Efﬁcient Exploration of Linked Data Cloud

Jakub Klímek

, Martin Ne

caský

, Bogdan Kostov

, Miroslav Blaško

and Petr K

remen

Faculty of Mathematics and Physics, Charles University in Prague, Praha, Czech Republic

Faculty of Electrical Engineering, Czech Technical University in Prague, Praha, Czech Republic

Keywords:

Linked Data, RDF, SPARQL, Exploration, Visualization.

Abstract:

As the size of semantic data available as Linked Open Data (LOD) increases, the demand for methods for

automated exploration of data sets grows as well. A data consumer needs to search for data sets meeting

his interest and look into them using suitable visualization techniques to check whether the data sets are

useful or not. In the recent years, particular advances have been made in the ﬁeld, e.g., automated ontology

matching techniques or LOD visualization platforms. However, an integrated approach to LOD exploration is

still missing. On the scale of the whole web, the current approaches allow a user to discover data sets using

keywords or manually through large data catalogs. Existing visualization techniques presume that a data set

is of an expected type and structure. The aim of this position paper is to show the need for time and space

efﬁcient techniques for discovery of previously unknown LOD data sets on the base of a consumer’s interest

and their automated visualization which we address in our ongoing work.

1 INTRODUCTION

Recently, the amount of semantic data available on

the Web has increased dramatically, especially thanks

to the Linked Open Data initiative (LOD) (Heath and

Bizer, 2011) which builds the so called LOD cloud.

The LOD cloud is a large distributed ecosystem of

interlinked semantic data. It is getting bigger every

year – it has grown from 300 data sets in 2011 to

more than 1000 data sets in 2014 which contain ap-

prox. 100 billions of entries

. Originally, LOD was

hoped to become the ﬁrst successful realization of the

semantic web (Berners-Lee et al., 2001). It involves

semantically integrated data cloud capable of expres-

sive queries for data search. However, the current

LOD cloud has low quality and the established links

among data entities are often too conservative to al-

low integrated semantic exploration. Combined with

the rising amount of data, it becomes clear that the

LOD in their current form are still hard to explore,

leaving the semantic integration responsibility to their

consumers (Jain et al., 2010). Due to this fact, the

LOD cloud is not ready for direct crawling/exploring

of the data (see e.g. LDSpider (Isele et al., 2010)).

On the other hand, typical data consumers (develop-

ers, data analysts, etc.) usually do not need to search

for particular data directly. More often, they need to

http://lod-cloud.net/

ﬁnd data sets which contain potentially relevant data

for their application or data analysis. For example, a

consumer may need to ﬁnd statistical data sets which

measure demography indexes in regions of the Czech

Republic. In particular, a consumer needs a service

which enables him to express his interest (i.e. the

speciﬁcation of data sets he looks for) and returns a

(small enough) set of data sets which correspond to

the interest. He also needs to understand interrela-

tions of the found data sets using a proper visualiza-

tion of those interrelations. He then needs to evaluate

their relevance by means of their proper visualization

and exploring their content. Often, after he obtains

the visualized content, he needs to reﬁne his interest

and repeat the exploration steps. In other words, the

service must allow him to work in iterations.

We call the process of searching for data sets the

LOD exploration and the service which enables it the

LOD exploration service. To enable efﬁcient LOD

exploration, three fundamental questions must be an-

swered. (1) In which form the data consumer should

formulate his interest? (2) How a (small enough) set

of data sets corresponding to the consumer’s interest

should be found on the base of the formulated inter-

est? (3) How should the service offer a proper visu-

alization of the found data sets so that the consumer

can look inside and decide whether he needs them or

not?

Klímek J., Ne

caský M., Kostov B., Blaško M. and K

remen P..

Efﬁcient Exploration of Linked Data Cloud.

DOI: 10.5220/0005558002550261

In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 255-261

ISBN: 978-989-758-103-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

1.1 Motivating Example

Let us look at the process of LOD exploration from

the consumer’s point of view. Let us say that a con-

sumer builds a web application which deals with pub-

lic institutions in the EU. Therefore, his interest is to

explore the LOD cloud in order to ﬁnd suitable data

sets with data about public institutions in the EU. He

expresses this interest and provides it to the LOD ex-

ploration service. The service returns a set of data

sets which potentially meet the interest. The data sets

are annotated with a relevance with respect to the in-

terest. The consumer picks several relevant data sets

and the service chooses their appropriate visualization

automatically. Moreover, the service informs the con-

sumer that it could be possible to better visualize the

institutions on a map but a data set with a mapping

of addresses of public institutions to GPS coordinates

is needed. The consumer likes this idea and extends

his interest with a requirement for such data set. This

starts a new iteration of the exploration process. The

service discovers new data sets with GPS coordinates

and narrows down the previously discovered data sets

with public institutions – only those which are linked

to some new found data set with GPS coordinates re-

main in the result. Moreover, it provides a visualiza-

tion of public institutions in the data sets on a map

using the GPS coordinates. The current approaches

require the consumer to know the data sets in advance

and create the visualization manually. It is not possi-

ble to discover and visualize the data sets in a dynamic

way described above.

1.2 Purpose of Our Work

We see a need for a formal foundation of the LOD ex-

ploration service. In particular, a formal model of the

LOD exploration service needs to be deﬁned, includ-

ing its expected features and behavior. A formalism

for expressing consumer’s interest and speciﬁcation

of available visualization techniques will be part of

this model. We aim to develop time and space ef-

ﬁcient algorithms for matching consumer’s interest

against the LOD cloud and algorithms for matching

the discovered data sets with available visualization

techniques. The importance of the proposed problem

lies in the fact that there are currently many initia-

tives (research, governmental, driven by private sec-

tor) which publish their data sets in the LOD cloud.

All of them expect that wide audience of data ana-

lysts and application developers will exploit the pub-

lished data sets in various unexpected ways. How-

ever, the complexity of the LOD exploration signiﬁ-

cantly reduces the possible beneﬁts of the LOD cloud

and wastes the investments made to the LOD cloud by

the publishers. The LOD exploration service will en-

able consumers to ﬁnd the right data sets to fulﬁll their

needs much more easily. In this paper we describe our

ongoing work and our aims for the near future.

2 STATE OF THE ART

Recent survey by Marie et al. (Marie and Gandon,

2015) splits evolution of Linked Data based explo-

ration research into multiple phases represented by

corresponding exploration systems – browsers, rec-

ommenders and exploratory search systems (ESS). In

the early phases of the semantic web (2001-2007),

browsing paradigms convenient for small size and

relatively homogeneous data sets were investigated

– e.g. text-based browsers inspired by classical

web browsers, visualization-based (graph) browsers,

and faceted browsers. Later, LOD (2007) elevated

new browsing paradigms reﬂecting higher quality,

rising size and wide domain coverage of the DB-

Pedia and Freebase data sets. One of the ﬁrst

Linked Data browsers is Explorator (Araujo et al.,

2009). Subsequently, other browsers appeared, like

Hide the stack (Dadzie et al., 2011) (SPARQL query

templates-based), or rdf:SynopsViz (Bikakis et al.,

2014) (faceted-browser generator based on SPARQL

endpoints analysis). Another exploration tool is Free-

base Parallax

, which offers advanced visualizations

like timelines, or maps, but works with a ﬁxed data

source – Freebase. Semaplorer (Schenk et al., 2009)

is a tourist domain exploration mashup based on mul-

tiple large data sets providing 4 different types of

facets (person, time, tag and location). Facete is a

JavaScript SPARQL-based faceted search library for

an arbitrary SPARQL endpoint. The next shift ac-

cording to Marie et al. were recommenders that ex-

pose computed links like intra-domain, resp. cross-

domain semantic similarities. The most recent re-

search concentrates on ESS, which extends recom-

menders with more advanced functionalities. Marie

et al. differentiate between a view-based ESS (allows

user to deﬁne views on the data set) and an algorithm-

based ESS (uses semantics to select a small amount of

computed relations such as similarity or relatedness

instead of the original graph).

Neither of the discussed exploration systems is

capable of realizing the use case described in Sec-

tion 1.1, being unable to provide discovery and vi-

sualization on the data set level. Particular techniques

for data set discovery and visualization relevant to our

http://parallax.freebaseapps.com

work are surveyed in sections 2.1 and 2.2. Section 2.3

describes our previous work on the Linked Data Vi-

sualization Model (LDVM) which we will extended

within the exploration scenario.

2.1 Data Set Discovery Techniques

W3C addressed the problem of dataset discoverabil-

ity by issuing the Data Catalog Vocabulary (DCV) as

a W3C Recommendation (Erickson and Maali, 2014)

in 2014. It provides a common vocabulary for data

cataloging tools, like CKAN, backing e.g. datahub.io.

The LOD data sets are registered within such data cat-

alogs, which are in turn registered within data catalog

registries such as dataportals.org. The lack of data

catalogs for large amount (Rakhmawati et al., 2013)

of datasources is addressed by data indexers, e.g.,

Sindice

. Each data catalog and data indexer provides

metadata (description) for data sets which can help in

data set discovery. In addition to DCV, there are other

vocabularies such as VoID(Cyganiak et al., 2011) for

describing metadata of the data set and its relations to

other data sets, LOV

for describing ontologies that

are used by the data set and VOAF

, extending LOV

with relationships to topic classiﬁcations.

A survey of systems using also statistical data can

be found in (Rakhmawati et al., 2013). E.g., Sindice’s

vocabulary for data sets and data set analytics

de-

scribes statistics about usage of classes and predicates

as well as their connections. In (Nikolov et al., 2012)

a semantic web index is utilized in order to identify

relevant data sets for interlinking and ranking them.

However, the current data set descriptions ignore on-

tological commitments of the contained data and lack

ontological foundation of different data set descrip-

tions together with user interests. During data set dis-

covery, ontology matching techniques (Shvaiko and

Euzenat, 2013) can help in producing links among

data sets. The paper (Leme et al., 2013) introduces

probabilistic exploration that looks for candidate data

sets for input ontologies based on probabilistic clas-

siﬁers. Data sets are ranked according to the prob-

ability that links between input ontologies and can-

didate data sets can be found. Another approach

(de Oliveira et al., 2012) uses application speciﬁc

SPARQL queries and user’s feedback to ﬁlter and

rank relevant data sets.

The data set discovery scenario can be ideally im-

plemented as a query answering service over an in-

tegrated data set of data set descriptions. There are

http://sindice.com/

http://lov.okfn.org/

http://lov.okfn.org/vocommons/voaf/

http://vocab.sindice.net/

numerous techniques and tools that handle query an-

swering over a single data set, focusing on the trade-

off between expressiveness and computational com-

plexity. OWL 2 RL, QL and EL proﬁles (Motik

et al., 2009) are designed for tractable reasoning and

query answering, e.g. for Ontology Based Data Ac-

cess and Integration. Query engines for these proﬁles

are implemented in Stardog (Clark and Sirin, 2013)

and GraphDB (Ontotext, 2014). Querying more ex-

pressive ontologies is supported by in-memory query

evaluation engines like Pellet (Sirin et al., 2007), or

OWL2Query (Kremen and Kostov, 2012) augmenting

SPARQL conjunctive queries with OWL DL seman-

tics. Due to the model checking, these engines ﬁght

with high time complexity (NEXPTime) and memory

consumption.

2.2 Data Set Visualization Techniques

More and more projects are focused on visualizing

Linked Data. A detailed survey of Linked Data visu-

alization techniques is by Dadzie and Rowe (Dadzie

and Rowe, 2011). There are several tools that visual-

ize data based on speciﬁc vocabularies. map4rdf sup-

ports faceted discovery of Spanish institutions and en-

ables the user to add a speciﬁc overlay containing sta-

tistical SCOVO-based data in a form of a timeline vi-

sualization. LinkedGeoData browser enables its users

to explore points of interest all over the world. Cube-

Viz (Ermilov et al., 2013) offers sophisticated Dat-

aCube vocabulary visualizations. FoaF Explorer

focused on visualizing FOAF proﬁles. ViDaX (Du-

mas et al., 2012) is a Java desktop Linked Data vi-

sualizer based on the Prefuse

visualization library.

Based on ontologies and property types, it suggest

suitable visualizations to its users. Tools like IsaViz

(Pietriga, 2002), Fenﬁre (Hastrup et al., 2008) and

RDF–Gravity

use the well-known node-link visual-

ization technique to represent a data set. IsaVis also

belongs to a group of tools implementing Fresnel -

Display Vocabulary for RDF

, which speciﬁes how

a resource should be visually represented by Fresnel-

compliant tools.

2.3 Linked Data Visualization Model

Let us brieﬂy describe the Linked Data Visualization

Model (LDVM) which we deﬁned in our previous

work (Brunetti et al., 2013; Klímek et al., 2014). It

is an abstract visualization process customized for the

http://xml.mfd-consult.dk/foaf/explorer/

http://prefuse.org/

http://semweb.salzburgresearch.at/apps/rdf-gravity/

http://www.w3.org/2005/04/fresnel-info/

Figure 1: Linked Data Set Exploration Scenario.

speciﬁcs of Linked Data. In short, LDVM allows

users to create data visualization pipelines that con-

sist of four stages: Source Data, Analytical Abstrac-

tion, Visualization Abstraction and View. The aim

of LDVM is to provide means of creating reusable

components at each stage that can be put together to

create a pipeline even by non-expert users who do

not know RDF. The idea is to let expert users to cre-

ate the components by conﬁguring generic ones with

proper SPARQL queries and vocabulary transforma-

tions. In addition, the components are conﬁgured in

a way that allows the LDVM implementation to au-

tomatically check whether two components are com-

patible or not. If two components are compatible,

then the output of one can be connected to the input

of the other in a meaningful way. With these com-

ponents and the compatibility checking mechanism

in place, the visualization pipelines can then be cre-

ated by non-expert users. What is missing is a proper

ontological model for description of the components

and their bindings, expressive component compati-

bility descriptors and efﬁcient compatibility checking

algorithms that would scale to the magnitude of the

LOD cloud. There are also recent approaches similar

to LDVM. LDVizWiz (Atemezing and Troncy, 2014)

is a LDVM-like approach to detecting categories of

data in given SPARQL endpoints and extracting ba-

sic information about entities in those categories. A

lightweight application of LDVM in enterprise is de-

scribed in LinDa (Thellmann et al., 2014).

3 IDENTIFIED OBJECTIVES

We aim at deﬁning a formal model of the LOD ex-

ploration scenario and corresponding service depicted

in Figure 1. The scenario consists of two fundamental

activities:

Discover. A user provides a description of his inter-

ests. This is an approximation of questions he

needs to answer by combining some datasets, un-

known to him in advance. The LOD exploration

service provides a set of datasets corresponding

to the description. Here, we integrate and further

extend approaches mentioned in Section 2.1.

Visualize. The LOD exploration service automat-

ically chooses visualizations suitable for the

datasets discovered in the previous step and offers

the visualizations to the user who can view the

datasets from different viewpoints (different suit-

able visualizations) and choose datasets that best

suit his interests. Here we integrate and further

extend approaches mentioned in Sections 2.2 and

2.3.

Our approach can be classiﬁed as algorithm-based

exploratory search in the data set space. To cover

the described LOD exploration scenario, we aim at

achieving three objectives in our work.

3.1 Objective 1: Efﬁcient Dataset

Discovery in LOD Cloud

The ﬁrst objective is to design techniques and algo-

rithms for data set discovery in LOD, improving se-

lectivity and relevance comparing to current (typi-

cally keyword-based) techniques, as discussed in Sec-

tion 2.1. In particular, we will address the following

questions:

• How to improve Linked Data Set Discovery

(LDSD) ?

• How to formalize user interests for the purpose of

LDSD ?

• What are the missing data set characteristics suit-

able for LDSD and what is their nature (syntacti-

cal, structural, ontological) ?

• How efﬁcient would it be to equip data sets with

the new characteristics ? Which characteristics

can be created automatically ?

To achieve this we consider the scenario depicted

in Figure 2. First, we analyze the dataset typol-

ogy of the LOD cloud, formalize the dataset dis-

covery scenario in terms of newly designed ontolog-

ical model of datasets, and deﬁne related user in-

terest queries. The designed ontological model al-

lows us to research and identify practically applicable

data set descriptors taking into account their correct-

ness/completeness/accuracy, minimal descriptor size,

maximal information capacity, user-intuitiveness, as

well as minimal resource consumption during cre-

ation. Dataset descriptors of different nature will be

Figure 2: Linked Data Set Discovery Scenario.

considered, involving e.g. meta-data schemas, data

set statistics, data set subset w.r.t. a top-level ontol-

ogy, or extracted keyword list. The model will be di-

vided into two parts. The ﬁrst part is a general de-

scription of the LOD data sets and other entities such

as people, organizations and their relationships and

roles such as data set publisher and producer. This

model will be based on an analysis of the LOD cloud

and related data set typology. The second part of the

model describes a LOD data set from the perspective

of the data set discovery, involving the user interest

and data set descriptor model. This model will be

based on a conceptualization of data set cataloging

techniques, as well as ontology-based data set inte-

gration and ontology alignment techniques covered in

Section 2.1. This portfolio of techniques will allow

us to deﬁne descriptors of different nature in order

to capture fundamental intra-dataset and inter-dataset

semantic relationships.

Our next goal is to design the data set discov-

ery algorithm that selects a relevant small set of

data set descriptors according to the user interests.

To achieve this, the discovery algorithm will make

use of a unique combination of expressive ontolog-

ical queries and ontology alignment techniques tak-

ing into account different nature of the designed de-

scriptors. Currently, the most theoretical research fo-

cuses on individual features, see Section 2.1. We

will concentrate on the theoretical analysis leading

towards the design and development of tractable and

scalable algorithms for suitable combinations of these

features. Our previous work (Kremen and Kostov,

2012) already supports SPARQL conjunctive queries

using OWL DL 2 entailment regime. We will extend

it to support features reﬂecting the requirements of

the user interest model, and design suitable optimiza-

tion techniques. This involves e.g. identiﬁcation and

grouping of potentially complement (w.r.t. the user

interest) data sets in the search result, or ordering the

data sets in the search result according to user inter-

ests match.

3.2 Objective 2: Dynamic Dataset

Visualization

Once the user discovers a reasonable number of data

sets using techniques from the previous objective, he

wants to see a visualization of the discovery results

(see Figure 1). Such discovery result visualization

should present the discovered data sets and relation-

ships among them, together with their mapping to

the expressed interest. This helps the consumer with

deciding which data sets should be explored further.

Next, the user needs to look inside the discovered data

sets and decide whether he can use them to achieve his

goals. The objective is to propose a dynamic data set

visualization approach which identiﬁes suitable visu-

alizations of discovered data sets and presents them to

the user. The suitable visualizations need to be identi-

ﬁed automatically and on demand based on the char-

acteristics of the discovered data sets and available vi-

sualization components. There is no static mapping

between a data set and a visualization. For each vi-

sualizer, the developed ontology model will describe

its inputs, each associated with data structures needed

for producing the desired visualization, and the kind

of visualization the visualizer produces. The formal

speciﬁcation of available visualizers will enable our

algorithms to present visualizers suitable for discov-

ered data sets to the user and inform him about the

kinds of visualizations he can obtain and what addi-

tional data sets must be discovered for their full func-

tionality (see Section 1.1 for an example). To achieve

this, the algorithms will analyze the descriptors of the

discovered data sets, available transformers and visu-

alizers and generate their binding (so called visual-

ization pipeline) that leads to the visualization. The

model will also conceptualize possible kinds of rela-

tionships between discovered data sets and the user

interest. Without such technique, the user would have

to identify the pairings between data sets and visual-

izers and build the visualization pipelines manually.

One additional problem needs to be solved to

achieve this objective. It can be useful to apply a

given visualization component to a certain data set

and yet the descriptors of both can mismatch and

therefore the pairing will not be identiﬁed. To ad-

dress this problem we will extend the solution with

components able to transform data structures to other

data structures. The approach will be based on and

will extend our former LDVM approach described in

Section 2.3.

3.3 Objective 3: Experimental

Evaluation of the Methods

The last objective is to evaluate achievements of Ob-

jectives 1 and 2 in a real environment. The achieve-

ments will be implemented in a form of a prototypical

LOD exploration service and evaluated on two sce-

narios. First, we will evaluate the prototype on the

Czech LOD cloud, involving data sets published by

the OpenData.cz initiative as well as data sets created

by other national projects. Although it contains just

10s of data sets with approx. 1 billion of records, their

users still face problems addressed by our work. Our

familiarity with the local Czech LOD cloud allows us

to deﬁne expected behavior of the evaluated LOD ex-

ploration service and compare its real behavior to the

expected one. Second, we will evaluate the prototype

on a representative part of the whole LOD cloud. This

evaluation will be on the data sets which are not un-

der our control and come from different sources. It

will enable us to experimentally evaluate the achieve-

ments and demonstrate situations of data consumers

who do not know the explored data sets in advance.

The prototype will consist of a data set descriptor

storage and an implementation of the designed algo-

rithms in the form of a query engine. The storage

will be realized using existing OWL 2 databases, like

GraphDB, or Stardog, see Section 2.1. We will pop-

ulate the storage with descriptors which will be cre-

ated manually or automatically, based on the descrip-

tor type and the quality of data set and its metadata.

We will evaluate descriptor characteristics like com-

pleteness/accuracy, etc. (see Section 3.1). The proto-

type implementation will also include the developed

algorithms for dynamic data set visualization. Partic-

ular visualizers needed to demonstrate usefulness of

our approach on a selected set of LOD cloud data sets

will not be developed from scratch – existing visual-

ization libraries, e.g. D3.js, will be reused. Using the

prototype implementation, we will verify that the user

can comfortably view the results of data set discovery

and gain a visual overview of each one that will enable

him to decide whether the offered data set is useful.

4 CONCLUSIONS

In this paper we described our ongoing work towards

a Linked Data exploration service, which will also in-

clude visual insights into the discovered data sets. We

aim at supporting users in their search for usable data

sets in the Linked Data Cloud as we can currently see

that this problem is becoming increasingly important.

We base our approach on formal ontological model of

data sets and on algorithms for automatic matching of

user requirements and data sets.

ACKNOWLEDGEMENTS

This work was partially supported by a grant from the

European Union’s 7th Framework Programme num-

ber 611358 provided for the project COMSODE.

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