=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Licenses Compatibility and Composition in the Web of Data
|pdfUrl=https://ceur-ws.org/Vol-914/paper_23.pdf
|volume=Vol-914
|dblpUrl=https://dblp.org/rec/conf/semweb/VillataG12
}}
==Towards Licenses Compatibility and Composition in the Web of Data==
https://ceur-ws.org/Vol-914/paper_23.pdf
Towards Licenses Compatibility and
Composition in the Web of Data
Serena Villata? and Fabien Gandon
INRIA Sophia Antipolis, France
{firstname.lastname}@inria.fr
Abstract. We propose a general framework to attach the licensing terms
to the data where the compatibility of the licensing terms concerning the
data affected by a query is verified, and, if compatible, the licenses are
combined into a composite license. The framework returns the composite
license as licensing term about the data resulting from the query.
1 Introduction
The absence of clarity concerning the licensing terms does not encourage the
reuse of the data in the Web of Data [3]. When consumers query the Web of
Data, results from different datasets, and thus released under different licensing
terms, are provided. In this paper, we propose first to verify the compatibility
among the licensing terms associated to a query result, and second, to com-
pose, if compatible, the distinct licensing terms for creating a composite license.
The composite license is retuned together with the query result using the stan-
dard SPARQL query results XML format by means of the 1 element.
We adopt Semantic Web languages only, and reuse the Creative Commons (CC)
licenses schema [1] to define the anatomy of our licenses. Licenses are composed
by models: cc:Permission, cc:Requirement, and cc:Prohibition. Models are
composed by elements eli like ShareAlike, Attribution, and many others. We
choose CC because it provides a general schema for licenses specification, even
if there are works which should not be released under the CC licenses [5, 3].
For addressing this issue and covering a wider range of machine-readable license
specifications, we align the CC vocabulary with the other schemas including li-
censing terms (Figure 1). We extend and adapt existing proposals for licenses
compatibility and composition in the area of service license analysis [2] to the
Web of Data scenario. However, the different application scenarios (service com-
position vs Web of Data) open different problems. The compatibility rules we
define are different, and the definition of the composite license mirrors the same
differences. Truong et al. [6] address the issue of analyzing data contracts using
RDF for the contracts representation. This work concentrates on data contracts
and not on data licenses. Krotzsch and Speiser [4] present a semantic framework
for evaluating ShareAlike recursive statements while we address the problem of
licenses composition.
?
The author acknowledges the support of the DataLift Project ANR-10-CORD-09.
1
http://www.w3.org/TR/rdf-sparql-XMLres/#head
omv:hasLicense omv:LicenseModel
doap:license gr:License
premis:licenseTerms premis:LicenseInformation
subPropertyOf cc:license subClassOf cc:License
voag:hasLicenseType voag:LicenseModel
nie:license vivo:License
mo:license meb:License
Fig. 1: Alignment between the Creative Commons vocabulary and other vocabularies.
2 Our proposal
Licenses Compatibility. We define a set of compatibility rules assessing the
possible compatibility among the elements composing the licenses L, following [2]
for service licenses. First, there are certain elements which are broader in scope
of permission than other elements, e.g., Sharing is more permissive than Re-
production [1]. The subsumption rules2 for cc:Permission elements, and the
way they can be combined are shown in Table 1. Table 2.a shows whether an
element el1 is compatible with another element el2 , i.e., el1 el2 , concerning
cc:Permission elements3 . The rationale is that these elements are compatible
if there is a subsumption relation between them.
Subsumption More permissive Less permissive
DerivativeW orks Sharing DerivativeW orks Sharing
Distribution Reproduction Distribution Reproduction
DerivativeW orks Reproduction DerivativeW orks Reproduction
Sharing Reproduction Sharing Reproduction
DerivativeW orks Distribution DerivativeW orks Distribution
Table 1: Compatibility rules for subsumption relation among cc:Permission elements.
Second, a possible situation in analyzing license compatibility is that one li-
cense Li specifies clauses which are not specified by the other license Lj , e.g., Li
specifies cc:Prohibition and Lj does not specify this clause. Table 2.b shows
the compatibility rules for specified elements against Unspecified elements4 . The
requirement for specification of Attribution does not affect the compatibility with
Unspecified (the same holds for Notice, SourceCode, and CopyLeft). Concerning
prohibitions, these elements are not compatible with Unspecified, e.g., commer-
cial use is the default setting of the licenses, thus we cannot assume compatibility
if commercial use is denied by NonCommercial. For permissions, we follow the
“conservative” approach where unspecification means a denial of compatibility5 .
2
Subsumption means that there is a compatibility if a certain license element eli
is more permissive, i.e., it accepts more, than the other license element elj .
3
Rules are expressed under the form of truth tables where elements are evaluated as
compatible T , or incompatible F .
4
We interpret unspecified elements as “do not care”, as in [2].
5
Table 2.c shows three exceptions of compatible elements from distinct models.
el1 el2 el1 el2 el1 el2 el1 el2
Sharing DerivativeW orks T N otice U nspecif ied T
Reproduction Distribution T Attribution U nspecif ied T
Reproduction DerivativeW orks T ShareAlike U nspecif ied T
Reproduction Sharing T SourceCode U nspecif ied T
Distribution DerivativeW orks T CopyLef t U nspecif ied T
Sharing Distribution F N onCommercial U nspecif ied F
(a) HighIncomeN ationU se U nspecif ied F
el1 el2 L1 L2 Reproduction U nspecif ied F
Attribution ShareAlike T el1 ∧ el2 Distribution U nspecif ied F
Attribution N onCommercial T el1 ∧ el2 DerivativeW orks U nspecif ied F
ShareAlike N onCommercial T el1 ∧ el2 Sharing U nspecif ied F
(c) (b)
Table 2: (a) Compatibility rules among cc:Permission elements, (b) Compatibility
rules among cc:Requirement, cc:Prohibition, cc:Permission elements against Un-
specified, (c) Composition rules among cc:Requirement and cc:Prohibition elements.
Let L(C) be the set of licenses associated to the named graphs affected by
the consumer’s query, we say that two licenses are compatible if the models
in both the licenses are compatible [7]. The models are compatible if (i) the
models are the same, (ii) the models are composed by elements which satisfy
the compatibility rules (Table 2.c), and (iii) their elements are compatible. The
elements are compatible if (i) the elements are the same, (ii) the elements satisfy
the subsumption rules (Table 1), (iii) the elements satisfy the compatibility rules
against Unspecified (Table 2.b), and (iv) the elements satisfy the compatibility
rules (Table 2.a-c). If the licenses are not compatible, then we leave to the data
provider to decide the strategy to deal with this situation, e.g., the data is
returned together with the more constraining license among L(C).
Licenses Composition. If the licenses are compatible then we compose
them such that the resulting composite license Lc ( is the composition rela-
tion) satisfies the following properties: Lc can be generated only if all the licenses
composing it are compatible, and Lc is consistent with the set of licenses used to
compose it. The definition of Lc is achieved through the definition of (i) redefini-
tion rules to be applied in case a subsumption relation holds (Table 1), (ii) com-
position rules necessary to maintain the consistency of Lc w.r.t. L(C) (Table 2.c),
and (iii) heuristics to compose the elements of each license into Lc . We consider
three basic heuristics: OR-composition: ∀l ∈ Li then l ∈ Lc ; AND-composition:
if ∃l ∈ L1 ∧ . . . ∧ Ln then l ∈ Lc ; Constraining-value: most constraining l ∈ L(C)
is included in Lc . We leave to the data provider the choice of her best strategy
for composing the licenses, e.g., AND-composition typically leads to a shorter
and simpler license, while OR-decomposition leads to a more complex license
where all the clauses in L(C) are listed. For example, assume we want to ana-
lyze two licenses L1 and L2 (Figure 2.a-b). We first compare the two licenses at
the model level. Both licenses contains the model cc:Permission. Even if the
two elements are not the same, there is a subsumption relation between them
(Table 1). The first license does not contain the model cc:Prohibition and the
@prefix cc: http://creativecommons.org/ns.
@prefix : http://example/licenses. @prefix cc: http://creativecommons.org/ns.
@prefix : http://example/licenses.
:lic1 a cc:License;
cc:permits cc:Reproduction; :licComposite a cc:License; THE ELEMENTS
cc:requires cc:ShareAlike. cc:permits cc:DerivativeWorks;
(a) cc:requires cc:ShareAlike;
cc:prohibits cc:CommercialUse.
@prefix cc: http://creativecommons.org/ns.
@prefix : http://example/licenses. THE MODELS:
- Permission
:lic2 a cc:License; - Requirement
cc:permits cc:DerivativeWorks; - Prohibition
cc:prohibits cc:CommercialUse.
(b) (c)
Fig. 2: Example of compatibility evaluation and composition of two licenses.
second license does not contain the model cc:Requirement. However, the two
elements cc:ShareAlike and cc:CommercialUse are compatible (Table 2.c).
Thus, the two licenses are compatible. Lc is obtained using the more permissive
permission, and the OR-composition heuristic.
3 Future challenges
The first point to be addressed is a legal and social validation of the proposed
framework. This means to evaluate, not only quantitatively the performance of
the algorithms to retrieve remote licensing statements referenced in the data,
but also the legal value of the composite license. We will address an evaluation
of the disparate named graphs that might be used in a “typical” query, and
their relative proportions that have compatible or incompatible licenses. Finally,
we are defining more complex heuristics like the one looking for the minimal
composite license.
References
1. H. Abelson, B. Adida, M. Linksvayer, and N. Yergler. ccREL: The creative commons
rights expression language. Technical report, 2008.
2. G. R. Gangadharan, M. Weiss, V. D’Andrea, and R. Iannella. Service license com-
position and compatibility analysis. In ICSOC, LNCS 4749, pages 257–269, 2007.
3. T. Heath and C. Bizer. Linked Data: Evolving the Web into a Global Data Space.
Morgan & Claypool, 2011.
4. M. Krötzsch and S. Speiser. Sharealike your data: Self-referential usage policies for
the semantic web. In ISWC, LNCS 7031, pages 354–369, 2011.
5. P. Miller, R. Styles, and T. Heath. Open data commons, a license for open data. In
LDOW, 2008.
6. H. L. Truong, G. R. Gangadharan, M. Comerio, S. Dustdar, and F. De Paoli. On
analyzing and developing data contracts in cloud-based data marketplaces. In AP-
SCC, IEEE, pages 174–181, 2011.
7. S. Villata and F. Gandon. Licenses compatibility and composition in the Web of
Data. In COLD, 2012.