Jakub Dzikowski

Making your semantic application addictive: incentivizing users!

Since many semantic content creation methods and initiatives have been developed, a fully automatic process cannot be implemented. As a consequence, many application designers are interested in the question of how to motivate users to become a part of this endeavor, compensating the limitations of purely machine-driven content creation techniques. Methods from areas as diverse as community support, participation management, usability engineering, mechanism design, and social computing, can be applied to analyze semantically enabled applications, and design incentivized variants thereof. Building on top of these theories, we revisit fundamental design issues of semantic-content authoring technology focusing on the identification of a set of incentives that drives people to contribute, and on the ways such incentives can be translated into guidelines for technology and application designers. In order to show some best practices that designers should take into account encouraging large-scale user participation in semantic applications, we describe an experiment conducted with students of the Poznan University of Economics. Following the empirical findings and insights gained during the experimental operations, we propose some general guidelines, which provide software developers with a baseline to create semantics-based technologies and end-user applications that are not just functional, but facilitate and encourage user participation.

Towards the semantic web --- incentivizing semantic annotation creation process

One of the main obstacles hampering the adoption of semantic technologies, is the lack of interest of users to create semantic content. In this paper, we focus on the incentives that may be applied and embedded within an application, in order to motivate users to create semantically annotated content. As an example, we show a semantically-enhanced Social Web Recommendation application, called Taste It! Try It!, integrated with a Linked Data source and Social Network. We also discuss the findings from the experiments run with 140 users.

Challenges in Using Linked Data within a Social Web Recommendation Application to Semantically Annotate and Discover Venues

This paper focuses on a semantically-enhanced Social Web Recommendation application, called Taste It! Try It! It is a mobile restaurants’ review and recommendation application based on a Linked Data source and integrated with a social network. The application is consuming Linked Data (while creating the reviews), producing semantic annotations (about the reviewed entities) and then, querying the gathered data in order to offer personalized recommendations. In this paper, we focus only on the consumption and usage of Linked Data for the needs of social recommendation system and point out the challenges and shortcomings that need to be addressed.