Detecting Inner-Ear Anatomical and Clinical Datasets in the Linked Open Data (LOD) CloudMuntazir Mehdi0Aftab Iqbal0Yasar Khan0Stefan Decker0Ratnesh Sahay0Insight Centre for Data Analytics
,
NUI Galway
,
Ireland
Linked Open Data (LOD) Cloud is a mesh of open datasets coming from di erent domains. Among these datasets, a notable amount of datasets belong to the life sciences domain linked together forming an interlinked \Life Sciences Linked Open Data (LSLOD) Cloud". One of the key challenges for data publishers is to identify and establish links between newly generated domain speci c datasets and LSLOD Cloud. While a number of publishing tools exist for creating links from new to existing datasets, tools to detect domain-speci c relevant datasets for linking purposes are missing. In this paper, we propose an extended technique for automatically identifying relevant datasets in LSLOD Cloud for inner-ear anatomical and clinical terminologies. We validate the proposed technique with experiments over the publicly accessible LSLOD Cloud using realworld terminologies and datasets provided by clinical organizations.
Introduction
Silk [
11
] and LIMES [
7
], have been proposed to help publishers link their local
datasets to a remote LOD dataset. Given that there are now hundreds of remote
datasets and many of them are black-boxes that do not describe their content [
1
],
in practice it becomes a challenging task for a publisher to nd potential datasets
in order to setup links with the LOD Cloud. Currently, new publishers require
knowledge of available LOD datasets. The publishers thus search and manually
inspect the LOD datasets. In certain cases, the content of remote datasets are
usually described using VoID4, SPARQL 1.1 Service Descriptions, and specialized
vocabulary (or ontology), which may help, but these are not available for many
endpoints [
1, 10
].
This paper is based on our previous work of nding clinical trial datasets in
the LOD Cloud [
5, 6
], speci cally in LSLOD Cloud. Our main focus is on Exact
Literal Matching: a direct look-up of domain-speci c keywords/terms, meaning
an exact case-sensitive phrase match. In this paper we extend our proposed
\Multi Matching" algorithm [
6
] by (i) removing duplicate literal matching results
and reducing the overall query execution time; and (ii) validating the extended
algorithm on a real-life cochlea (inner-ear) datasets and terminologies. The
approach presented in this paper will work for any remote dataset accessed via a
SPARQL endpoint and will involve e cient look-ups, as opposed to ine cient
post- ltering (REGEX Filters) and non-standard SPARQL (Full-text Search).
Further, we present comparative evaluation of our previously proposed approaches
and approach presented in this paper for a real-world use case. The rest of the
paper is as follows: Section 2 presents our methodology and describes the extension
of our previous work to query SPARQL endpoints in order to determine their
relevance. Section 3 presents evaluation of our proposed approaches using di erent
metrics, seeking relevant LSLOD datasets for interlinking. Section 4 discusses
related work and Section 5 concludes this paper. Before moving to the next
section, we introduce our motivating scenario involving data providers who wish
to publish their corpora as Linked Data.
Motivating Scenario: Our work is conducted in the context of the SIFEM EU
project5, which aims at developing a semantic infrastructure interlinking an open
source Finite Element Tool [
2
] with existing data, models and new knowledge
for the multi-scale modeling of the inner-ear with regards to the sensorineural
hearing loss. One of the core goals of the project is to publish biomedical datasets
provided by the consortium partners as high-quality Linked Data.
4 http://www.w3.org/TR/void/; l.a. 2015/08/15.
5 http://www.sifem-project.eu/; l.a. 2015/08/15.
The SIFEM terminologies6, (Table 1 provides some examples of Terminologies)
are used for the identi cation of potential LSLOD datasets. The life sciences
community have been very active within the Linking Open Data movement. Of
the 1,014 linked datasets that are contained in LOD Cloud, 83 datasets relate to
life sciences, incorporating 3 billion facts and 191 million links in order to form
LSLOD Cloud. The proposed method in this paper probe SPARQL endpoints of
LSLOD datasets for detecting inner-ear anatomy and clinical datasets for linking.
2
Methodology: Detect Matching
The Detect Matching technique, presented in this paper, uses the Query Terms
(QTerms) generated using our previously proposed tools given in [
5
]. The QTerms
are used to probe SPARQL endpoints in order to nd relevant LSLOD datasets.
Each of the approach (Multi-Matching ( Match) [
6
], and Detect Matching given
in this paper) takes as input a set of QTerms and a list of URLs for di erent
SPARQL endpoints (SEndpoints). We generate SEndpoints by logging URLs
of all candidate SPARQL endpoints in a particular domain. For our use-case, we
created a list of SEndpoints speci cally for the life sciences domain. Therefore, we
considered 35 SPARQL endpoints that are made publicly available by Bio2RDF7.
In [
6
], we presented Direct-Matching (DMatch) approach that creates a
single SPARQL query per QTerm and Multi-Matching Match approach that
creates eight di erent SPARQL queries for each QTerm. Although, ( Match)
covers possible variants for a QTerm that may exist at a speci ed SPARQL
endpoint but it is a costly operation (as we observe later in the Section 3),
because each QTerm is queried eight times on a speci ed SPARQL endpoint.
A QTerm can be represented with and without a language tag in a speci c
LSLOD dataset. For example, consider a QTerm that appears as \cochlea" and
\cochlea"@en on a particular LSLOD dataset, therefore, Match consider hits
twice for the same QTerm. Our third approach is re ned to exploit the VALUES
clause provided in SPARQL 1.1, which can be used to provide a list of values that
are used for query evaluation. By using VALUES keyword, we are able to supply
all possible variants (e.g., Cochlea, cochlea, COCHLEA, Cochlea@en,Cochlea@en,
cochlea@en, COCHLEA@en) for a QTerm in a single SPARQL query rather than
executing multiple queries on a speci ed SPARQL endpoint (as shown in the
Listing 1.1). Moreover, using the VALUES feature of SPARQL 1.1, we are able to
eliminate the duplicate matched results for a single QTerm. The pseudo-code of
DetMatch is shown in Algorithm 1.
SELECT DISTINCT ? s ?p WHERE f
? s ?p ? o .
VALUES ? o f
" C o c h l e a " " C o c h l e a " " c o c h l e a " "COCHLEA"
" C o c h l e a "@en " C o c h l e a "@en
" c o c h l e a "@en "COCHLEA"@en
" C o c h l e a "^^ h t t p : / / . . / . . / XMLSchema#s t r i n g
. . . . . . . g
Listing 1.1: Example SPARQL query for DetMatch approach.
3
Evaluation
In Section 2, we presented three di erent approaches to probe public SPARQL
endpoints for its relevance to a local dataset. In this section, we will compare the
results of our proposed approaches by carrying out a series of experiments on the
datasets provided by our clinical partners. All experiments were conducted on a
computer running 64-bit Windows7 OS, with 4GB RAM and an Intel Core i5
(2.53 GHz) CPU. We use a MySQL RDBMS to store experimental data, including
the endpoints to search and the results of successful queries. For experimentation,
we consider a total of 220 clinical terminologies (CTerms) (some terminologies are
exempli ed in Table 1), which results in a total of 589 Query Terms (QTerms).
And as mentioned in Section 2, we consider a total of 35 public endpoints from
Bio2RDF. Queries are sent to the public endpoints over HTTP using the standard
SPARQL protocol.
3.1
Results
We compare the results of our experiments based on three metrics: (i)Matched
Results (MR):represents the total number of query results obtained for all
terms against all SPARQL endpoints; (ii) Matched Terms (MT): represents
the number of distinct terms for which non-empty results were found for at least
one SPARQL endpoint; and (iii) Matched Endpoints (ME): represents the
number of distinct endpoints for which some term generated a non-empty result
8.
A detailed comparison of MT, MR and ME based on the three approaches
(DMatch, Match and DetMatch) is presented in Fig. 1{3 respectively.
In general, we see a signi cant increase in the number of matched terms (cf.
Fig. 1) when comparing DMatch approach against Match and DetMatch
approaches. This is obvious due to the usage of di erent variants (i.e., lowercase,
uppercase, proper case etc.) for each QTerm while probing SPARQL endpoints.
The reason behind same number of matched terms for Match and DetMatch
is because of using VALUES clause for DetMatch to supply all possible variants
for each QTerm in a single SPARQL query whereas in Match, we executed
multiple queries to cover all possible variants for each QTerm.
The signi cant increase in the number of matched terms (MT) subsequently
increases the number of matched results (MR) for Match and DetMatch in
contrast to DMatch approach (cf. Fig. 2). Comparing the matched results of
Match against DetMatch (cf. Fig. 2), we see a decline in the later approach.
The reason is the elimination of duplicate matched results for a QTerm using
DetMatch approach, which is not handled in Match due to the usage
of multiple queries. For example, consider an example term oxytocic that is
contained in the BioPortal SPARQL endpoint9. This term is de ned using
dct:title and obo:exact synonym predicates with the values \oxytocic"@en and
\oxytocic"^^http://www.w3.org/2001/XMLSchema#string respectively. In the
case of DetMatch, the term oxytocic is matched only once but in the case of
Match, it is matched twice due to the usage of multiple variants for a QTerm.
With respect to the number of SPARQL endpoints that are matched (cf. Fig. 3),
8 To illustrate these metrics, consider an example where a set of two QT erms = f
\a erent", \neuron" g is searched using either DMatch, Match or DetMatch
on SEndpoints = f \BioPortal", \ClinicalTrials", \MeSH" g, where the returned
results contained 3 matches of \neuron" in \BioPortal", 2 matches in \ClinicalTrials"
and 1 match in \MeSH". Similarly, the returned results for \a erent" had 0 matches
in \BioPortal", \ClinicalTrials" and \MeSH". Then MR = 6, MT = 1 and ME = 3.
9 http://bioportal.bio2rdf.org/sparql; l.a. 2015/08/15.
we observe that Match and DetMatch produce better results than DMatch
approach because of the usage of di erent possible variants for each QTerm.
Finally, we compare the query execution time for the three approaches
(DMatch, Match and DetMatch). The time (in seconds) is calculated
based on the execution time of all QTerms (that are present in one dataset) on a
speci c SPARQL endpoint. Fig. 4 shows the average query execution time (based
on 35 SPARQL endpoints that are considered) for the three di erent approaches
on our datasets.
From Fig. 1 and Fig. 3, we see that Match and DetMatch have identi ed
same number of matched terms as well as SPARQL endpoints but the overall
execution time of Match is much higher than that of DetMatch (cf. Fig. 4).
The reason is, Match executes eight SPARQL queries per QTerm, however,
DetMatch uses all possible combinations for a QTerm in a single SPARQL
query, thus reducing the overall query execution time. Similarly, it can also be
noticed that the query execution time of DetMatch (cf. Fig. 4) is slightly higher
than that of DMatch, with the amount of returned results as better as Match
(cf. Fig. 1 and Fig. 3).
In this section, we have presented results of three di erent approaches (DMatch,
Match and DetMatch) to probe SPARQL endpoints for its relevance to a
local dataset. By evaluating the results based on three di erent metrics (MT,
MR and ME) along with query execution time, we see that DetMatch approach
turns out to be the best option for discovering external LSLOD datasets that are
potential targets for interlinking with local dataset. The reason is, DetMatch
has the ability to state all possible variants for each QTerm in a single SPARQL
query, which lacks in the DMatch approach and not cost e ective in the case of
Match approach due to the 8 query-load. From the publisher's perspective,
if completeness of results is primarily important with less importance to the
e ciency of discovery process (execution time), then we can say that Match and
DMatch approaches are best suited for discovering public SPARQL endpoints
that are potential targets for interlinking to a local dataset.
Related Work
This work aims to nd relevant datasets on the LOD Cloud, LSLOD in particular.
In past the majority of approaches taken in exploiting the generic and noisy
LOD Cloud are performed in two categories (i) identify schema-level links (using
rdfs:subClassOf, owl:equivalentClass) between class hierarchies among di erent
RDF and OWL datasets; and (ii) identify instance-level links (using owl:sameAs)
among di erent RDF and OWL datasets.
Instance-level links: Leme at al. [
3
] identi es datasets for interlinking and
ranks them using probability measures based on a set of analyzed features. The
proposed approach suggests links amongst di erent data sources using high
level information, while schema- or instance-level information is not taken into
account. Nikolov et al. [
8, 9
] propose an approach to identify relevant datasets
for interlinking consisting of two steps: (1) searching relevant entities in other
datasets using keywords; and (2) ltering irrelevant datasets based on semantic
concept similarities using ontology matching techniques. We previously mentioned
that there are a number of linking frameworks available for Linked Data, including
Silk [
11
] and LIMES [
7
]. Both of these works provide a declarative language for
guiding the creation of links between datasets based on predicates. However, both
of these tools presume that a SPARQL endpoint for the target dataset is speci ed
in the input. Our work addresses the prior step of identifying public endpoints
of LSLOD datasets that are interesting to link to. Maali et al. [
4
] propose an
extension of the Google Re ne tool to curate and RDFize local datasets. The
extension can help nd legacy URIs for entities from target endpoints speci ed
by the user. The authors propose using custom full-text search over SPARQL
endpoints to nd relevant URIs for keyword terms in the local dataset; e.g., using
bif:contains over Virtuoso endpoints. However, they presume that the endpoints
of interest are manually speci ed by the user. Other works have discussed the
di culties of discovering relevant SPARQL endpoints on the Web. For example,
Buil Aranda et al. [
1
] note that few structured descriptions are available for
endpoints. Paulheim and Hertling [
10
] also note that the discovery of endpoints
is a di cult problem and propose methods to nd endpoints given a URI of
interest.
5
Conclusion
In this paper, we extend our approach of direct look-up of domain-speci c
keywords/terms in the LSLOD Cloud. We have been able to remove { compared
to previous approach [
5, 6
] { duplicate literal matching results and reduce the
overall query execution time. The extended approach is validated using the
domain speci c anatomical and clinical cochlea (inner-ear) terminologies. We
envision that our approach will complement the state-of-the-arts in schema and
instance linking tools for the LOD Cloud. Our approach will support linking
frameworks to generate automated links (without requiring prior knowledge of
LSLOD datasets), this in turn will help domain experts and scientists to explore
domain-speci c information contained in publicly available infrastructure like the
LOD Cloud. The three algorithms (DMatch, Match and DetMatch) have
certain advantages and trade-o s in context of query execution time, number of
results, and results replication, therefore, we intentionally avoid any t-for-all
suggestion. Depending on di erent clinical contexts, i.e., set of input clinical
terminologies, user may select one of the three algorithms or their combination
targeting speci c clinical scenario.
Acknowledgements: This publication has emanated from research supported in
part by the research grant from Science Foundation Ireland (SFI) under Grant Number
SFI/12/RC/2289 and EU project SIFEM (contract Number 600933).
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