provides predicates for expressing access
and statistical information. A key feature of VoID is the ability to embed the
dataset description with the data. This is achieved by the data linking back to
its description using the void:inDataset predicate. VoID also introduces the
notion of a Linkset which is a collection of links between a pair of datasets.
The linkset description captures the context of the links, i.e. which datasets are
linked using what predicate. Some benefits of providing separate linksets are that
the linksets can develop independently of the datasets and even be provided by
third-parties, as well as being used in co-reference services. Figure 1 illustrates
four example links for a ChemSpider entry to the DrugBank dataset. These links
are drawn from three distinct linksets published by different providers using a
variety of link relationships.
However, VoID does not prescribe which properties must be provided and
those that are more optional. This makes the general re-use of VoID dataset
descriptions difficult as there is no guarantee that the information you need will
Computing Identity Co-Reference 5
be provided. For example, for the pharmaceutical companies involved with Open
PHACTS it is essential to understand the licensing restrictions of the dataset,
but this information may not be present in the dataset description. There is also
no notion of capturing the version of a dataset, which is essential to know when
linking between datasets. To overcome these challenges in the Open PHACTS
project, we have defined a checklist of properties that must be provided [13].
We have also identified additional vocabulary terms for capturing the context of
the linkset, e.g. the Provenance, Authoring and Version vocabulary (PAV) [6] is
used to provide the version number of a dataset.
4 Identity Co-Reference Computation
For systems such as the Open PHACTS Discovery Platform (OPSDP) [14], links
are required between several datasets. However, it is not practical to require that
each pair of datasets is directly related. As such we propose that co-reference
identities can be transitively computed from those that are supplied. However,
care needs to be taken to avoid computing inaccurate co-references as may result
from a chain of links with varying meaning. In this section we detail some alter-
native strategies, the problems with them, and outline the approach currently
adopted by the Open PHACTS Identity Mapping Service (IMS) [4].
4.1 Link Predicate
A VoID linkset includes three key pieces of information: the dataset that is the
subject of the link triples, the dataset that is the object of the link triples, and
the predicate used in the links. Based on this information it is feasible to compute
transitive co-reference links based on the properties of the link predicate. For
example, given the linksets A −−−→ B and B −−−→ C which link the datasets
p p
A and B, and B and C respectively, with the link predicate p, and let p be
the predicate owl:sameAs, then it follows through the properties of the link
predicate that we have the linkset A −−−→ C which links datasets A and C.
p
However, as shown by Halpin et al [15] when owl:sameAs links are used they
are often not truely equivalent. Additionally, as indicated in Section 2, many dif-
ferent link predicates are in use in life sciences datasets. These predicates each
have different properties, e.g. rdfs:seeAlso is neither transitive nor symmetric.
As such, it is not possible to compute a complete network of co-reference iden-
tifiers across the set of required datasets based on OWL reasoning over the link
predicates. Therefore we need a custom approach to computing the transitive
co-reference across datasets that requires more than just the link predicate as a
means of control.
4.2 Linkset Justification
The limitation of the linkset predicate approach stems from the generality of the
linking predicate and thus the lack of domain knowledge that it conveys. One
6 C.Y.A. Brenninkmeijer et al.
Term Justification
Chemical entity The concepts linked represent the same chemical entity.
sio:SIO 010004
Gene The concepts linked are conceptually the same gene.
sio:SIO 010035
InChI Key The concepts linked have the same InChI Key.
cheminf:CHEMINF 000059
Protein The concepts linked are conceptually the same protein.
sio:SIO 010043
Protein coding gene A gene resource and a protein resource are being treated
sio:SIO 000985 as being equivalent
Table 1: A subset of the vocabulary terms used to capture the justification of
a linkset and the operational equivalence that is interpreted. sio represents the
Semantic Science Integrated Ontology namespace and cheminf the Chemical
Information Ontology namespace.
approach to overcome this, whilst still retaining the notion of a VoID linkset,
would be to mint a new linking predicate for each notion of equivalence; these
could be created as sub-properties of existing mapping predicates. However there
is a major social barrier to such an approach; gaining consensus on the required
linking predicates and updating the existing links in the datasets to use these
new link predicates. As such, it is unlikely to gain traction.
Another alternative is to annotate the linkset descriptions with additional
contextual data; this enables the use of the existing links unchanged. We term
this the justification for the linkset; the notion captured is the scientific inter-
pretation of the operational equivalence applied by the linkset. For example, two
chemical datasets, A and B, that are linked because they have the same InChI
string would express this relationship in the linkset VoID header with the triples
:A-B_Linkset void:linkPredicate skos:exactMatch ;
bdb:linksetJustification cheminf:CHEMINF_000059 .
where :A-B_Linkset is the resource that describes the linkset, the link predicate
is declared to be skos:exactMatch, and the justification is specified using the
BridgeDb vocabulary namespace (bdb6 ) with the value taken from the Chemical
Information Ontology namespace (cheminf7 ). The linkset can be expressed as
j
A −−−→ B where the justification j is cheminf:CHEMINF 000059 and the link
p
predicate p is skos:exactMatch. The set of supported justifications within the
Open PHACTS IMS can be found in [13]; a subset of which are included in Table
1. A key advantage of this approach is that it extends rather then changes the
existing data.
Based on the justification of linksets, we can compute transitive linksets. For
example, we can generate a linkset between datasets A and C through some
6
http://vocabularies.bridgedb.org/ops to appear soon
7
http://semanticscience.org/resource/CHEMINF_000059 accessed Sept. 2013
Computing Identity Co-Reference 7
intermediary dataset B if there is a linkset between A and B and one between B
and C such that both linksets have the same justification. Definition 1 formally
gives the rule for computing transitive linksets based on their linkset justification.
Note that we do not require that the linksets have the same link predicate. The
resulting transitive linkset is given the weaker of the two link predicates with a
hierarchy of
owl:sameAs skos:exactMatch skos:closeMatch rdfs:seeAlso.
Thus, if p was the link predicate owl:sameAs and r the link predicate rdfs:seeAlso,
j
the computed linkset A −−−→ C would have the link predicate rdfs:seeAlso.
r
Definition 1 (Transitive computation based on linkset justification).
j j
Given datasets A, B, and C, linksets A −−−→ B and B −−−→ C both with the
p r
justification j and link predicates p and r respectively then we can generate the
linkset
j
– A −−−→ C if p r;
r
j
– A −−−→ C if r ≺ p.
p
By iteratively applying the rule given in Definition 1 it is possible to compute
chains of linksets that use the same justification. However it is possible to enter
an infinite cycle; thus the IMS implementation prevents the same linkset being
used more than once in a chain. As part of the provenance of the computed
linkset, the linksets that are used to compute it are tracked.
4.3 Permitting Cross-type Equivalence
Within the life sciences it is common to use gene names as proxies for protein
names since gene names are shorter and more standardised. It is easy for a
human to distinguish when this is being done but impossible for a computer to
distinguish. A key requirement for the OPSDP is to permit a user to enter with
a gene name that is then resolved to a URI for that gene. However, it should
be possible to retrieve information about the target – a protein, or group of
proteins – for which the entered gene name is a proxy. This means that it must
be possible to state that a gene and a protein are operationally equivalent, i.e.
to have equivalence across semantic types.
This is straightforward using the linkset justification approach. We introduce
a new justification for a protein coding gene (sio:SIO 000985), see Table 1.
The complication comes when computing the transitive linksets to enable a user
entered gene to relate to the protein information in each of the datasets. The
transitive computation now needs to support equivalence across semantic types
with different justifications. In particular we want to support chains of one or
more protein linksets, a protein-gene linkset, and one or more gene linksets. It
is important to prevent the use of protein-gene linksets to go from a gene to a
8 C.Y.A. Brenninkmeijer et al.
protein and back again; this is to prevent a chain of links whereby we end up
with protein X being claimed to be the same as protein Y due to crossing the
semantic type boundary multiple times.
Additional contextual information is required from the linkset description.
Specifically, the semantic type of the data being linked. This needs to be cap-
tured at the linkset level since datasets can contain multiple semantic types,
e.g. ChEMBL, DrugBank and Ensembl [9]. Two additional predicates are used
to capture the semantic type: bdb:subjectsType and bdb:objectsType. Note
that these mirror the VoID predicates for specifying the datasets that are linked.
Definition 2 extends the co-reference transitive computation rule given in
Definition 1 to support cross-type mappings. Note that for simplicity we have
omitted the link predicate from the rules in Definition 2. These are derived in the
same way as for the rule given in Definition 1. The first clause of Definition 2 is a
combination of the clauses in Definition 1, with the additional constraint that all
of the datasets involved are of the same semantic type. The second clause allows
for a linkset of the same semantic type and a linkset with a cross semantic type
justification to be combined, with the resulting linkset being given the cross-type
justification.
Definition 2 (Cross-type transitive computation). Let A, B, and C be
datasets, τ and τ 0 be semantic types with τ 6= τ 0 , and j and j 0 be linkset justifi-
cations such that j links datasets with the same type and j 0 links datasets across
types. Then the following two rules hold for transitive linkset computation:
– Same semantic type and justification
j j
Aτ −−−→ Bτ ∧ Bτ −−−→ Cτ
j
Aτ −−−→ Cτ
– Cross semantic type and justification
j j0 j0 j
Aτ −−−→ Bτ ∧ Bτ −−−→ Cτ 0 ∨ Aτ −−−→ Bτ 0 ∧ Bτ 0 −−−→ Cτ 0
j0
Aτ −−−→ Cτ 0
By iteratively applying Definition 2 it is possible to compute the co-reference
of URIs across proteins and genes. Note that the definition only permits a single
cross-type link justification, e.g. “protein coding gene”, to be used in any chain,
although arbitrary numbers of same type justifications, e.g. “same protein” or
“same gene”, can be applied. This is due to the consequence of the second rule
being given the cross-type link justification. Thus, a chain of links resulting in
inaccurate mappings is prevented.
5 Open PHACTS IMS Implementation
The Open PHACTS IMS implementation is an extended version of BridgeDb
to support cross-references over linked data sources, i.e. supporting the use
Computing Identity Co-Reference 9
Fig. 2: Screenshot of the Open PHACTS Identity Mapping Service web interface
showing the results for the UniProt entry for insulin receptor.
of URIs to represent records in datasets. The source code is available from
https://github.com/openphacts/IdentityMappingService and the service
is accessible through the Open PHACTS API, https://dev.openphacts.org/.
The IMS has implemented the transitive co-reference computation rule given
in Definition 2. Computed linksets are created with dataset descriptions giving
full details of the datasets linked and the justification for the link. A screenshot
of the web interface to the IMS is shown in Figure 2 with the results for a look-up
for the UniProt insulin receptor URI.
5.1 Result of Co-reference Computation
For the 1.3 release of the OPSDP, the IMS was supplied with 104 linksets from
9 providers linking 37 datasets and containing 7,096,712 links. These are shown
in the visualisation in Figure 3a; nodes represent datasets and edges the linksets
between them, with the colour signifying the linkset justification. Note that
the large number of linksets is a consequence of splitting links based on their
semantic type – resulting in multiple linksets between some datasets – and in
the case of Ensembl the linksets were further split by species with 12 species
covered. The visualisation highlights the Open PHACTS design decision to use
a small number of datasets as mapping centres; chemical alignment is performed
through the Open PHACTS Chemical Registration Service [19] – OCS in the
figure – with a few through the Human Metabolome Database (HMDB) [23] –
Hm in the figure – proteins centre around UniProt – S in the figure – and genes
around Ensembl – En in the figure.
Following the transitive co-reference computation, there are 883 linksets con-
taining a total of 17,383,846 links. These are shown in the visualisation in Fig-
ure 3b. The left side of the visualisation is dominated by the linksets that match
InChIs via the Open PHACTS Chemical Registration Service while the right side
10 C.Y.A. Brenninkmeijer et al.
(a) Visualisation showing connectivity of the linksets provided as input to the IMS.
(b) Visualisation showing connectivity of the linksets after co-reference computation.
Legends
Colour Justification URI
Red InChI cheminf:CHEMINF 000059
Purple Chemical entity sio:SIO 010004
Green Protein sio:SIO 010043
Orange Gene sio:SIO 010035
Blue Protein coding gene sio:SIO 000985
Light blue Pathway sio:SIO 001107
Yellow Pathway name edam:data 2342
Code Dataset Code Dataset
AERS Adverse events reporting system Hm Human metabolite database
Bg BioGrid I InterPro
Ca Chemical abstracts service Ip International protein index
Chebi Chemical entities of biological interest L NCBI Gene
Ch.M ChEMBL molecule MGI Mouse genome informatics
Ch.T ChEMBL target MSH Medical subject headings
Ch.TC ChEMBL target components Pw Pathway ontology
Ck KEGG Compound Om Online mendelian inheritance in man
Cw ConceptWiki OCS OPS Chemical registration service
Cpc PubChem Compound Pd Protein databank
Cs ChemSpider Q NCBI Reference Sequence Database
D Saccharomyces genome database R Rat genome database
Db.D Drugbank drugs S UniProt
Db.T Drugbank targets Ug UniGene
Em European nucleotide archive Up UniParc
En Ensembl Wi Wikipedia
F FlyBase Wp Wikipathways
Hac HGNC accession number Z Zebrafish information network
Fig. 3: Visualisations showing connectivity of the linksets.
Computing Identity Co-Reference 11
is dominated by protein coding genes due to the application of the cross-type
co-reference rule. The visualisation highlights a second Open PHACTS design
decision; to use ConceptWiki8 – Cw in the figure – as the source for text-to-URI
translation. This is shown by its connectivity to every other dataset; one of the
reasons for computing the co-references. As a consequence of these co-reference
computations, users of the OPSDP are able to enter with any chemical, protein,
or gene URI known to the IMS and retrieve information about chemicals or
targets.
5.2 Evaluation of Co-reference Computation
There are several benefits to the co-reference computation approach that has
been implemented as part of the OPSDP IMS; not least of which is the increased
inter-connectivity across the datasets.
First it eases the burden on the dataset providers; with only a small number of
additional metadata triples being required to provide justifications and semantic
types. This allows systems such as the OPSDP to exploit the fact that many
datasets already contain links within their data to instances in other datasets;
particularly those who publish their data as RDF. However it cannot be expected
that they can link to every dataset that is required for every possible use case.
Thus, by applying the co-reference computation we are able to infer additional
connectivity across the data.
Second the co-reference computation tightly controls what can be equated,
e.g. only chemical entities can be related through an InChI, and which justi-
fications are allowed to cross semantic types, e.g. protein coding gene relating
genes and proteins. These safeguards ensure that the result of the co-reference
computation matches the expectations of the domain scientists.
User evaluation of the results of the co-reference computation is on-going
with domain scientists. The IMS has been successfully deployed by the OPSDP
enabling the integration of data across the data sources shown in Figure 3.
6 Related Work
Identifiers.org [18] provide a linked data identifier for many life sciences datasets.
This consists of a URI constructed according to the rules of the identifier scheme
of the underlying dataset. However, they do not attempt to identify co-referent
identifiers. The Identifiers.org approach is complimentary to the co-reference
work reported here. The IMS accepts and returns the Identifiers.org form of
URI for each of the datasets.
Bio2RDF [5] is another closely related approach. Bio2RDF republishes ex-
isting datasets as RDF where the source data has been originally published as
database dumps or in other formats. Where the original datasets contain links
to other datasets these are published with the RDF. These links could be used
8
http://ops.conceptwiki.org/ accessed September 2013
12 C.Y.A. Brenninkmeijer et al.
as a source of mapping information for the IMS and the IMS already returns the
Bio2RDF identifier as an alternative URI.
BridgeDb [16] and sameas.org [11] are co-reference services similar to the
IMS. The goal of sameas.org is to ingest the links of as many linked data sources
as possible as such it has a much broader coverage of topics than the IMS.
It provides an API for returning all known equivalent URIs. However, there is
no curation of the links nor context for the data. Additionally, to the best of
our knowledge, there is no transitive computation across the co-referent URIs.
BridgeDb is a life sciences focused database identity cross-reference service. How-
ever, it does not track the equivalent URIs for the database entries. It also does
not characterise the database cross-reference nor their context. The IMS is an
extension of BridgeDb to provide a URI look-up service that understands the
context of the links as well as the linking relationship.
Another closely related area of research is focused on generating tools for
identifying links between pairs of datasets; either at the schema level Ontology
Matching or at the instance level. The latter of these is most relevant to the work
in this paper. Since 2009 there has been an instance matching track9 in the an-
nual ontology matching competition10 to compare such tools. The most recent re-
sults are available from http://www.instancematching.org/oaei/imei2013/
results.html. The links generated by these instance matching tools could be
used as input to the IMS.
7 Conclusions
In this paper we presented the challenges for co-reference across life sciences
datasets that stem from each dataset using their own identifier scheme. We
have argued that there is not a one size fits all notion of equivalence across
concepts in these different datasets since they model the data at different levels
of granularity, e.g. should a drug entry be equated to an entry about the chemical
compound. Additionally, users of the data want to apply varying notions of
equivalence based on the task they are performing, e.g. should genes and proteins
always be equivalent. As such we have proposed that the notion of operational
equivalence should be captured in the linksets that relate a pair of datasets as
the justification for the linkset. The advantage of stating it as a justification
rather than a mapping predicate is that existing linksets can be easily extended.
Due to the fact that each pair of datasets is not related in the web of data, we
have developed rules for transitively computing co-reference across datasets. To
support scenarios where genes and proteins should be equated, the co-reference
computation allows the crossing of semantic types. We presented a rule for pre-
venting undesired co-references being computed whilst ensuring that concepts
within a given type are completely covered. This has been implemented in the
9
http://www.instancematching.org/oaei/imei2013/results.html accessed
November 2013
10
http://oaei.ontologymatching.org/ accessed November 2013
Computing Identity Co-Reference 13
Open PHACTS Identity Mapping Service. Details of how the IMS is used within
the OPSDP can be found in [14,4].
As future work we will allow applications to apply different scientific lenses
[3] over the co-reference network to vary the notion of operational equivalence
being applied, i.e. to activate different combinations of linksets based on their
justification. These lenses depend upon the justifications used to compute the
co-references.
Acknowledgements
The research leading to these results has received support from the Innovative
Medicines Initiative Joint Undertaking under grant agreement number 115191,
resources of which are composed of financial contribution from the European
Union’s Seventh Framework Programme (FP7/2007- 2013) and EFPIA compa-
nies’ in kind contribution.
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