International Journal of Internet and Distributed Systems, 2013, 1, 1-8 Published Online February 2013 (
How Interlinks Influence Federated over SPARQL
Nur Aini Rakhmawati
Digital Enterprise Research Institute, National University of Ireland, Galway, Ireland
Received January 15, 2013; revised February 17, 2013; accepted February 25, 2013
As the Web of Data grows, the number of available SPARQL endpoints increases. SPARQL endpoints conceptually
represent RPC-style, coarse-grained data access mechanisms. Nevertheless, through the potential interlinking of the
contained entities, SPARQL endpoints should be able to over distinct advantages over plain Web APIs. To our knowl-
edge, to date, there has been no study conducted that gauges the impact of the link on SPARQL query execution, espe-
cially in a federated set-up. In this paper, we investigate how the existence and types of typed links influences the exe-
cution characteristics of different SPARQL federation frameworks. In order to measure the query performance, we
propose a combined cost model based on a statistic analysis of the query performance metrics that involves parameters
such as type of link, the data catalogues and cache, number of links, and number of distinct subjects. As result, we show
that number of distinct subject and number of links have significant impact on Federation over SPARQL Endpoints
performance whereas type of link does not have significantly influence in federation query performance.
Keywords: SPARQL; Federation over SPARQL Endpoints
1. Introduction
Nowadays many data have been published in RDF for-
mat and connected each other by a link. This condition
encourages people to integrate data across dataset to
yield more valuable information. The easiest way to in-
tegrate data is employing links between dataset. Those
links can navigate us to data which has the same identity
or has relation each other. According to Linked Open
Data (LOD) Cloud statistics [1], more than 50% of data-
set in LOD cloud have more than 1000 out going links. It
indicates that a dataset publisher consider to put effort to
generate links. However, to the best of our knowledge,
there have been no study conducted to investigate the
benefit of link in linked data.
The higher number of links may cause usage of band-
width increases, but in the other hand, the data result can
be more retrieved easily. Moreover, the duration of ga-
thering data may take longer than usual. Besides the
number of link, the type of link may influence the per-
formance of query. The identity link such owl:sameAs
could answer more than relation link such rdf:seeAlso.
Thus, we observe carefully the impact of number of link
as well as type of link on query performance. Further, we
also identify the other factors that could have significant
impact on Federation over SPARQL Endpoint perform-
ance. For instance, an entity may have more than one
link which navigate to several different dataset. In this
case, the number of datasets involved could be one factor
to be considered. The more number of dataset involved,
the more number of request delivered is.
To submit a query, there are three type query inter-
faces to access data: SPARQL Endpoint, native reposi-
tory and HTTP request [2]. Since the SPARQL Endpoint
offers flexibility in term of formulating query, we focus
on the Federation over SPARQL Endpoints query per-
formance. In addition, it is also motivated by the beyond
emerging of SPARQL 1.11 which will support federation
query service. The federation features allows us to write
SPARQL query easier to gather data from various
SPARQL Endpoint.
To summarize, the primary contributions of our work
are the following:
To the best of our knowledge, no study has probed
the impact of link on federation SPARQL query. Our
observation shows the effect of the link on federation
query through the experimental as well as statistical
We also propose cost and benefit model in relation
with several of our observation key performance fac-
We conduct an investigation of the significant per-
formance factor in the Federation over SPARQL End-
opyright © 2013 SciRes. IJIDS
This paper is structured as follows: We review related
works in the Section 2. Section 3 gives an overview how
to write SPARQL query to integrate data from multiple
data sources. We investigate the cost and benefit model
and its variables, followed by our approach to construct a
cost model in Section 4. To build our cost model, we run
experiment in Section 5. We also validate our cost equa-
tion. Eventually, we conclude our work in Section 6.
2. Related Work
Decentralized data is nature of Linked Data infrastructure.
Crawling data in the single repository could not be cheap
solution in Linked Data as it requires much disk space to
store data and high system specification to process a
query [3]. To overcome this problem, several Linked
Data system that is similar to distributed database have
been developed recently. This system can be broken
down into Link Traversal, Federation over single reposi-
tories and Federation over SPARQL Endpoints. Link
Traversal [4] discovers related data by following the
HTTP Uniform Resource Identifier (URI). The complete-
ness is big issue in the Link Traversal system, therefore it
is not suitable for large scale system. Federation over
single repositories and SPARQL Endpoints use a media-
tor to deliver an incoming query to multiple data sources
and aggregate all the retrieved result. Accessing data in
the Federation over single repositories relies on native
API of the repository. To date, only a few of repository
systems provides this API. As stated in LOD Cloud sta-
tistic, 68.14% of data sources provides SPARQL End-
point. Therefore, in this work we only take into account
Federation over SPARQL Endpoints. SPARQL Endpoint
conceptually represents RPC-style, coarse-grained data
access mechanisms to execute SPARQL Protocol and
RDF Query Language (SPARQL)2 query that becomes a
standard query for Resource Description Framework
(RDF)3 data since 2008. There exists research addressed
to build federation over SPARQL Endpoint, namely:
Sesame Sail4, one of Sesame part in conjunction with
Alibaba5, allows multiple datasets to be virtually com-
bined into a single dataset. The performance of federa-
tion to execute complex query is poor since it sends
query to all datasource.
FedX [5] is addressed to deal with Federation SAIL
performance in federated query. To define relevant sour-
ce, it delivers ASK query before query processing. It
only submits sub query to the source that answers TRUE
value, in order to reduce the cost of communication. It
also applies exclusive group to cluster related sub queries
that have same query destination.
Splendid [6] is also extended from Sesame which em-
ploys VoID6 as data catalogue. Based on statistic in the
VoID, it calculates the cardinality function to detect the
relevant source for a sub query. Apart from cardinality
estimation, it sends ASK query if sub query destination
can not defined by cardinality estimation. Once the
source selection is done, it builds sub queries and join
order for optimization.
DARQ [7] is an extension of ARQ7, a well known
SPARQL query engine processor. Similar to Splendid, it
employs Service Description8 as its data catalogue to
specify the destination of sub query. The Service De-
scription contains data description and statistical infor-
mation has to declare in advance during setup phase.
To construct our cost model, we run our query set on
Federation Sail, FedX and Splendid since they are exten-
sion of Sesame Framework.
3. Data Integration in SPARQL Query
We discover three ways to integrate data in Federation
over SPARQL Endpoints by distinguishing the availabil-
ity of link among datasets.
3.1. No Link
The availability of link between two datasets allows us to
integrate their data, but there are some possibilities to
gather data among dataset without any links. First, we
can use UNION operator. In this way, the query result of
one datasource treats independently from other result, the
query processor only combines them before passing it to
user. Thus, this scheme is usually suitable to collect all
data that having the same behaviour but not identical.
Query 1 presents how to collect medicines for certain
disease from Drugbank9, Dailymed10 and Diseasome11
SPARQL Endpoints.
The other alternative of data integration without em-
ploying any links is object comparison. With regard to
sameness of data, we can compare several non URI ob-
ject of each predicate among dataset by defining them in
FILTER. The easiest way is only compare the rdfs:label
among data, however it may be inaccurate since the same
label does not mean the same data. Due to case sensitive
of SPARQL query, we must add REGEX in FILTER
condition. Thus, the cost query is more expensive. Con-
sider an example, Query 2 aims to find drug in Sider12
SPARQL Endpoint which is similar to Acetaminophen in
Drugbank SPARQL Endpoint. If the REGEX is removed,
Copyright © 2013 SciRes. IJIDS
PREFIX rdfs: <>
PREFIX drugbank:
PREFIX diseasome-disease:
PREFIX dailymed:
SELECT ?diseasename ?drugname WHERE {
?drug a a drugbank:drugs .
?drug rdfs:label ?drugname .
?drug drugbank:possibleDiseaseTarget diseasome-disease:1055 .
diseasome-disease:1055 rdfs:label ?diseasename .
?drug a dailymed:drugs .
?drug rdfs:label ?drugname .
?drug drugbank:possibleDiseaseTarget diseasome-disease:1055 .
diseasome-disease:1055 rdfs:label ?diseasename .
Query 1. Example of SPARQL Query to collect data from
multiple sources using UNION.
PREFIX rdfs: <>
PREFIX sider: <>
PREFIX drugbank-drug:
select *
drugbank-drug:DB00316 rdfs:label ?drugname .
?sider a sider:drugs .
?sider rdfs:label ?sidername .
FILTER REGEX(str(?sidername),str(?drugname),"i")
Query 2. Example of SPARQL Query to collect data from
multiple sources using REGEX.
the query will yield empty result because each label has
different case.
3.2. Reusing Identifier
To find related data, we can reuse URI identifier from
other party. In this situation, the datasets do not have link
between them directly, but both of them pose the same
URI object from other dataset that can join them. The
comparing indirect link is much better than comparing
non URI object in term of accuracy issue. For instance,
we pick one query from FedBench [2] (Query 3). This
query finds the relation of drug in Drugbank and KEGG
via drugban k:casReg istryNumb er which is an URI iden-
tifier of BioRDF-Cas dataset.
3.3. Link
Typically, a query in federation utilizes link to gather
data across dataset. This link can be generated manually
and automatically by a tool such SILK [8] and Limes [9].
Those tools produce a set of links from two dataset as
defined in the link specification. Having links both iden-
tity and relationship make data can be integrated in
straight way. By employing diseasome:possibleDrug,
Query 1 can be altered by Query 4.
By having a link between two datasets, the query cost
can be cheaper while number of distinct outgoing data-
sets is not too high. The high number of distinct outgoing
datasets leads the number of requests to other dataset
increases. Consequently, the query mediator needs longer
time to process a query. For better explanation, given an
example Query 5 which purposes to gather all drug that
are as same as drug in Drugbank via owl:sameAs. Ac-
cording to Drugbank dataset, each owl:sameAs in entity
drugbank:drugs could have four distinct outgoing data-
sets such as DBpedia, ce., www4.wiwiss.fu-berlin .de/sider
and If we assume all subjects have
exactly four distinct datasets, the query mediator will
send four requests for each subject. Therefore, the high-
est number of request is four times number of subjects.
PREFIX drugbank:
PREFIX drugbank-cat:
PREFIX kegg: <>
PREFIX purl: <>
SELECT ?drug ?title WHERE {
?drug drugbank:drugCategory drugbank-cat:micronutrient> .
?drug drugbank:casRegistryNumber ?id .
?keggDrug a kegg:Drug .
?keggDrug kegg:xRef ?id .
?keggDrug purl:title ?title .
Query 3. Example of SPARQL Query to collect data from
multiple sources using Reusing Identifier.
PREFIX rdfs: <>
PREFIX diseasome:
PREFIX diseasome-disease:
SELECT ?diseasename ?drugname {
diseasome-disease:1055 diseasome:possibleDrug ?drug .
?drug rdfs:label ?drugname .
diseasome-disease:1055 rdfs:label ?diseasename .
Query 4. Example of Federation SPARQL Query by using
PREFIX drugbank:
PREFIX rdfs: <>
PREFIX owl: <>
Select * {
?drug a drugbank:drugs .
?drug owl:sameAs ?other .
?other rdfs:label ?name .
Query 5. Example of Federation Sparql Query by using
Copyright © 2013 SciRes. IJIDS
4. Cost and Benefit Model
As we described in Section 3, the unavailability of link
causes user has to map data from one data source to other
source. Consequently, consumer puts totally effort in
data integration. If the publisher generates link to connect
its data to other dataset, the data integration cost is shared
between consumer and publisher. In order to be measur-
able, we develop cost model for consumer as outcome of
federation query performance metric.
4.1. Cost Variables
1) Bandwidth Usage (BU)
In order to measure networking cost, we only calculate
total of uplink and downlink bandwidth during query
execution between framework and SPARQL Endpoints.
We ignore the bandwidth usage from user to framework.
2) Number of Requests (RQ)
The bandwidth usage depends on amount of data
transmission. Therefore, it can not present number of
request from framework to SPARQL Endpoint. A query
may have more than one request to complete the result.
In our experiment, we measure number of requests that
refers to number of submission of sub query to each
SPARQL endpoint.
3) Response Time (T)
How responsive system to respond a query need to be
evaluated in federated query. The response time is de-
fined as how long it takes time from a query generated to
result retrieved.
4.2. Benefit Variables
By categorizing the availability of link of data integration
in Federated over SPARQL Endpoints, we define benefit
and cost that arise as result of the existence of link as
well as the type of link. Hartig [4] proposed query execu-
tion time as cost and number of result as benefit in Link
transversal. In the Link Traversal environment, the data
knowledge is hard to know in advance and the data could
be change dynamically. Contrast to Link Traversal envi-
ronment, the data in the Federation over SPARQL End-
points can be observed before query execution. Therefore,
we consider query completeness and soundness as bene-
fit instead of number of result. The high number of result
does not mean better result because that might be redun-
dant result or invalid result. The query completeness is
defined as number of true answer that is stored in dataset,
whereas the query soundness refers to number of retri-
eved of true answer. We adopt completeness and sound-
ness metric combination from LUBM [10]. Let Sq be
Soundness of query result, Cq be Completeness of Query
and β be weight between Cq and Sq, then Fmeasure of query
completeness and soundness Fq is defined as follow:
F= βC+S
4.3. Multiple Regression Model
Multiple Regression model is common way to present
relation of cost with its parameters. The cost acts as de-
pendent variable that its value is depend on certain inde-
pendent variables. There are two kinds of regression
models : linear and non linear.
1) Linear Regression Model
Given y as dependent variable, x1, x2, ... xn as n inde-
pendent variable and c0, c1, … cn-1 as coefficient of re-
gression, the multiple regression linear is
01 2n
y=c+c x +cx++cx
In this model, dependent variable has linear correlation
with each independent variables. Further, the good linear
model should have the coefficient of determinant (R2)
close to one. R2 represents the correlation between actual
and predicted dependent variable which is described by
following formula:
xy y
R= Kδδ
where K is the number of samples,
are mean
of x and y respectively,
δ is the standard deviation of x,
δ is the standard deviation of y.
2) Non Linear Regression Model
If the majority of independent variables could not ful-
fil linear correlation and the R2 close to zero, we must
transform it to non linear model. There are many form of
non linear regression models such exponential, power,
polynomial, trigonometric, etc. Non linear regression
model is more complicated than linear regression model
because the function is built from trial and error. In this
paper, we endeavor to build our formula in exponential
and power model. Let Cn be ln(cn), the exponential model
can be written in the following equation:
where as the power model is explained in the following
y=C xxx
The coefficient of regression is estimated statistically
from sample experiment of known independent and de-
pendent variables. To obtain those coefficients, we define
following independent variables:
1) Type of Link (TL)
Copyright © 2013 SciRes. IJIDS
Copyright © 2013 SciRes. IJIDS
[11] distinguishes the link into three categories: Rela-
tionship Link, Identity Link and Vocabulary Link. We
only take account Relationship and Identity link in our
model since we do not integrate data with its vocabulary.
To observe the effect of link type, we define TL as one of
cost parameter which equals 0 and 1 for Relationship
Links and Identity Links respectively.
larger number of distinct subject is, the more time needed
to execute a query is.
5) Number of Distinct Outgoing Datasets (DD)
Without data catalogue, a framework queries blindly
to each dataset. Thus, the number of outgoing dataset
might not influence performance. The number of outgo-
ing dataset has a significant impact on framework with
catalogue because the number of request is limited by the
number of outgoing dataset.
2) Data Catalogue and Cache Benefit (DC)
By having a catalogue and cache, federation frame-
work can reduce the request of the existence of data.
Thus, we consider this as important factor in federation
query performance. Through our observation, applying
caching scheme during execution make framework per-
form better after second running. The second better per-
formance is framework that have a data catalogue. Hence,
we define the level of range of DC value in Table 1.
For each model (linear and non linear), we create 25
6 combination of independent variables that influence the
value of cost. The independent variable can be eliminated
and added in the model. Note that, the model must have
at least two independent variables. Finally, the model
with highest R2 is chosen as our model.
Once we obtain the best model, we decide the signifi-
cance of independent variable by calculating Pvalue of
independent variable using T Test of null hypothesis. If
the Pvalue is smaller than 0.05, we accept the independent
variable has significant impact on federation query per-
3) Number of Links (NL)
The higher number of link is, the higher the number of
request is because the framework has to ask to SPARQL
Endpoint as amount of link.
4) Number of Distinct Subject (DS)
The number of distinct subject which is involved in a
query is also considered as key performance factor. The 5. Experiment
5.1. Environment
Table 1. Data catalogue and cache benefit value.
Framework Features Value
Data Catalogue and Cache 1
Cache 0.75
Data Catalogue 0.5
Data Catalogue and Cache 0.25
1) Dataset
As proof of concept, we run queries in four datasets,
namely Sider, Diseasesome, Dailymed and Drugbank.
Those datasets are chosen because there exist links
among of them and comprises identity and relationship
type as illustrated in Figure 1. For more details of statis-
tic of related links in dataset, refer to Table 2. To provide
Figure 1. The Relation of drugbank, disease, dailymed and sider dataset.
Table 2. Dataset statistic.
Dataset Distinct
Outgoing Link
Outgoing Dataset Triples Links
Dailymed 3 8 164,27639,635
Drugbank 14 18 766,92056,958
Disease 6 10 91,18231,750
Sider 2 7 193,24920,294
SPARQL Endpoints, we install four Fuseki13 as Endpoint
service for each dataset.
2) Query Set
The query set should cover wide range of all parame-
ters but it should be in general form, in order to compare
among queries performances fairly. Our query set com-
prises 90 queries which is not included operators and
operands since we only consider the usability of link to
improve federated query performance. With respect to
data integration category in Section 3, we set up query to
cover all categories. But we could not find any query
pattern for Reusing Identifier category in our dataset.
3) Federation Framework
To evaluate the query performance, we choose three
frameworks, namely FedX, Sesame Sail and Splendid.
All of them are built on top of Sesame framework. FedX
and Sesame Sail represent framework without data cata-
logue whereas Splendid represents framework having
data catalogue. The frameworks and SPARQL Endpoints
are installed in a Linux virtual machine.
5.2. Result
In total, we should have 240 results but 28 queries are
failed execution because the query execution time ex-
ceeds the time out duration (one hour) or the federation
query framework does not support such query. For ex-
ample, Splendid could not execute no link query pattern
because it binds the same address while comparing the
literal value. Although we increase the time out limit to 3
hours, only two no link queries can be processed suc-
cessfully by Sesame Sail and FedX. Hence, we exclude
all the no link query result to build our model. Based on
no link query result, the existence of link can boost fed-
eration query performance.
The average of Fmeasure of completeness and soundness
is 9.67. It implies that all independent parameters do not
influence the query completeness and soundness result.
All the framework accomplish to execute query even
though the performance is poor.
The linear regression model is our first fitting cost
model. Table 3 depicts low coefficient of determination
(R2 adjustment) in the linear regression model. Obvi-
ously, the independent and dependent variables have
little linear correlation. As described in Table 4, the bet-
ter result is obtained from Exponential Regression Model
which the R2 adjustment is nearly 50% or above. Ac-
cording to our null hypothesis, the Exponential Regres-
sion model contains only DD and DS as its significant
Table 5 shows that the value of R2 Adjustment of
power model equation surpasses the value of exponential
model in all cost. Therefore, we choose this model as our
cost model. Number of distinct subjects (DS) signifi-
cantly contribute in all cost. On the other hand, the type
of link is not significantly related with query federation
performance because the framework treats all the type in
the same way.
With respect to the power regression calculation, we
obtain the value of each coefficient parameters that will
be inserted to the cost formula. As written in formula
Figure 2, it can be noted that not all the parameters is
included in the formula.
0.180.17 0.350.14
0.063 0.470.82
0.11 0.680.42
Figure 2. Multiple power regression model for federation
over SPARQL endpoints cost model.
Table 3. R2 adjustment linear model.
Cost Independent
Variable R2 Adjustment Significant
T DS DC 2.1% DS
BU DS DD NL 48.54% DS NL
Table 4. R2 adjustment exponential model.
Cost Independent
Variable R2 Adjustment Significant
Table 5. R2 adjustment power model.
Cost Independent
Variable R2 Adjustment Significant
T DS NL DC 71.58% DS NL
Copyright © 2013 SciRes. IJIDS
5.3. Cross Validation
Once the cost formula is created, we conduct new eval-
uation as cross validation. The new evaluation compos-
ing new 10 queries is also executed on three federation
frameworks. Eventually, we calculate the relative error
produced by estimation equations for each query as
shown in the following formula:
where RE is relative error, V' is estimated value and V is
actual value.
The result of validation can be found at Figures 3, 4
and 5. 50% of estimated response time value has error
smaller than 50%. Bandwidth usage validation result
shows that more than 57.14% of validation value has RE
Figure 3. Estimated and actual bandwidth usage.
Figure 4. Estimated and actual response time.
Figure 5. Estimated and actual number of requests.
less than 30%. The most promising result is the number
of request which 71.42% of estimated value is less than
30% RE. In general, the RE tends to be high when num-
ber of links and number of distinct subjects are high. As
illustrated in Figure 1, we only have 11 links that con-
nect two entities in our dataset. Given that, we only can
generate 9 queries for building our model and 2 queries
for validation dealing with the high of number of link
and distinct subject. This number is too small comparing
to total of query. Hence, our model is not suitable for
high number of links and number of distinct subjects.
6. Conclusions
We presented an investigation of impact of the existence
of link in Federation over SPARQL Endpoints perform-
ance. In order to calculate the query performance, we
proposed a cost model in multiple linear and non linear
regression form. In addition, we also formalized benefit
model by measuring completeness and soundness value.
We defined type of link, number of outgoing datasets,
number of links, number of distinct subjects and data
catalogue benefit as the independent variables to build
our cost and benefit model. Those independent variables
can determine the estimation of response time, band-
width usage and number of request as metric of as a re-
sult, FMeasure of soundness and completeness value closes
to 1 which indicates all independent variables do not in-
fluence the query completeness and soundness result.
Based on coefficient of determination calculation, not all
parameters can be inserted in the cost model. Moreover,
the power regression model is more fitted for building
the cost model than linear regression and exponential
models. Hence, we construct cost model based on power
regression model. With respect to the significance of pa-
rameter, number of distinct subjects and number of links
have significant impact on Federation over SPARQL
Copyright © 2013 SciRes. IJIDS
Copyright © 2013 SciRes. IJIDS
Endpoints performance.
By analysing the failure of no link query pattern, we
found that joining data across different dataset without
using a link need large resource such as bandwitdth. This
failure is caused by the complexity of no link query pat-
tern and high number of object comparisons. Thus, the
existence of link can boost federation query performance.
Further, we proved that the type of link does not influ-
ence the Federation over SPARQL Endpoints perform-
ance since the federation framework treats link like other
RDF predicate. The type of link could have more impact
for analysing the coverage of link in term of answering
7. Acknowledgment
We acknowledge Dr. Michael Hausenblas and Dr. Mar-
cel Karnstedt for supervising author during this research.
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