Describing Linked Datasets
On the Design and Usage of voiD, the “Vocabulary Of Interlinked Datasets”
Keith Alexander∗†

Richard Cyganiak

Talis Ltd.

DERI, National University of
Ireland, Galway‡

Jun Zhao

DERI, National University of
Ireland, Galway

Department of Zoology,
University of Oxford

ABSTRACTIn this paper we discuss the design and implementation of
voiD, the “Vocabulary Of Interlinked Datasets”, a vocabulary that allows to formally describe linked RDF datasets.
We report on use cases for voiD, the current state of thespecification and its potential applications in the context oflinked datasets.

1.

§

Michael Hausenblas

INTRODUCTION

With the growth of the number of linked datasets [12],automating certain tasks, such as discovery, selection andoptimisation, becomes more and more important. Now, one
might argue that URIs and RDF [17] are all one needs to explore the linked datasets; follow-your-nose1 , however bears
some inherent problems. The possible links that can be followed from a starting URI raises both performance and trust
issues. The main reason for these issues lies in the granularity level of the available descriptions. Additionally, thedynamics of the data-sources [13] also has an impact on theperformance of, say, a crawl over a collection of datasets;
and the reliability of secondary data resources [26].
In the early days of linked data (2006 and 2007) [5] themain focus of the community was on publishing data andfinding good practices. Now, in the second phase, otherissues such as usability, quality, performance, reliability of
the infrastructure and the data in the linked data ecosystem
are increasingly recognized to be important.
How can we overcome the limitations of follow-your-nose
while retaining the self-descriptive momentum and being
able to exploit available tools, methodologies, etc.? A simple yet effective approach is to decrease granularity. Rather
than talking about single resources, we talk about somethingwhich up to now only existed in drawings, such as in the
LOD cloud2 , which graphically represents the landscape of∗keith.alexander@talis.com†richard.cyganiak@cyganiak.de‡michael.hausenblas@deri.org§jun.zhao@zoo.ox.ac.uk
1linked data on the Web using bubbles for datasets and arcs
between bubbles for the links between these datasets. The
voiD vocabulary, the “Vocabulary Of Interlinked Datasets”,
allows one to describe datasets (the bubbles) and linksets(the arcs between the bubbles), and in turn enables a number of tasks to be automated in a scalable manner.
The remainder of this paper is as follows: in Section 2 wediscuss some use cases that provide the motivation for voiD.
Then, in Section 3 we report on the design of the voiD core
vocabulary and its usage along with other vocabularies suchas Dublin Core [25]. We describe the publication and theconsumption of voiD descriptions in Section 4. In Section 5
we discuss current and potential applications of voiD andreport on related work in Section 6. We discuss future plans
and conclude in Section 7.

2.

USE CASES

In the following we will describe our motivation use cases
for voiD. In the context of linked data, we basically differentiate between:• on the one hand linked data publisher (a person ororganisation exposing structured data as RDF on the
Web and interlink it with other datasets), and• linked data consumer on the other hand; these might
be machines, for example using a semantic indexer ora query engine or, as well, humans, e.g., when using a
Web of Data browser such as Tabulator [4].
It is worth noting that the following use cases are notnecessarily restricted to the linked data domain.

2.1
2.1.1

Efficient Discovery of Datasets
Dataset Publisher

A dataset publisher might not be identical with the party
who created the raw datasets, but one who publishes them
onto the Web in a more accessible format. A dataset publisher wants to be able to publish metadata about the dataset
such that:

http://esw.w3.org/topic/FollowLinksForMoreInformation
http://www4.wiwiss.fu-berlin.de/bizer/pub/
lod-datasets_2008-09-18.html• The dataset can be found and aggregated by searchengine applications, or discovered in relevant searches;

Copyright is held by the author/owner(s).
LDOW2009, April 20, 2009, Madrid, Spain.
.

• The metadata provides clear licensing information sothat consumers can know how they can use the data2

and to whom they should attribute credits for creating/publishing the dataset;• That consumers can obtain information about accessinterfaces, such as APIs and SPARQL endpoints.
It is in the best interest of a dataset publisher to provide
potential users of the data with information that supports
them in accessing and using the dataset.

2.1.2

Expressing Research Data

A developer working together with biologists wants to help
domain experts to find research data published by their peer
colleagues. These are often produced for a particular experiment, for a particular study or publication, or hosted bya particular public database. Scientists might know whose
datasets they would prefer to access because they often havea clear idea about their content or they trust more on thatdata provider. When looking for new datasets, they mightsearch for datasets that provide relevant content (such as information about genes, proteins, or micro-array gene expression), that are produced in a right experiment environment,or that provide additional information that will complement
their local experiment results.
To find the right dataset and to make this dataset accessible for biologists in a user-facing application, the developer
often has to go through the following process:• Locate a dataset that contains information relevant
to biologists’ research interests, such as information
about a specific organism; or more specifically, genomic information about a particular organism;• Find out how this dataset can be programmatically accessed, as an RDF dump, through SPARQL endpointor any other protocol;• Find out the licence associated with the dataset, making sure that data are accessible under open-access licence or certain attribution;• Understand the content of the dataset in order to perform an alignment with other datasets. Information
about URIs used in the dataset can help one with thedata identity alignment, schema(s) used in the dataset
for data schema alignment, and its links with otherdatasets for assisting data integration.

Effective Dataset Selection

A consumer may have discovered several datasets, for example as a result of an indexer query. The question thenarises how to select appropriate datasets from this list of
potential candidates. The consumer, either a human or aquery federation engine, might wish to define “appropriateness” along the following criteria:
1. The content of the dataset, that is, what the dataset
mainly is about. Based on some kind of categorisation
scheme a selection could take place;

Search Engine Provider

A search engine provider wants to discover detailed descriptions about datasets efficiently. A crawler has stumbled upon an individual RDF document on the Web. How
will it discover metadata that applies to the entire dataset
and cannot be repeated in every single document? The simple approach of just putting the voiD description online andlinking it from somewhere on the site does not meet ourneeds, as it could take the crawler a long time to find thedescription. It is important that the voiD description is
discoverable as soon as the crawler finds the first RDF document, so that the crawler can use voiD metadata to guide
its processing of the data.
The Sindice search engine [23] already uses Semantic Sitemaps [7] to enable discovery and efficient processing of datasets. It seems natural to address the situations above bybuilding on Semantic Sitemaps.

2.2

2.3

2. The interlinking to other datasets, that is, to which
other datasets and how the dataset is interlinked;
3. Vocabularies used in the dataset.
The criteria listed above can be understood in terms of
quality and quantity. For example, one might be interested
only in datasets containing foaf:interest links to a certain
other dataset. Or, where the number of links are of interest, one may specify that only datasets with more than one
million links should be taken into account.

2.4

Query Optimisation

With many datasets now on the Web both connectable(through shared vocabulary terms) and connecting (by linking to resources in other datasets), it is naturally desirable
to query across multiple datasets at once with SPARQL.
Optimisation of SPARQL queries can be achieved in astatic way. A set of logical rules [22] can be applied to aquery engine, to calculate all equivalent query plans for agiven query and then choose the most optimised query plan
to be executed. To optimise SPARQL queries dynamically,i.e., deciding the best execution approach during the execution phase [14], one can use the statistical information
about datasets, such as how much information is provided
about a particular entity or property. This information canbe used by the query mediator to optimise query plans by,for example, modifying the order in which a query pattern is
executed according to the estimated size of data results [8].

3.

VOCABULARY DESIGNThe Vocabulary of Interlinked Datasets (voiD) [2] is a vocabulary and a set of instructions that enables the discovery
and usage of linked datasets. The principle of the voiD effort is to use real requirements to guide the scope of thedesign, and to re-use existing vocabularies wherever possible instead of creating our own. Therefore, we have kept
the creation of new classes and properties under the voiD
namespace (http://rdfs.org/ns/void#) to the minimum.

3.1

Datasets

In the following, we will define and explain the basic concepts voiD operates with. A fundamental entity in voiD isa dataset.
Definition 1. A dataset is a set of RDF triples that are
published, maintained or aggregated by a single provider.
We think of a dataset as a meaningful collection of triples,that deal with a certain topic, originate from a certain sourceor process, are hosted on a certain server, or are aggregatedby a certain custodian. The term thus has a social dimension

that is not easy to capture in a formal definition. This differentiates datasets from RDF graphs [17], which are purely
mathematical constructs. Any arbitrary set of RDF triples
is an RDF graph, by definition, regardless of the triples’ semantics. Also, typically a dataset is accessible on the Web,for example through resolvable HTTP URIs or through a
SPARQL endpoint, and it contains sufficiently many triples
that there is benefit in providing a concise summary.
The ultimate purpose of creating a void:Dataset instanceis that this single resource represents the entire RDF dataset,and thus allows us to make statements about the entire
dataset within the standard RDF model. The relationshipbetween a void:Dataset instance and the concrete triples
contained in the dataset is established only in an operationalmanner: A voiD description usually contains access information, such as the address of a SPARQL endpoint where thetriples can be accessed.
We find that most datasets describe a well-defined set ofresources. Hence, a dataset can also be seen as a set of descriptions for certain resources, which often share a common
URI prefix (such as http://dbpedia.org/resource/).
HTTP URIs have “owners”, due to their use of DNS domain names. URI ownership is defined as “a relation between a URI and a social entity, such as a person, organisation, or specification.”[15] Information about a URI that isprovided by the URI owner is called authoritative information. We use this notion to define authoritative datasets:
Definition 2. A dataset is authoritative with respect toa certain URI namespace if it contains information about
resources named by URIs in this namespace, and is publishedby the URI owner.
A straightforward method of publishing authoritative datasets is by using resolvable HTTP URIs in the linked datastyle. The URI owner also configures the server that responds when the URI is resolved. Therefore, if resolving
yields a description of the resource named by the URI, thenthe data is authoritative.
The notion of authoritative information supports the social convention that a URI owner gets to decide what a URI
identifies. Providing authoritative information is how the
URI owner communicates this decision to the world. Even
if third parties disagree with that information, they can stillagree that they are talking about the same thing, which
would be much harder without the grounding provided by
the existence of an authoritative source.

3.2

The conceptual model of voiD links is depicted in Fig. 1.
Let us assume there are two datasets. One of them contains
links to the other, that is, it contains RDF triples that connect resources from both datasets. We model this in voiD
using two instances of void:Dataset, and another dataset:LS1 which is a subset of one of the datasets, and declared
to be of type void:Linkset. We define void:Linkset as:
Definition 3. A linkset LS is a set of RDF triples where
for all triples ti = hsi , pi , oi i ∈ LS, the subject is in one
dataset, i.e. all si are described in DSsrc , and the object isin another dataset, i.e. all oi are described in DSsink .
The natural expectation is that both DSsrc and DSsink
are themselves described in voiD. We note that the triples
ti are often referred to as “interlinking triples”.

3.2.1

3.2.2

• void:subset to state where the interlinking triples reside (read: a dataset :DS has a subset :LS);• void:target to declare an interlinking target (for the
non-directed case); in the directed case, one can use
void:subjectsTarget and void:objectsTarget to determine the direction (both being sub-properties ofvoid:target);• void:linkPredicate to express the RDF property (type)of the interlinking in a linkset.
We note that it is expected that per RDF predicate a respective instance of void:Linkset could be created, depending on the needs of an application. Further, one may take
into consideration that due to the modelling of void:target
and its sub-properties, light-weight subsumption inferencing
may be necessary to apply generic queries that will not distinguish between the directed and non-directed case.
In listing 1 a sample voiD description is depicted describing the interlinking from DBpedia to DBLP. It is an example
3

Figure 1: Interlinking modelling in voiD.

Interlinking Regarding Directionality

Independent of the former situation, voiD distinguishes
between the non-directed vs. directed cases. In somecases one is interested in stating the direction of the interlinking (for example with foaf:interest), and in othersituations the direction is of no interest (e.g., owl:sameAs),
as shown in Fig. 3.
In order to express the interlinking as outlined above, voiDoffers the following RDF properties:

Linksets

Besides datasets, voiD also deals with interlinking between datasets. Interlinking in voiD is a first-class citizens,
hence modelled as a class.

Inline links vs. 3rd-party links

In voiD we are able to model two different situations: theclassic LOD3 case vs. the 3rd-party case (Fig. 2). In theclassic LOD case, the linkset is a subset of one of the two
involved datasets, while in the 3rd-party case a third dataset
is involved that actually contains the linkset.
Though the 3rd-party cases is not yet widely implemented
in the context of linked data, this pattern of keeping links
separate from interlinked datasets has been well argued inexisting research such as found in the Hypertext community [6]. In LOD, there are already first applications (RKBexplorer, see section 5.1), and it is very likely that such systems will evolve over time and grow considerably.

LOD ... Linking Open Data datasets, see http://esw.
w3.org/topic/SweoIG/TaskForces/CommunityProjects/
LinkingOpenData

(a) Non-directed case.

(a) Classic LOD case: Describing the :DBpedia
dataset and its contained :DBpedia2DBLP linkset(b) Directed case.
Figure 3: Interlinking regarding direction.

• foaf:homepage of the dataset’s homepage should be
used, to allow one to connect different descriptions of
the same dataset provided in different places on theWeb. The recommended process in voiD is IFP smushing on foaf:homepage property;(b) 3rd-party case: Describing the stand-alone:DBpedia2DBLP linkset• dcterms:subject should be used to categorise a dataset.
For the general case, we recommend the use of a DBpedia resource URI (http://dbpedia.org/resource/XXX) to categorise a dataset, where XXX stands forthe thing which best describes the main topic of what
the dataset is about. However, DBpedia might notcontain concepts for describing some domain specificdatasets. For example, there are no exact DBpedia resource URIs for describing that a dataset is about “insitu hybridisation image”. We hence encourage publishers to describe such datasets using concepts widelyadopted in their own communities, so that they can
not only capture precisely the categorisation of theirdatasets but also ensure that these datasets could beconnected with other relevant data from their domains;

Figure 2: Interlinking regarding authoritative datasets.

for a directed case. The description defines nothing about
who published this voiD description about DBpedia, whichmeans that it could also be an example for a 3rd-party case.
Further, the listing 2 shows a SPARQL query that is executed against listing 1 to search for a dataset that is about“computer science” and which is linked from DBpedia. The
result yields the dataset :DBLP.

3.3

Reuse of Other Vocabularies

In the voiD guide [1] we describe the reuse of other vocabulary terms not directly defined in the core voiD vocabulary
alongside with voiD. Some important properties from othervocabularies are listed in the following. We note that there
are many other aspects one may want to choose to describe
in a dataset. A complete description of recommended usage
can be found from the voiD user guide [1].
• Properties from the dcterms namespace for general
metadata, such as the publishing organization and publishing date of a dataset;• Statistical information represented using the “Statistical Core Vocabulary” (SCOVO)4 [11].

3.4

Dataset Licensing

As stated in Section 2, it is crucial for a data publisher toassociate appropriate licensing information with their published data, so that potential users of the dataset would4

http://purl.org/NET/scovo

1
2
3
4
5

@prefix@prefix@prefix@prefix@prefix

owl : < http :// www . w3 . org / 2 0 0 2 / 0 7 / owl # > .
foaf : < http :// xmlns . com / foaf /0.1/ > .
dc : < http :// purl . org / dc / terms / > .
void : < http :// rdfs . org / ns / void # > .
dbp : < http :// dbpedia . org / r e s o u r c e / > .

SCOVO for representing statistics. The main class in SCOVO
is the scovo:Item, which records a single number or statistical value along with so called dimensions. We provide two
types of information for describing statistics:

6
7
8
9

• Statistics concerning the whole dataset or linkset, suchas overall triple count or fine-grained statistics, expressing the number of instances of a class or property by using different pre-defined dimensions, including void:numberOfResources, etc.;: DBpedia a void : Dataset ;foaf : homepage < http :// dbpedia . org / > ;void : subset : DBpedia2DBLP .

10
11
12
13
14
15

: DBLP a void : Dataset ;foaf : homepage < http :// dblp . l3s . de / d2r / > ;dc : subject dbp : C o m p u t e r _ s c ie n c e ;dc : subject dbp : Journal ;dc : subject dbp : Proceedings .

• Attributing statistics to a source, recording where astatistical datum stems from.

16
17
18
19
20

: DBpedia2DBLP a void : Linkset ;void : subjectsT arget : DBpedia ;void : objectsTarget : DBLP ;void : linkPredicate owl : sameAs .

Listing 1: An exemplary voiD description.

Listing 4 demonstrates possible statistic information one canpublish for their dataset. The current modelling of statistics
1
2
3

1
2
3
4
5
6
7

SELECT DISTINCT ? dataset
WHERE {? dataset a void : Dataset ;dcterms : subject dbp : C o m p u t e r _s c i e n c e .
? linkset void : s ubjectsT arget : DBpedia ;void : objectsTarget ? dataset .
}

4
5
6
7

: DBpedia a void : Dataset ;void : statItem [rdf : value 20000;
scovo : dimension void : n u m b e r O f R e s o u r c es ;scovo : dimension foaf : Person ;dcterms : source < http :// wiki . dbpedia . org / >] .

;Listing 4: Expressing statistics about a dataset invoiD.

Listing 2: An exemplary query on voiD.

know under which terms they can use it and what attribution they should apply. The dcterms:license property
should be used to to point to the license under which adataset has been published. Further, to allow automatic
analysis of datasets, voiD also recommends a set of canonical identifiers for well-known licenses [1]. The example below states that the DBpedia dataset is published under theterms of the GNU Free Documentation License.
1
2: DBpedia a void : Dataset ;dcterms : license< http :// www . gnu . org / c o p y l e f t / fdl . html > .

Listing 3: An exemplary voiD description aboutdata license.
Licensing of datasets is a complex issue. Datasets are collections of facts rather than creative works, and different
laws apply. Scientists are most cautious about publishing
their datasets onto the Web and they might request very
specific or strict policies for sharing their data. Most licenses such as Creative Commons or the GPL are based on
copyright and are designed to protect creative works, but
not databases, and applying them to datasets might nothave the desired legal result. Meanwhile, efforts such asOpen Data Commons [19] and Science Commons [16] aredeveloping dedicated licenses for data.

3.5

Statistics

Of special interest to distributed SPARQL agents willbe the statistics about the triples available in the dataset,
described with the void:statItem predicate. We adopt

in voiD is still experimental. We had to make choices between (i) a precise usage of scovo through a rather verbose
expression and (ii) creations of shortcuts to express statistics
needed for describing linked datasets:• Scovo has an implicit assumption that all scovo:Items
associated with the dataset they describe share thesame dimensions. This does not fit well with our requirements for being able to mix items of different dimensions for a dataset. On the other hand, the correct
Scovo modelling would lead to awkwardly complex andverbose notation for simple statistics.
• We encourage the use of classes and properties in places
where scovo requires an instance of scovo:Dimension.
This breaks the symmetry of the scovo model. scovo
would require us to create a scovo:Dimension for eachclass or property. This would be quite verbose.
Because of the issues above, queries for statistics information using SPARQL can be awkward. It will often requirea verbose check to make sure that an item has only certain
dimensions and no others.

3.6

Additional Terms in voiD

RDF datasets use one or more RDF-Schema vocabularies
or OWL ontologies, hence we provide the void:vocabulary
to list vocabularies used in a dataset. To express technicalfeatures of a dataset, such as formats in which the data isavailable, one can use void:feature. Further, a SPARQL
endpoint that provides access to a dataset via the SPARQL
protocol can be announced using the void:sparqlEndpointproperty. Listing 5 shows the usage of the terms describedabove. We note that a complete list of the terms is available
from the voiD user guide [1].

1
2
3

: DBpedia void : sparqlEn dpoint< http :// dbpedia . org / sparql > ;void : feature [ dcterms : format" application / rdf + xml " ; ] ;void : vocabulary< http :// xmlns . com / foaf /0.1/ > .

Listing 5: Additional voiD terms usage.

4.

PUBLICATION AND CONSUMPTION

We envision dataset publisher to offer a voiD description
along with their dataset. A voiD description typically has
two parts, (i) manually created part (categorisation, vocabulary, license, etc.), and (ii) automatically generated part,
mainly regarding statistics.
In the following we will discuss the publication process of
voiD descriptions and their discovery in order to consume
them.

4.1

Publication

Publishing a voiD file means to physically deploy it on
the Web in an RDF serialisation. We have detailed out theoptions in the voiD guide [1].
For dataset that are published as a collection RDF documents, as commonly seen in the linked data publishing style,
one can use a dcterms:isPartOf triple in each document tolink back to the URI identifying the voiD dataset, as shownin listing 6. Resolving the dataset URI will answer a voiDdescriptions about the entire dataset, allowing agents to discover the voiD description when encountering an individual
document from the collection. The intuition behind using
the dcterms:isPartOf property is that the RDF document
contains an RDF graph whose triples are part of the dataset.
1< http :// dbpedia . org / data / Berlin > dcterms : isPartOf: DBpedia .

Figure 4: The voiD discovery-via-sitemaps process.

5.

VOID IN THE WILD

After releasing the first edition of voiD in early 2009, we
have seen a certain community uptake. People and organisations would start using in different areas and for different
purposes, potentially far beyond what we have envisioned in
the realm of our own use cases. We report on known usagesof voiD in the following and point out potential application
areas.

5.1

Existing Applications

5.1.1

5.1.2
Listing 6: Use backlinks publish voiD description ofa dataset.
As discussed in [10], we can imagine that voiD descriptions
are crawled and indexed by semantic search engines (suchas Sindice [23] or Yahoo’s search monkey [18]) in order toprovide a central point of lookup.

4.2

Discovery via Sitemaps

A discovery mechanism for use by RDF-harvesting web
crawlers (Fig. 4) has been defined as follows:
1. Given a domain name, the client gets the file robots.txt
and searches for a line that starts with Sitemap:; therest of that line is the URI of a sitemap;
2. The semantic sitemaps extension to the sitemap protocol defines a <sc:dataset> element that can have a<sc:datasetURI> child element. If present, the valueof that element is a URI that identifies the dataset
datasetURI;
3. The dataset URI datasetURI is dereferenced whichyields the voiD description of the dataset.

Tools for Creating voiD Descriptions

To boot-strap the process of creating voiD descriptions,several tools are available: ve, the voiD editor (Fig. 5),
liftSSM5 , an XSLT script able to boot-strap from a Semantic
Sitemap, and, for creating the quantitative, statistical data,a new release of the NX parser6 , offering a voiD export for
statistics.

“Linked Datasets Explorer” (LDE)

To let user browse and explore a collection of voiD descriptions, we have developed the LDE demonstrator. Fig. 6
shows the current state of LDE7 which operates on a manually created, so called “seed” set of voiD descriptions.

5.1.3

RKB explorer

The RKB explorer has a voiD site8 which enables querying
and browsing for CRS datasets. Further, the interlinking of
the RKB sites can be visualised using the underlying voiDdescriptions (Fig. 7).

5.1.4

Query Federation

Only recently Clarck-Parsia announced their voiD support9 :“There is a touch point with the linked data effort, which meant that the new voiD vocabulary5
http://rdfs.org/ns/void-guide#sec_4_3_Publishing_tools
6
http://sw.deri.org/2006/08/nxparser/release/nxparser-1.1.jar
7
http://ld2sd.deri.org/lde/8
http://void.rkbexplorer.com/
9
http://clarkparsia.com/weblog/2009/02/04/
distributed-query-pellet-into-the-void/for describing datasets turns out to be very usefulfor describing the distributed data sources that
we query over, including their interrelations.”
Further, OpenLink plans to release its “Smart SPARQL
Federation capabilities”, based on voiD, soon.

5.1.5

Middleware

OpenLink’s Sponger Middleware uses voiD for generating linked data from non-RDF data sources such as HTMLpages. An example10 from http://linkeddata.uriburner.
com/ with the voiD description deployed as XHTML+RDFa
is shown in Fig. 8. Further, the statistics maintenance intheir Virtuoso Quad Store is performed based on voiD.

5.2

Potential Applications

We envision voiD to be applied in many scenarios, someof which we have identified earlier in section 2. Only recently, for example, we have started to develop a dataset
ranking algorithm based on voiD descriptions; this is subject
to more research. One could further apply voiD to DARQ(Distributed ARQ) [22].
A totally different application domain is visualisation: forexample, “The Map of Data”11 in Sindice can be generated
automatically thanks to voiD.
Ultimately, to be of use, one wants applications that benefit from voiD. Put in other words, this means that, given
there are applications that consume voiD and offer some
added value, the incentive for publishers to provide voiDdescriptions is self-evident. One such application could bea sort of dynamic dataset selector which, configured with a
specification of the dataset (topics, license, interlinking withcertain other datasets) would at run-time of an application
discover and select appropriate datasets according to thesearch specification.

6.

RELATED WORKTo the best of our knowledge, no comparable approachto voiD exists. That is, in the context of the Web of Data,
we are not aware of any specification that allows the description of datasets and their interlinking the way voiD
does. However, we acknowledge previous work of Semantic
Sitemaps [7] and build upon it.
In the scope of the Web of Documents, we note that at thetime of writing a W3C Working Draft of POWDER (Protocol for Web Description Resources) [3] is available. POWDER aims at providing information about Web resources,
such as scope, authoritative information, etc., without retrieving the resources themselves. POWDER comes in two
flavours, (i) as human-legible XML, and (ii) in an RDF version. It also provides a GRDDL transformation to turn theformer into the latter. The descriptions can be applied togroups of resources defined via listing of URIs, regular expressions, etc. Several publishing methods are suggested(via HTML <link> in the header, HTTP Link: header orusing XHTML+RDFa). Especially in the Web of Trust,
POWDER is expected to play a vital role, though implementation complexity might hinder wide-spread adoption.
Further, OASIS’s XRDS (eXtensible Resource Descriptor
Sequence) [24] is an XML format for metadata discovery
10

http://linkeddata.uriburner.com/about/html/http://twitter.com/mhausenblas#Dataset
11
http://sindice.com/map

about a resource. The discovery protocol for XRDS documents given a URI was defined in 2006 as part of Yadis,
focusing on services such as OpenID and OAuth. In early
2008, XRDS-Simple was proposed12 , but is now obsolete.
Only very recently, the latest draft of “/host-meta” [21]was proposed. The core of this proposal is a single “wellknown location”, /host-meta, acting as a directory of theinteresting metadata about a Web site. The format allows
different types of site metadata to be referenced by an URI
or included inline.
One could understand the “HTTP Link: header” [20] proposal related to voiD, as it also supports discovery, offering
metadata about resources by resurrecting a (currently deprecated) feature of HTTP. This proposal is at the time ofwriting still under vivid discussion and not yet seen stable.
Regarding federated SPARQL queries, DARQ (DistributedARQ) [22] proposes so called “service descriptions” thatare able to specify capabilities of a SPARQL endpoints.
The service descriptions enable the DARQ query engine
to decompose a query into sub-queries, each of which canbe answered by an individual service using query rewriting and cost-based query optimisation to speed-up query
execution. Further, we note an attempt called “SPARQL
Endpoint Description”13 that aimed to allow the announcement of endpoint capabilities and contents, support discovery through service directories, and supply browsing andfederation hints. Both proposals seem to be not further
maintained and/or have not reached wide-spread adoption.
Finally, we note that the W3C Technical ArchitectureGroup (TAG) started to contemplate about “Uniform Access to Metadata”14 , basically being a survey regarding the
problem of specifying a uniform method for obtaining information pertaining to a resource without necessarily having
to parse a representation of the resource.

7.

OUTLOOK

We have released the voiD vocabulary and voiD user guideto linked data communities in January this year. In this release, we have used the use cases presented in section 2 toguide the design scope of the voiD vocabulary. Supports
for describing the quality, provenance and versions of linked
datasets are to be addressed in the next release of voiD.
Also, the statistics modelling in the current voiD model isstill experimental. We are communicating with user communities and the SCOVO team in order to propose a morestable modelling in the coming release15 . Additionally wewill liaison with initiatives such as the “Ontology Metadata
Vocabulary” [9] sharing similar goals.
To test and evaluate the usefulness of voiD, we need toolsthat use voiD to support the discovery of datasets or the
SPARQL query federation. Fortunately, semantic query engines like Sindice and SPARQL query processing systems(like OpenLink) are adopting voiD in their implementations.
It is challenging to completely automate the creation of voiDdescriptions. We need tools like the NX parser to take as
12

http://www.hueniverse.com/hueniverse/2008/03/
putting-xrds-si.html
13
http://esw.w3.org/topic/SparqlEndpointDescription
14
http://www.w3.org/2001/tag/doc/
uniform-access-20090205.html
15
See
http://code.google.com/p/void-impl/issues/
list?can=2&q=milestone:Release2.0forplannedissues.

much as possible of the heavy lifting for non-technical data
publishers as possible.

Acknowledgements
Our work has partly been supported by the European Commission under Grant No. 217031, FP7/ICT-2007.1.2, project“Domain Driven Design and Mashup Oriented Development
based on Open Source Java Metaframework for Pragmatic,
Reliable and Secure Web Development” (Romulus)16 , andthe Joint Information Systems Committee [Project “FlyWeb”]. The authors would further like to thank (alphabetically): Orri Erling, Hugh Glaser, Olaf Hartig, Tom Heath,
Andreas Langegger, Ian Millard, Marc-Alexandre Nolin, Yves
Raimond, Yrjänä Rankka, Francois Scharffe, and Giovanni
Tummarello.

8.

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Figure 5: Manual creation of voiD descriptions with ve.

(a) Looking-up datasets.

(b) Browsing datasets.

Figure 6: Linked Datasets Explorer (LDE).

Figure 7: Visualisation of RKB interlinking.

Figure 8: OpenLink’s instant-voiD-generator for structured HTML pages.