=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Green Linked Data
|pdfUrl=https://ceur-ws.org/Vol-782/HoxhaEtAl_COLD2011.pdf
|volume=Vol-782
|dblpUrl=https://dblp.org/rec/conf/semweb/HoxhaRE11
}}
==Towards Green Linked Data==
https://ceur-ws.org/Vol-782/HoxhaEtAl_COLD2011.pdf
Towards Green Linked Data
Julia Hoxha1 , Anisa Rula2 , and Basil Ell1
1
Institute AIFB, Karlsruhe Institute of Technology, {julia.hoxha, basil.ell}@kit.edu,
2
Dipartimento di Informatica Sistemistica e Comunicazione, Università degli Studi di
Milano-Bicocca, anisa.rula@disco.unimib.it
Abstract. We here present a vision of what needs to be addressed when
designing and publishing linked data on the Web. Our approach aims
at reducing the amount of incorrect, irrelevant, or redundant content –
which can also be seen as pollution in the Web of Data – when publishing
linked data. At the foundation lie the design principles adapted from
green engineering.We envision a holistic framework that evaluates, along
these principles and their respective assessment metrics, datasets from
publishers and allows configuration of new validation tools.
1 Introduction
The rapid growth of the Web of Data has contributed to the creation of large
amounts of linked data that often results in low quality content. For this reason,
it is important to investigate the problem of pollution from which the linked data
environment may suffer. Pollution refers in our case to incorrect, irrelevant, or
redundant content, which aggregates low value to users and services that con-
sume these data. Examples include broken links, ambiguous use of owl:sameAs,
redundant definition of vocabularies, multiple URIs for the same resource in a
dataset, complex vocabularies that cannot be efficiently reused, uncomprehensi-
ble data, unaccessible data, non-maintained data, etc.
We approach the field of green engineering, since it has long been involved
with quality assurance when designing materials, processes and systems that are
benign to the environment. Moreover, this field offers an ecological perspective
when discussing the problems encountered in linked data publishing. At the foun-
dation of our approach lie the fundamental principles of green engineering [2],
which we adapt for the linked data setting. We aim at providing a vision of what
needs to be addressed when designing and publishing linked data, in order to
minimize pollution in the Web of Data, increase reuse and achieve sustainability.
To concretize this vision, we introduce a framework that applies the principles
with measurable aspects to evaluate how green the datasets from publishers are.
The issue of quality on the Web of Data has been addressed along aspects
such as syntax errors and inconsistencies in datasets [6], link discovery and main-
tainance [8], quality and trustworthiness assessment based on provenance infor-
mation [5], etc. Our approach is not complementary to these works, rather en-
compasses and aligns them to our principles. The framework that we introduce
is holistic and based on green engineering aspects, which can be extended with
new measures and validation tools for higher quality of the published data.
2 Julia Hoxha1 , Anisa Rula2 , and Basil Ell1
2 Green Linked Data Principles
We introduce each principle with a short description and assessment measures,
which are partly contribution of Web community [1]. Basic resources related to
the Web of Data include vocabularies, datasets, RDF links, and URIs. The prin-
ciples are non-orthogonal, therefore some measures occur in different principles.
Principle 1. Inherent rather than circumstantial
Ensure that data are as inherently benign as possible
Benign refers to data that maximize the qualities in which the publishers and
consumers are interested. Publishers are interested that their data is consumed,
i.e. data is 1)accessible 2)understandable by consumers and 3)meet their demand.
Dimension Measures
server accessibility; accessibility of a SPARQL-endpoint;
Accessibility dereferenceable URIs; accessibility of the RDF dumps
usage of a dedicated provenance vocabulary [5];
Reliability basic provenance information; usage of digital signatures
labeling and readable description of classes, properties and entities;
Comprehensibility indication of exemplary URIs; exemplary SPARQL queries
Table 1. Dimensions and Measures of Principle 1
Principle 2. Prevention Instead of Treatment
It is better to prevent waste than to treat or clean up after it is formed.
Publishers should strive to produce data with ”zero-waste”, which in the Web
of Data results from the lack of use or consumption, i.e. consumers (human and
machines) are unable to effectively exploit published data for beneficial use.
Dimension Measures
Visibility listing in linked data catalogues
valid entity definition as members of disjoint classes
Consistency inconsistent values for properties; usage of uniform datatypes
valid usage of inverse-functional properties
Comprehensibility see Table. 1
Table 2. Dimensions and Measures of Principle 2
Principle 3. Maximize Reuse
Reuse existing resources: vocabularies, URIs, links
Publishers should strive to maximize usage of provenance information, usage of
established vocabularies, and referencing of prominent URIs.
Dimension Measures
existence of provenance information
Provenance usage of established provenance ontologies
usage of an established representation format
Uniformity usage of established vocabularies;referencing of established URIs
Redundancy multiple URIs for same entity; identity resolution
Table 3. Dimensions and Measures of Principle 3
Towards Green Linked Data 3
Principle 4. Design for Separation
Modularization operations should be a component of the design process
Engineering large monolithic ontologies leads to artifacts that can rarely be
reused, due to fitting to the design requirements. Modularization helps solve
this challenge using instead a set of micro-ontologies, therefore increasing op-
portunities for the reuse of the developed artifacts.
Dimension Measures
usage of related provenance ontologies
Provenance metadata on derivation history, data engineering/generation process
Partitionability usage of micro-ontologies; metadata on micro-ontologies
Table 4. Dimensions and Measures of Principle 4
Principle 5. Maximize Efficiency
Design datasets in order to maximize efficient exploitation
Published linked data should allow consumers to search, query and browse them
achieving required results with minimum effort and time.
Dimension Measures
no syntax errors; proper datatypes
Validity no deprecated classes and properties; usage of proper datatypes
number of triples, number of internal and external links
Size scope and level of detail in the dataset
reasoning performance
Performance scalability; browsing efficiency
Consistency see Table. 2
Table 5. Dimensions and Measures of Principle 5
Principle 6. Output-Pulled Versus Input-Pushed
Bringing content and publishing rate in line with demand
Publishers should have possible consumers in mind when designing their data.To
this aim, they should cover user needs providing only the necessary resources.
Dimension Measures
existence of concrete user requirements;
Consumer requirements existing tools or applications consuming such/similar data
identification of semantic data and vocabulary gap via query logs [7]
Semantic Gap identification of sparse result sets and near matches [4]
Table 6. Dimensions and Measures of Principle 6
Principle 7. Conserve Complexity
When making design choices, publishers should strive to reuse a complex on-
tology or dataset as it is, instead of recycling i.e. extracting parts of it and
modifying them for further use. Complexity should be viewed as an investment
for reuse.
4 Julia Hoxha1 , Anisa Rula2 , and Basil Ell1
Dimension Measures
size of the vocabulary (classes, properties, instances, derived properties, etc.);
Complexity entropy distance-based structure complexity
Partitionability see Table. 4
Table 7. Dimensions and Measures of Principle 7
Principle 8. Meet Need, Minimize Excess
Design for unnecessary capability or capacity solutions should be considered a
design flaw
Publishers should try to provide datasets that meet the necessary capabilities,
with no excessive details, while ”‘one size fits all”’ solutions are a design flaw.
Dimension Measures
conformance to user requirements (e.g. competency questions);
Scope level of detail of dataset
Granularity no one-size-fits-all but domain-specific micro-ontologies
Size see Table. 5
Table 8. Dimensions and Measures of Principle 8
Principle 9. Design for Afterlife
Design for performance in a commercial afterlife
It is necessary to provide updates and maintainance after the planned end of life
of the data. To reduce waste, components that remain functional and valuable
can be recovered for reuse and/or reconfiguration.
Dimension Measures
Validity see Table. 5
Accessibility see Table. 1
indication of the most recent data validation (update)
Timeliness frequency of validation; exclusion of outdated data;inclusion of recent data;
appropriate metadata to indicate outdated or deprecated dataset/URIs
indication of maintainance period
Targeted Lifetime usage of proper vocabularies on maintainance data
Table 9. Dimensions and Measures of Principle 9
3 Green Linked Data Framework
In this section, we introduce an envisioned framework, which is a Web platform
addressing three main groups of visitors 1) those who want to learn about linked
data and the green approach, 2) publishers that wish to check their linked data
before publishing them online, and 3) software developers who can contribute
with validators that check particular measures pertaining to the principles.
We have initiated the implementation of this framework online1 , aiming to
make it a future point of reference for the users of the Web of Data. Through the
1
http://www.greenlinkeddata.org
Towards Green Linked Data 5
introduction of the green principles and the dimensions in which they expand,
as well as via the further enrichment of the website with materials and related
links, we aim to raise the concern among these users about the importance of
the quality of linked data published online.
Fig. 1. greenlinkeddata.org Framework
Besides its informative nature, the platform aims at enabling users to make
concious decisions about the data they need to publish, and most importantly
help them evaluate how these data conform to the green principles. Therefore,
for the publishers the framework offers the possibility to automatically check the
datasets or vocabularies they want to publish based on the measures defined.
A publisher may choose to check its data towards one or several principles and
dimensions (Fig. 1).
The evaluation of the data will be done through validators which will consist
of open source or off-the-shelf algorithms offered in the Web of Data community,
as well as new validators (e.g. to check comprehensibility) that we are imple-
menting. A more interesting feature of the framework is the possibility provided
to software developers to submit their validators, for example as Web services.
For example one measure for the comprehensability of a dataset is the label-
ing completeness metric LClp where lp is a set of labeling properties such as
rdsf:label. This metric evaluates the ratio of non-information resources for
which at least one label is defined [3].
There is also the possibility to suggest new dimensions and respectively con-
tribute with appropriate validators. Thus, our goal is to provide an open frame-
work, where Web users not only contribute with validators, but also with new
6 Julia Hoxha1 , Anisa Rula2 , and Basil Ell1
ideas, materials and tools. Furthermore, the platform allows adding to each prin-
ciple in the website new comments that may consist of, but are not restricted to,
best practices, benefits or even difficulties they have had when dealing with those
aspects. They may also contribute with suggestions on how to extend dimensions
and measures of that principle.
4 Discussion and Conclusion
At the foundation of this approach lie green engineering principles, which we
have transfered to linked data publishing. In contrast to the physical artifacts
addressed in the original approach, we deal with data that represent immaterial
artifacts. The fundamental differences between these two types of artifacts have
necessarily been taken into account.
Physical artifacts are subject to decay and abrasion in consequence of usage.
They cannot easily be duplicated or distributed, and possess the property of
excludability. Since material goods are naturally scarce, this can lead to rivalry.
In contrast, data are immaterial, thus can be easily duplicated and distributed,
without being subject to decay. While porting the principles to the linked data
setting, we have extraced only 9 of the original 12 principles, excluding e.g.
those dealing with renewable type of resources, infrastructure used to create and
provide the data, or discussion on green energy consumption.
In our future work, we will focus on extending the principles with other
measures and bringing to life via further development the envisioned framework.
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