=Paper=
{{Paper
|id=Vol-2451/paper-13
|storemode=property
|title=Automatic License Compatibility Checking
|pdfUrl=https://ceur-ws.org/Vol-2451/paper-13.pdf
|volume=Vol-2451
|authors=Giray Havur,Simon Steyskal,Oleksandra Panasiuk,Anna Fensel,Victor Mireles,Tassilo Pellegrini,Thomas Thurner,Axel Polleres,Sabrina Kirrane
|dblpUrl=https://dblp.org/rec/conf/i-semantics/HavurSPFMPTPK19
}}
==Automatic License Compatibility Checking==
https://ceur-ws.org/Vol-2451/paper-13.pdf
Automatic License Compatibility Checking
Giray Havur1,2 , Simon Steyskal1,2 , Oleksandra Panasiuk3 , Anna Fensel3 ,
Victor Mireles4 , Tassilo Pellegrini5 , Thomas Thurner4 , Axel Polleres1 , and
Sabrina Kirrane1
1
Vienna University of Economics and Business, Austria
2
Siemens AG Österreich, Austria
3
STI Innsbruck, University of Innsbruck, Austria
4
The Semantic Web Company, Austria
5
St. Pölten University of Applied Sciences, Austria
Abstract. In this paper, we introduce the Data Licenses Clearance Cen-
ter system, which not only provides a library of machine readable licenses
but also allows users to compose their own license. A demonstrator can
be found at https://www.dalicc.net.
1 Introduction
Licensing in general and rights clearance in particular are complex topics that
require a high level of domain expertise and legal expertise. Primary challenges
include the high transaction costs associated with the manual clearance of licens-
ing terms and conditions; the need for sufficient expertise to detect compatibility
conflicts between licenses; and the ability to resolve such conflicts. An alternative
approach could be to model licenses in a manner that supports automatic license
compatibility checking. Among the most prominent Rights Expression Language
(REL) vocabularies used to represent licenses are the Creative Commons Rights
Expression Language (ccREL)6 , the Open Digital Rights Language (ODRL)7 ,
and an ODRL profile called RightsML8 . When it comes to reasoning over license
representations, an early proposal for a generic logic for reasoning is provided by
Pucella and Weissman [7], but it has not been implemented with existing RELs
like ODRL or MPEG-21 nor has it been evaluated in practice. García and Gil
[2] propose an ontology to describe copyright issues in closed datasets for rights
clearance purposes. Hosking et al.[6] present a rule-based engine, built on top of
the Carneades Framework [3], to reason over various sets of licenses, while addi-
tionally suggesting potential licenses by which to safely share derived outputs.
Instead of applying deductive reasoning they used a non-monotonic formalism
suitable for modeling situations in which contradictory statements are being
processed. Villata and Gandon [8] and Governatori et al. [4] describe the for-
malization of a license composition tool for derivative works. They extend their
6
https://www.w3.org/Submission/ccREL/
7
https://www.w3.org/TR/odrl-model/
8
https://iptc.org/standards/rightsml/
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
License
odrl:Policy
odrl:permission odrl:obligation odrl:prohibition
odrl:duty
odrl:Permission odrl:Duty odrl:Prohibition
odrl:action
odrl:Action
dalicc:needsPermission
dalicc:needsDuty
owl:sameAs dalicc:needsProhibition
odrl:includedIn dalicc:Question
odrl:implies dalicc:excludesPermission
dalicc:excludesDuty
dalicc:question dalicc:excludesProhibition
dalicc:contradicts
Dependency Graph dalicc:Questionnaire Questionnaire
Fig. 1. Interaction between the constituent parts of the framework
research by introducing semantics based on a deontic logic [5] for the comparison
of the permissions, prohibitions and duties stated in a given license. The limita-
tion of existing work is the fact that compatibility can just be checked against
a handful of selected permissions, obligations and prohibitions and not against
a selection of licenses. In this paper, we present the Data Licenses Clearance
Center (DALICC) system9 , which focuses on extending existing vocabularies to
enable modeling and reasoning over several well-known license texts. With this
demo we aim to demonstrate: (i) our machine ODRL representation for a num-
ber of well-known license families (CC, Apache, BSD, MIT, GPL); and (ii) the
DALICC system that can be used to both generated custom licenses and check
automatically check license compatibility.
2 Modelling Licenses using ODRL
In DALICC licenses are modelled using ODRL, which was recently released
as a W3C recommendation. The model is further extended with a dependency
graph, which is necessary for checking license consistency, and a model that
underpins a dynamic questionnaire that enables users of the DALICC system
to search for licenses. Figure 1 depicts the central role of odrl:Action in inte-
grating the licenses, dependency graph and questionnaire. For the modelling,
we selected 14 commonly used licenses from 5 license families (CC, Apache,
MIT, BSD, GPL), which can be applied to various data assets, such as cre-
ative works, software and datasets. The ODRL information model is partic-
ularly suitable for modeling licenses in the form of policies that express per-
missions, prohibitions and duties related to the usage of assets. ODRL also
defines a vocabulary of general terms (e.g., odrl:reproduce, odrl:distribute,
odrl:modify) that can be further extended with terms from other vocabularies
such as CC REL10 . However, during our analysis we identified the need for ad-
ditional terms (e.g., dalicc:perpetual as a validity type, dalicc:worldwide as a
9
https://www.dalicc.net/
10
https://creativecommons.org/ns#
jurisdictional property, dalicc:chargeLicenseFee as permission and prohibition
actions, and dalicc:modificationNotice as a duty action), which we modelled
using the DALICC vocabulary. DALICC utilizes a dependency graph for rep-
resenting the semantic relationship between defined actions. The core function
of the dependency graph is to encode expert knowledge about the implicit and
explicit semantic dependencies between actions. The corresponding dependency
graph represents the semantics of an action in another action (e.g., odrl:sell
odrl:includedIn odrl:commercialize), implications derived from a specific ac-
tion (e.g., cc:Attribution odrl:implies cc:Notice), equalities (e.g., odrl:copy
owl:sameAs odrl:reproduce), and contradictions between specific actions (e.g.,
cc:ShareAlike dalicc:contradicts dalicc:addStatement). Additionally, the DAL-
ICC questionnaires are encoded using RDF, enabling multilingual interfaces and
rapid refactoring using the RDF editing capabilities of PoolParty Semantic Suite.
To this end, we have created four controlled vocabularies, one each for: (i) ques-
tions, (ii) question types, (iii) interaction between the UI and the License Search,
and (iv) interaction between the UI and the Composer. Each question is an
instance of dalicc:Question class, a subclass of skos:Concept, with three at-
tributes that define their appearance and behaviour in the UI: skos:prefLabel,
skos:definition, and rdf:type, all of which are adopted by the DALICC system.
3 Reasoning
To reason over licenses we use Answer Set Programming (ASP)[1], a declarative
(logic-programming-style) paradigm for solving combinatorial search problems
by defining and evaluating rule sets. Licenses are represented in ASP as a set of
rules of the form rule(L,C,I,𝛼,T) where L, C, I, 𝛼, and T correspond to license
name, category of rule, assignee, action, and asset, respectively.
Policies are derived from the RDF graphs of the licenses. Herein, a rule that
permits or prohibits the execution of an action on certain assets does not only
affect other rules that govern the execution of the same action on the same
asset(s) but also those permitting or prohibiting related actions on the same as-
set(s). In this sense, clingo is an alternative to extensive materialization, which
in this case is essential for search, and also enables listing sets of compatible
statements. This is necessary for effective computation of conflicts between li-
cences, in particular for identifying the conflicting and non-conflicting parts of
a license.
4 The DALICC System
The DALICC framework consists of the three main functional components,
namely: license library, license search, and license composer, as shown in Figure
2. The DALICC system, which is an implementation of the DALICC framework
is the result of coupling a Virtuoso11 triplestore, a Drupal12 based web appli-
cation, the PoolParty Semantic Suite13 , and a Clingo Answer Set Programming
11
https://virtuoso.openlinksw.com/
12
https://www.drupal.org
13
https://www.poolparty.biz/
System
License Library License Search License Composer
Data Sources
Reasoner
License Library, Customized
Dependency Graph & License
Questionnaire
Fig. 2. The DALICC Framework
Fig. 3. The License library UI
Fig. 5. The License composer UI
Fig. 4. The License search UI
(ASP) reasoner14 . This web application provides the user with three different
workflows that cover the functionality of the framework: (i) displaying the li-
censes in the license library; (ii) searching for a license that meets the user’s
needs; and (iii) composing customized licenses from scratch. The license library is
a repository that contains machine-readable and human-readable representations
of the licenses. Licenses properties are queried using SPARQL and presented to
the user in an easily digestible manner, as seen in Figure 3. In the case of license
search (cf. Figure 4), the user fills in a dynamic questionnaire which is used to
find the most suitable license based on their individual needs via communicating
with the reasoner. When the form is submitted, the underlying JavaScript trig-
gers a SPARQL query that retrieves the actions of type odrl:action and other
relations with respect to the answer. Afterwards, this information is sent to the
reasoner so that the reasoner returns the licenses that are consistent with the
given input. The license composer (cf. Figure 5) is a tool that allows customized
licenses to be easily created from a set of questions which are mapped to ODRL,
ccREL and DALICC vocabularies. In order to ensure the validity of the machine
readable licenses and the corresponding license compatibility assessment, both
14
https://potassco.org/clingo/
the development and the testing of the platform’s components have been carried
out in close collaboration with legal experts within the DALICC consortium.
5 Conclusion
In this paper, we discussed how licenses can be modeled using various Rights
Expression Languages and how reasoning can be applied to detect licensing
conflicts. The DALICC demonstrator is a viable proof of concept and illustrates
the practical applicability of semantic technologies for legal purposes. We are
planning to mature the system and extend its functional scope from license
management towards policy management.
Acknowledgements. DALICC was funded by the Austrian Federal Ministry
of Transport, Innovation and Technology (BMVIT) under the program “ICT of
the Future”. More information is available at https://iktderzukunft.at/en/
and https://dalicc.net/.
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