In this paper, we introduce the Data Licenses Clearance Center 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. 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 licensing terms and conditions; the need for suficient 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 RightsML 8. 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 additionally 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 formalization 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/
License odrl:Policy odrl:permission y odrl:obligation odrl:prohibition t u odrl:Permission : odrl:Duty d l r d o odrl:action odrl:Action owl:sameAs odrl:includedIn odrl:implies dalicc:contradicts Dependency Graph dalicc:needsPermission dalicc:needsDuty dalicc:needsProhibition dalicc:Question dalicc:excludesPermission
dalicc:excludesDuty
dalicc:question dalicc:excludesProhibition
dalicc:Questionnaire Questionnaire
research by introducing semantics based on a deontic logic [
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 integrating 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 creative works, software and datasets. The ODRL information model is particularly suitable for modeling licenses in the form of policies that express permissions, 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 additional 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 representing the semantic relationship between denfied 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 action (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 DALICC 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) questions, (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 attributes 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
To reason over licenses we use Answer Set Programming (ASP)[
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 afect 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 asset(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 efective computation of conflicts between licences, in particular for identifying the conflicting and non-conflicting parts of a license. 4
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 application, 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/ License Library
License Search
License Composer Reasoner
Data Sources
DepLQeicnuedenessnteiocLyninbGarraiarrepy,h & CuLsictoemnsizeed (ASP) reasoner14. This web application provides the user with three diferent workflows that cover the functionality of the framework: (i) displaying the licenses 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 ifnd the most suitable license based on their individual needs via communicating with the reasoner. When the form is submitted, the underlying JavaScript triggers 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
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/.