Rewarding people is common in several contexts, such as
human resource management and crowdsourcing applications. However,
designing a reward strategy is not straightforward, as it requires considering different parameters. These parameters include, for example,
management of rewarding tasks and identifying critical features, such
as the type of rewards and possibilities such as gamification. Moreover,
the lack of a common terminology introduces the problem of communication among experts and prevents integration among different reward
strategies. An ontology can offer a common understanding among domain experts and flexible management of rewarding parameters. Apart
from that, an ontology can also help in the interrelationship and integration between different reward schemes employed by different service
providers. In this paper, we present REWARD, a general-purpose ontology for capturing various common features of diverse reward schemes.
This ontology is a result of the CAP-A European project and its application to the crowdsourcing domain, but it is designed to cover different
needs and domains

Chrysakis, Ioannis

Dimou, Anastasia

Flouris, G.

Patkos, T.

Verborgh, Ruben

English

Ghent University Academic Bibliography

REWARD: Ontology for reward schemesIoannis Chrysakis1,2, Giorgos Flouris1, Theodore Patkos1,Anastasia Dimou2, and Ruben Verborgh21 FORTH, Institute of Computer Science, Greece{hrysakis,fgeo,patkos}@ics.forth.gr2 IDLab, Dept. of Electronics and Information Systems, UGent – imec, Belgium{ioannis.chrysakis,anastasia.dimou,ruben.verborgh}@ugent.beAbstract. Rewarding people is common in several contexts, such ashuman resource management and crowdsourcing applications. However,designing a reward strategy is not straightforward, as it requires con-sidering different parameters. These parameters include, for example,management of rewarding tasks and identifying critical features, suchas the type of rewards and possibilities such as gamification. Moreover,the lack of a common terminology introduces the problem of communi-cation among experts and prevents integration among different rewardstrategies. An ontology can offer a common understanding among do-main experts and flexible management of rewarding parameters. Apartfrom that, an ontology can also help in the interrelationship and inte-gration between different reward schemes employed by different serviceproviders. In this paper, we present REWARD, a general-purpose ontol-ogy for capturing various common features of diverse reward schemes.This ontology is a result of the CAP-A European project and its appli-cation to the crowdsourcing domain, but it is designed to cover differentneeds and domains.1 IntroductionRewarding is a common strategy for improving people’s effectiveness in differentdomains, such as business3, games [5], applications and services [2], or organi-zational workflows [7]. A successful reward strategy significantly improves theengagement of the applied target audience [1]. For example, by rewarding specifictasks’ accomplishment, user loyalty increases4, and continuous growth, recurringengagement and personal or team improvement are ensured [5].Although studies analyze features of successful reward strategies [6,9], mostare tailor-made for specific target audience needs. Domain experts design a re-ward strategy from scratch, even though it is not straightforward as it requiresredesigning and adapting to the specific target audience. Moreover, the lack of acommon terminology introduces the problem of communication among expertsand prevents integration among different reward strategies.3 https://www.shopify.com/enterprise/ecommerce-loyalty-programs4 https://www.justuno.com/blog/how-to-effectively-use-loyalty-programs-to-increase-customer-retention/2 I. Chrysakis et al.A conceptual model [10] can help domain experts in the design process, bysetting a well-defined terminology, and in the combination of reward strategies.One way of implementing a conceptual model is to build an ontology. An ontologyhelps as well in the flexible adaptation of new features in any applied rewardstrategy and can enable integration among different service providers. Thus, ageneral-purpose ontology affects different beneficiaries, from service providers toend users. Service providers benefit from the flexibility and interoperability inthe design process and actual application. End users may transfer features fromone service provider to another (e.g., by exchanging points).In this paper, we present REWARD, a general-purpose ontology that imple-ments a conceptual model designed to represent a reward strategy. The ontologyis published at https://w3id.org/reward-ontology/.REWARD enhances and facilitates employing a reward strategy, by adopt-ing common features of reward strategies, and casting them to a well-definedterminology. The ontology’s concepts and relationships can give an extra levelof common understanding, expressiveness and flexible knowledge manipulationthrough exploiting semantic web technologies. Thus, our proposed ontologicalengineering process requires appropriate (minor) changes at schema or instancelevel for applying any reward strategy. REWARD can help improve interoper-ability and integration among different reward schemes employed by differentservice providers and define a uniform process for creating reward strategies.2 The REWARD OntologyWe build a general-purpose ontology that describes concepts employed by rewardstrategies, such as Tasks, Points, Badges, Tiers and Rewards [1, 5, 7, 9, 11]. Foreach concept we created a respective class (Fig.1). Tasks are related to actionsto reach conditions to earn a reward. Thus, instances of the class Task captureinformation about the applied rewarding actions per reward strategy.Tasks can be distinguished to Platform-Based and Point-based following aplatform and user-centric approach respectively [11]. The former is related tothe users’ interaction with the applied system (platform) that is initiated by theservice provider. In this case the rewarding is based on exclusive user’s inter-action with this platform, by following specific workflow scenarios. The latteris accomplished by the user to earn points due to execution of defined tasks.This distinction allows us to support tasks that are not necessarily bound to theapplied platform or do not require a specific workflow of execution. Moreover,platform-based tasks allow the service provider to define specific tasks that arecritical for the optimal performance of the system, and assign them to specificusers; this differentiates platform-based tasks from point-based ones, which arefreely selected by users without any prompt or encouragement by the system.These categories of tasks are represented as respective subclasses (is-a relation-ship) of the class Task. To support different levels of tasks according to variousparameters such as difficulty of execution we introduce the class of Task Levelwhich is connected with the Task class (via property: belongsTo).REWARD: Ontology for reward schemes 3Fig. 1. The REWARD Ontology (top) and the CAP-A implementation (bottom)Points quantify users’ effort and as such they are related with the tasks [8].In any reward strategy, a Point System classifies each user to a Tier accordingto currently earned points [8] and defines the redemption process, which deter-mines the types of rewards users can claim with their points. Specifically, thisinformation is ensured to the ontology through the properties isMatchedTo andisRedeemedBy respectively. Through multiple instantation, REWARD can sup-port different reward schemes per user by applying a different point system. Inthis case we need to define different adjustment values for instances of Tier andTask Level class through the properties TaskLevelAdjustment and TierAdjust-ment respectively. For each point system the ontology keeps a name throughthe property hasName. Apart from the current points used for redemption, theontology models the total acquired points for capturing users’ activity throughthe respective properties: hasCurrentPoints and hasTotalPoints.The users’ activity is related to the defined Badges which represent the de-gree of user recognition [4] and is a type of Rewards. Rewards can be intrinsic, ifusers’ effort is recognized based on internal motivation parameters, such as cu-riosity and self improvement, or extrinsic, if the motivation is based on externalmotivation parameters, such as earning a badge, a gift or a coupon [5].Rewards are modeled under Intrinsic or Extrinsic class [5]. Intrinsic rewardsare categorized in three general subclasses: Self Improvement, Community Build-ing and Visibility [5,8] and Other Intrinsic class to capture rest cases. These canbe ensured through the application of playful tasks that enable community com-mitment and increase peer recognition among all users.Badges are implemented as subclass of the Extrinsic class, which also con-tains other subclasses (Monetary, Gift, Coupon) [8] and Other Extrinsic class to4 I. Chrysakis et al.capture rest cases. Badges and tiers are modeled as instances of Badge and Tierclass respectively. For any critical notification the user receives feedback capturedin the respective Feedback class, a fundamental feature for reward strategies [8].The ontology supports various types of queries allowing the extraction ofbasic information regarding reward strategies. We present a set of representativecompetency queries that can be easily answered through REWARD: (i) What isthe tier of a user U? (ii) What rewards has user U gained? (iii) What is the levelof task T? (iv) Which types of tasks have been completed and how many are they?(v) What type of rewards have already been redeemed by users? (vi) How manyactive points does user U have for the point system P? (vii) Given a minimumdefined reward value, which point system(s) support any type of reward?3 ApplicationWe present a use case implemented in the context of CAP-A project5 (Fig.1, bot-tom part). The REWARD Ontology is used as a general-model for building therewarding framework of CAP-A which aims to engage users in participating incrowdsourcing tasks for improving privacy awareness on mobile applications [3].We define instances of the Point Based class related to the privacy context,that lead to rewarding, such as Annotation on Term of Service documents andadding of Evidence on a privacy topic. Four task levels are defined to facilitatetask management. Each of them represents different level of difficulty and cap-tured in the ontology as subclasses of the Task Level class (Task Level 1, TaskLevel 2, Task Level 3, Task Level 4 ). Furthermore, each task level subclass de-termines the task’s visibility and applicability according to the user’s tier andthe Point System. User’s tier is calculated according to the earned points.In the CAP-A project, we use the following tiers: Baby, Grown-Up, Novice,Enthusiast, Warrior, Expert, Guru, Royal which are implemented as instancesof Tier class. Rewards are stored as instances of the respective Reward class andinclude free features on mobile applications or acquirement of specific Badges.CAP-A Badges are modeled as instances of the Badge class (Inactive, Sleepy,Social/Buddy, Super Star, On Fire, Ambassador), while the free app features aremodeled as instances of the Coupon class. The badges are given to users basedon their activity in terms of specific task accomplishments, continuous work orgained expertise, but we skip further details due to lack of space.The Rewarding Loyalty Programs by Air flight companies is another usecase where the REWARD ontology can be used. An instance of a travel taskbelongs to Point-Based class. Tasks can be defend depending on the loyalty pro-gram, such as booking accommodation, renting a car, shopping etc., and withtwo levels of difficulty, depending om whether they require verification by ser-vice providers (e.g., Reclaim miles). The Point System denotes the appropriatelower point bounds for promoting users to different tiers (e.g., blue, silver, goldtier). The rewards are mostly extrinsic, in terms of free tickets, discounts and5 https://www.cap-a.euREWARD: Ontology for reward schemes 5coupons. Frequent Travelers are instances of User class that uses the specificloyalty program.Consequently, we conclude that REWARD can be adapted easily to thesedifferent domains as it is designed to be. Our future plans include evaluation ofthe ontology in more use-cases and domains. Finally, as REWARD provides ageneric model for designing any reward scheme, it leaves room for collaborationamong service providers by offering more features on their common end users.The first step for service providers would be to try matching their existing rewardschemes to REWARD in order to incorporate features such as consolidation ofpoints or offering combined rewards.AcknowledgementsThis work has been supported by the CAP-A project which has received fundingfrom the European Union’s Horizon 2020 research and innovation programmeunder the NGI TRUST grant agreement no 825618. Ruben Verborgh is a post-doctoral fellow of the Research Foundation – Flanders (FWO). The authorswish to thank Yannis Roussakis, Elias Tzortzakakis and Elisjana Ymeralli forcontributions in the validation process of CAP-A use case.References1. Antikainen, M.J., Vaataja, H.K.: Rewarding in open innovation communities–howto motivate members. International Journal of Entrepreneurship and InnovationManagement (2010)2. Detwiller, D., Sparks, R.: Rewarding users for sharing digital content (2012), uSPatent App. 13/306,9173. Flouris, G., Patkos, T., Chrysakis, I., Konstantinou, I., Nikolov, N., Papadakos,P., Pitt, J., Roman, D., Stan, A., Zeginis, C.: Towards a collective awareness plat-form for privacy concerns and expectations. In: OTM Confederated InternationalConferences” On the Move to Meaningful Internet Systems”. Springer (2018)4. Hamari, J., Eranti, V.: Framework for designing and evaluating game achievements.In: Digra conference. Citeseer (2011)5. Kavaliova, M., Virjee, F., Maehle, N., Kleppe, I.A.: Crowdsourcing innovation andproduct development: Gamification as a motivational driver. Cogent Business &Management (2016)6. Morschheuser, B., Hamari, J., Koivisto, J.: Gamification in crowdsourcing: a re-view. In: 49th Hawaii International Conference on System Sciences. IEEE (2016)7. Prendergast, C.: The provision of incentives in firms. Journal of economic literature(1999)8. Reiners, T., Wood, L.C.: Gamification in Education and Business. Springer (2015)9. Scekic, O., Truong, H.L., Dustdar, S.: Incentives and rewarding in social comput-ing. Communications of the ACM (2013)10. Wand, Y., Storey, V.C., Weber, R.: Analyzing the meaning of a relationship. ACMTransactions on Database Systems (1999)11. Yang, D., Xue, G., Fang, X., Tang, J.: Crowdsourcing to smartphones: Incentivemechanism design for mobile phone sensing. In: Proceedings of the 18th annualinternational conference on Mobile computing and networking. ACM (2012)

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