=Paper=
{{Paper
|id=Vol-1491/paper_13
|storemode=property
|title=Improving Discovery in Life Sciences Linked Open Data Cloud
|pdfUrl=https://ceur-ws.org/Vol-1491/paper_13.pdf
|volume=Vol-1491
|dblpUrl=https://dblp.org/rec/conf/semweb/Hasnain15
}}
==Improving Discovery in Life Sciences Linked Open Data Cloud==
https://ceur-ws.org/Vol-1491/paper_13.pdf
Improving discovery in Life Sciences Linked
Open Data Cloud
Ali Hasnain1
Insight Center for Data Analytics, National University of Ireland, Galway
firstname.lastname@insight-centre.org
Abstract. Multiple datasets that add high value to biomedical research
have been exposed on the web as part of the Life Sciences Linked Open
Data (LSLOD) Cloud. The ability to easily navigate through these datasets
is crucial for personalized medicine and the improvement of drug discov-
ery process. However, navigating these multiple datasets is not trivial
as most of these are only available as isolated SPARQL endpoints with
very little vocabulary reuse. The content that is indexed through these
endpoints is scarce, making the indexed dataset opaque for users. We
propose an approach to create an active Linked Life Sciences Data Com-
pendium, a set of configurable rules which can be used to discover links
between biological entities in the LSLOD cloud. We have catalogued and
linked concepts and properties from 137 public SPARQL endpoints. Our
Compendium is primarily used to dynamically assemble queries retriev-
ing data from multiple SPARQL endpoints simultaneously.
1 Scene Setting
A considerable portion of the Linked Open Data cloud is comprised of datasets
from Life Sciences Linked Open Data (LSLOD). The significant contributors in-
cludes the Bio2RDF project1 , Linked Life Data2 and the W3C HCLSIG Linking
Open Drug Data (LODD) effort3 . The deluge of biomedical data in the last few
years, partially due to the advent of high-throughput gene sequencing technolo-
gies, has been a primary motivation for these efforts. There had been a critical
requirement for a single interface, either programmatic or otherwise, to access
the Life Sciences (LS) data. Although publishing datasets as RDF is a necessary
step towards unified querying of biological datasets, it is not sufficient to retrieve
meaningful information due to data being heterogeneously available at different
endpoints [2]. Moreover in the LS domain, LD is extremely heterogeneous and
dynamic [14,6]; also there is a recurrent need for ad hoc integration of novel
experimental datasets due to the speed at which technologies for data capturing
in this domain are evolving. As such, integrative solutions increasingly rely on
federation of queries [4]. With the standardization of SPARQL 1.1, it is now
possible to assemble federated queries using the “SERVICE” keyword, already
supported by multiple tool-sets (SWobjects and Fuseki etc). To assemble queries
encompassing multiple graphs distributed over different places, it is necessary
1
http://bio2rdf.org/ (l.a.: 2015-03-31 )
2
http://linkedlifedata.com/ (l.a.: 2015-05-16 )
3
http://www.w3.org/wiki/HCLSIG/LODD (l.a.: 2014-07-16 )
that all datasets should be query-able using the same global schema [15]. This
can be achieved either by ensuring that the multiple datasets make use of the
same vocabularies and ontologies, an approach previously described as “a priori
integration” or conversely, using “a posteriori integration”, which makes use of
mapping rules that change the topology of remote graphs to match the global
schema [5]. The methodology to facilitate the latter approach is the focus of our
research. Moreover for LD to become a core technology in the LS domain, three
challenges need to be addressed: i) dynamically discover datasets containing
data regarding biological entities (e.g. Drugs, Molecules), ii) retrieve informa-
tion about the same entities from multiple sources using different schemas, and
iii) identify, for a given query, the highest quality data.
To address the aforementioned challenges, we introduce the notion of an
active Compendium for LS data – a representation of entities and the links
connecting these. Our methodology consisted of two steps: i) catalogue develop-
ment, in which metadata is collected and analyzed, and ii) links creation, which
ensures that concepts and properties are properly mapped to a set of Query Ele-
ments (Qe) [17]. For evaluation purposes, Qe are defined in the context of Drug
Discovery and can be replaced by other Qe(s). We assume that the proposed
Compendium holds the potential to be used for a number of practical applica-
tions including assembling federated queries in a particular context. We already
proposed the Link Creation mechanism, approaches and the linking statistics
[7] as well as the cataloguing mechanism [9] and in this article we briefly report
the methodology, initial results for Compendium development (cataloguing and
linking), and an architecture for implementing Domain Specific Query Engine
(under progress) as one of the practical applications that federates SPARQL
query based on the set of mapping rules defined in the Compendium.
2 State of the Art
One approach to facilitate the “A posteriori integration” is through the use
of available schema: semantic information systems have used ontologies to rep-
resent domain-specific knowledge and enable users to select ontology terms in
query assembly [11]. BLOOMS, for example, finds schema-level links between
LOD datasets using ontology alignment [10], but it relies mainly on Wikipedia.
Ontology alignment typically relies on starting with a single ontology, which is
not available for most SPARQL endpoints in the LOD cloud, hence could not
be applied in our case. Furthermore, ontology alignment does not make use of
domain rules (e.g. for two same sequences, qualifies for same gene) nor the use
of URI pattern matching for alignment [7]. Approaches such as the VoID [1] and
the SILK Framework [16] enable the identification of rules for link creation, but
require extensive knowledge of the data prior to links creation. Query federation
approaches have developed some techniques to meet the requirements of efficient
query computation in the distributed environment. FedX [13], a project which
extends the Sesame Framework [3] with a federation layer, enables efficient query
processing on distributed LOD sources by relying on the assembly of a catalogue
of SPARQL endpoints but does not use domain rules for links creation. Our ap-
proach for link creation towards Compendium development is a combination of
the several linking approaches as already explained by Hasnain et. al [7]: i) sim-
ilarly to ontology alignment, we make use of label matching to discover concepts
in LOD that should be mapped to a set of Qe, ii) we create “bags of words” for
discovery of schema-level links similar to the approach taken by BLOOMS, and
iii) as in SILK, we create domain rules that enable the discovery of links.
3 Proposed Approach/ Methodology
We proposed a Compendium for navigating the LSLOD cloud. Our methodology
consists of two stages namely catalogue generation and link generation. Data
was retrieved from 137 public SPARQL endpoints4 and organized in an RDF
document - the LSLOD Catalogue. The list of SPARQL endpoints was captured
from publicly available Bio2RDF datasets and Datahub5 .
3.1 Methodology for Catalogue Development
For cataloguing, a preliminary analysis of multiple public SPARQL Endpoints
was undertaken and a semi-automated method was devised to retrieve all classes
(concepts) and associated properties (attributes) available by probing data in-
stances. The workflow definition is as follows:
1. For every SPARQL endpoint Si , find the distinct Classes C(Si ) :
C(Si ) = Distinct (P roject (?class (toList (BGP (triple [ ] a ?class ))))) (1)
2. Collect the Instances for each Class Cj (Si ) :
Ii : Cj (Si ) = Slice (P roject (?I (toList (BGP (triple ?a a < Cj (Si ) > )))), rand())
(2)
3. Retrieve the Predicate/Objects pairs for each Ii : Cj (Si ):
Ii (P, O) = Distinct (P roject (?p, ?o (toList (BGP (triple < Ii : Cj (Si ) > ?p ?o ))))
(3)
4. Assign Class Cj (Si ) as domain of the Property Pk :
Domain(Pk ) = Cj (Si ) (4)
5. Retrieve Object type (OT ) and assign as a range of the Property Pk :
rdf : Literal if (Ok is String)
dc : Image if (Ok is Image)
Range(Pk ) = OT ; OT = dc : InteractiveResource if (Ok is U RL)
P roject (?R (toList (BGP
(triple < Ok > rdf : type ?R))) if (Ok is IRI)
(5)
6 7
RDFS, Dublin Core and VoID vocabularies were used for representing the
data in the LSLOD catalogue, a slice of the catalogue for Pubchem SPARQL
endpoint is presented (fig.1).
4
http://goo.gl/ZLbLzq
5
http://datahub.io/ (l.a.: 2015-05-05)
6
http://dublincore.org/documents/dcmi-terms/ (l.a.: 2014-07-12)
7
http://vocab.deri.ie/void (l.a.: 2014-07-12)
Fig. 1: An Extract from the LSLOD Catalogue for Pubchem dataset
3.2 Methodology for Link Generation
During this phase subClassOf and subPropertyOf links were created amongst
different concepts and properties to facilitate “a posteriori integration”. The
creation of links between identified entities (both chemical and biological) is not
only useful for entity identification, but also for discovery of new associations
such as protein/drug, drug/drug or protein/protein interactions that may not be
obvious by analyzing datasets individually. Figure. 1 shows the subClassOf and
subPropertyOf links with defined Qes. Links were created (discussed previously
in [7]) using several approaches: i) Naı̈ve Matching/ Syntactic Matching/ Label
Matching, ii) Named Entity Matching, iii) Domain dependent/ unique identifier
Matching, and iv) Regex Matching.
4 Applications/ Current Implementation
As of 31st May 2015, the Compendium consists of 280064 triples representing
1861 distinct classes and 3299 distinct properties catalogued from 137 endpoints.
4.1 DSQE: Domain-specific Query Engine
The general architecture of the DSQE (Fig 2) shows that given a SPARQL
query, the first step is to parse the query and get individual triple patterns.
Then Compendium is used for the triple pattern wise source selection (TPWSS)
to identify relevant sources against individual triple patterns of the query. The
Compendium enumerates the known endpoints relates each endpoint with one
or more graphs and maps the local vocabulary to the vocabulary of the graph.
The resulting query is executed on top of Apache Jena query engine.
An instance8 of the DSQE is deployed in the context of drug discovery
[8]. Using ‘Standard’ query builder, the user can select a topic of interest (e.g.
8
http://srvgal86.deri.ie:8000/graph/Granatum
Fig. 2: Compendium and Query Engine Architecture
Molecule) along with the list of associated properties. We plot the catalogued
subclasses of few Qe and the total number of distinct instances retrieved per Qe
while querying using DSQE (Fig 3).
5 Evaluation
So far we evaluated the performance of our catalogue generation methodology
and recorded the times taken to probe instances through endpoint analysis of 12
endpoints whose underlying data sources were considered relevant for drug dis-
covery - Medicare, Dailymed, Diseasome, DrugBank, LinkedCT, Sider, National
Drug Code Directory (NDC), SABIO-RK, Saccharomyces Genome Database
(SGD), KEGG, ChEBI and Affymetrix probesets. The cataloguing experiments
were carried out on a standard machine with 1.60Ghz processor, 8GB RAM
using a 10Mbps internet connection. We recorded the total available concepts
and properties at each SPARQL endpoint as well as those actually catalogued in
our Compendium [9]. Total number of triples exposed at each of these SPARQL
endpoints and the time taken for cataloguing was also recorded. We selected
those SPARQL endpoints which have a better latency for this evaluation, as the
availability and the uptime of the SPARQL endpoint is an important factor for
cataloguing. Best fit regression models were then calculated. As shown in Fig.
4, our methodology took less than 1000000 milliseconds (<16 minutes) to cata-
logue seven of the SPARQL endpoints, and a gradual rise with the increase in the
number of available concepts and properties. We obtained two power regression
models (T = 29206∗Cn1.113 and T = 7930∗Pn1.027 ) to help extrapolate time taken
to catalogue any SPARQL endpoint with a fixed set of available concepts (Cn )
and properties (Pn ), with R2 values of 0.641 and 0.547 respectively. Using these
models and knowing the total number of available concepts/properties, a devel-
oper could determine the approximate time (ms) as a vector combination. KEGG
and SGD endpoints took an abnormally large amount of time for cataloguing
than the trendline. The reason for this may include endpoint timeouts or network
Fig. 3: Number of retrieved Instances and Subclasses linked to any Qe
Fig. 4: Time taken to catalogue 12 SPARQL endpoints
delays. We also evaluated the performance of our Link Generation methodology
by comparing it against the popular linking approaches. Using WordNet the-
sauri we attempted to automate the creation of bags of related words using 6
algorithms [7]: Jing Conrath, Lin, Path, Resnik, Vector and WuPalmer with
unsatisfactory results (Figure 5(c)). Our linking approaches resulted in better
linking rate as shown in Figure 5(a,b)
6 Discussion
There is great potential in using semantic web and LD technologies for accessing
and querying Life sciences data for finding Meaningful Biological Correlations.
However, in most cases, it is not possible to predict a priori where the rele-
vant data is available and its representation. Our current research provides the
concept and methodology for devising an active Linked Life Sciences Data Com-
pendium that relies on systematically issuing queries on various life sciences
SPARQL endpoints and collecting its results in an approach that would other-
wise have to be encoded manually by domain experts. Current experiments and
evaluation uses a set of Qe, which were defined in a context of drug discovery. The
number of classes per endpoint varied from a single class to a few thousands. Our
initial exploration of the LSLOD revealed that only 15% of classes are reused.
However, this was not the case for properties, of which 48.5% are reused. Most
Fig. 5: (a) Number of Classes Linked, (b) Number of Properties Linked, (c)
Number of Classes linked through available similarity linking approaches
of the properties found were domain independent (e.g. type, seeAlso); however,
these are not relevant for the Compendium as they cannot increase the richness
of information content. Although a very low percentage of linking becomes pos-
sible through naı̈ve matching or manual/domain matching, the quality of links
created are highly trusted [7]. It is also worth noticing that 23% of identified
classes, and 56.2% of the properties remained unlinked, either because they are
out of scope or cannot match any Qe. This means that the quality as well as the
quantity of links created is highly dependent on the set of Qe used.
7 Open Issues and Future Directions
Multiple challenges faced which can hinder the applicability of our approach:
– Some endpoints return timeout errors when a simple query (SELECT DISTINCT
?Concept WHERE {[ ] a ?Concept}) is issued.
– Some endpoints have high downtime and cannot be generally relied.
– Many endpoints provide non-deferenceable URI and some derefenceable URI
do not provide a “type” for the instance.
In future an extension under consideration to available Compendium is to en-
rich it with statistical and provenance information with appropriate changes to
DSQE and evaluate the overall performance. This includes information includ-
ing void:triples, void:entities, void:classes, void:properties, void:distinctSubjects
and void:distinctObjects in case of statistical cataloguing where as dcterms:title,
dcterms:description, dcterms:date, dcterms:publisher, dcterms:contributer, dc-
terms:source, dcterms:creator,
dcterms:created, dcterms:issued and dcterms:modified in case of provenance.
Currently we are extending DSQE to convert any SPARQL 1.0 query into cor-
responding SPARQL 1.1 query by using TPWSS information and the SPARQL
”SERVICE” clause. Implementing so DSQE will be able to answer any fed-
erated SPARQL Query considering the desired endpoint being catalogued in
Compendium. The performance of this extended DSQE is aimed to compare
with state of the art query Engine FedX [13] using extensive evaluation criteria
including source selection in terms of number of ASK, total triple pattern-wise
sources selected, source selection time and total number of results retrieved per
query. For this evaluation we aim to select some queries from available query
federation benchmark e.g FedBench [12] and also plan to define some complex
biological queries applicable on 10 real time publicly available datasets. Issues
related to Identity Resolution are also considered as future work.
Acknowledgement
This research has been supported in part by Science Foundation Ireland under
Grant Number SFI/12/RC/2289 and SFI/08/CE/I1380 (Lion 2). The author
would also like to acknowledge Stefan Decker being PhD supervisor.
References
1. Alexander, K., Hausenblas, M.: Describing linked datasets-on the design and usage
of void, the’vocabulary of interlinked datasets. In: In Linked Data on the Web
Workshop (LDOW 09), in conjunction with WWW09. Citeseer (2009)
2. Bechhofer, S., Buchan, I., De Roure, D., Missier, P., et al.: Why linked data is not
enough for scientists. Future Generation Computer Systems 29(2), 599–611 (2013)
3. Broekstra, J., Kampman, A., Van Harmelen, F.: Sesame: A generic architecture
for storing and querying RDF and RDF schema. In: The Semantic Web—ISWC
2002, pp. 54–68. Springer (2002)
4. Cheung, K.H., Frost, H.R., Marshall, M.S., et al.: A journey to semantic web query
federation in the life sciences. BMC bioinformatics 10(Suppl 10), S10 (2009)
5. Deus, H.F., Prud’hommeaux, E., Miller, M., Zhao, J., Malone, J., Adamusiak,
T., et al.: Translating standards into practice–one semantic web API for gene
expression. Journal of biomedical informatics 45(4), 782–794 (2012)
6. Goble, C., Stevens, R., Hull, D., et al.: Data curation+ process curation= data
integration+ science. Briefings in bioinformatics 9(6), 506–517 (2008)
7. Hasnain, A., Fox, R., Decker, S., Deus, H.F.: Cataloguing and linking life sciences
LOD Cloud. In: 1st International Workshop on Ontology Engineering in a Data-
driven World collocated with EKAW12 (2012)
8. Hasnain, A., Kamdar, M.R., Hasapis, P., Zeginis, D., Warren Jr, C.N., et al.: Linked
Biomedical Dataspace: Lessons Learned integrating Data for Drug Discovery. In:
International Semantic Web Conference (In-Use Track), October 2014 (2014)
9. Hasnain, A., e Zainab, S.S., Kamdar, M.R., Mehmood, Q., Warren Jr, C.N., Fa-
timah, Q.A., Deus, H.F., Mehdi, M., Decker, S.: A roadmap for navigating the
life sciences linked open data cloud. In: Semantic Technology, pp. 97–112. Springer
(2014)
10. Jain, P., Hitzler, P., Sheth, A.P., Verma, K., Yeh, P.Z.: Ontology alignment for
linked open data. In: The Semantic Web–ISWC 2010, pp. 402–417. Springer (2010)
11. Petrovic, M., Burcea, I., Jacobsen, H.A.: S-ToPSS: semantic toronto publish/sub-
scribe system. In: Proceedings of the 29th international conference on Very large
data bases-Volume 29. pp. 1101–1104. VLDB Endowment (2003)
12. Schmidt, M., Görlitz, O., Haase, P., Ladwig, G., Schwarte, A., Tran, T.: Fedbench:
A benchmark suite for federated semantic data query processing. In: The Semantic
Web–ISWC 2011, pp. 585–600. Springer (2011)
13. Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: Fedx: a federation
layer for distributed query processing on linked open data. In: The Semanic Web:
Research and Applications, pp. 481–486. Springer (2011)
14. Stein, L.D.: Integrating biological databases. Nature Reviews Genetics 4(5), 337–
345 (2003)
15. Studer, R., Grimm, S., Abecker, A.: Semantic web services: concepts, technologies,
and applications. Springer (2007)
16. Volz, J., Bizer, C., Gaedke, M., Kobilarov, G.: Discovering and maintaining links
on the web of data. Springer (2009)
17. Zeginis, D., et al.: A collaborative methodology for developing a semantic model for
interlinking Cancer Chemoprevention linked-data sources. Semantic Web (2013)