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Digital data theft is difficult to detect and typically it also takes a long time to discover that data has been stolen. This paper introduces a data-driven approach based on Markov chains to create believable decoy project folders which can assist in detecting potentially ongoing attacks. This can be done by deploying these intrinsically valueless folders between real project folders and bymonitoring interactions with them. We present our approach and results from a user study demonstrating the believability of the generated decoy folders.

Human computation can address complex computational problems by tapping into large resource pools for relatively little cost. Two prominent human-computation techniques - games with a purpose (GWAP) and microtask crowdsourcing - can help resolve semantic-technology-related tasks, including knowledge representation, ontology alignment, and semantic annotation. To evaluate which approach is better with respect to costs and benefits, the authors employ categorization challenges in Wikipedia to ultimately create a large, general-purpose ontology. They first use the OntoPronto GWAP, then replicate its problem-solving setting in Amazon Mechanical Turk, using a similar task-design structure, evaluation mechanisms, and input data.

We discuss the challenge of scalable dissemination and communication approaches in a world where the number of channels is growing exponentially. The web, Web 2.0, and semantic channels have provided a multitude of interaction possibilities providing significant potential for yield, brand, and general reputation management. Our goal is to enable smaller organizations to fully exploit this potential. To achieve this, we have developed a new methodology based on distinguishing and explicitly interweaving content and communication as a central means for achieving content reusability, and thereby scalability over various heterogeneous channels.

A multitude of approaches to match, merge, and integrate ontologies, and more recently, to interlink RDF data sets, have been proposed over the past years, making ontology alignment one of the most active and at the same time mature area of research and development in semantic technologies. While advances in the area cannot be contested, it is equally true that full automation of the ontology-alignment process is far from being feasible; human input is often indispensable for the bootstrapping of the underlying methods, and for the validation of the results. The question of acquiring and leveraging such human input remains largely unaddressed, in particular when it comes to the incentives and motivators that are likely to make users invest their valuable time and effort in alignment tasks such as entity interlinking and schema matching, which can be domain-knowledge-intensive, technical, or both. In this chapter, the authors present SpotTheLink, a game whose purpose addresses this challenge, demonstrating how knowledge-intensive tasks in the area of the Semantic Web can be collaboratively solved by a community of non-experts in an entertaining fashion.

A large share of tasks in semantic-content authoring crucially rely on human intelligence [4]. This holds for many aspects of ontology engineering, but also for ontology-based annotation, be that for data-oriented resources, such as images, audio and video content, or for functionality, such as Web services and APIs. In previous work of ours we have extensively discussed the importance of motivators and incentive mechanisms to encourage a critical mass of Internet users - in particular, users beyond the boundaries of the semantic-technologies community - to contribute to such inherently human-driven tasks. Through OntoGame we have provided a framework for casual games which capitalizes on fun and competition as two key motivators for people to willingly invest their valuable time and effort in semantic-technologies-related tasks, whose technical details hide behind an entertaining collaboratively game experience [2]. This paper presents the newest release of the OntoGame series, called SpotTheLink, which addresses this challenge in the area of ontology alignment.

Interlinking is without doubt one of the most active and mature areas of research and development in semantic technologies. Over the last decade or more a multitude of approaches to match, merge and integrate ontologies, both at the schema and instance levels have been proposed and successfully applied to resolve heterogeneity issues and, more recently, to interlink RDF data sets exposed over the Web as part of the Linked Open Data Cloud. The strengths and weaknesses of existing interlinking solutions, as well as their natural limitations and principled combinations have been intensively studied, not least through community projects such as the Ontology Alignment Evaluation Initiative. Human input remains a key ingredient of the process, either as a source of domain knowledge used to train matching algorithms and to build the underlying knowledge base, or to validate automatically computed results. In this paper we describe how such human input could be acquired and used to enhance the results of existing data interlinking technology via crowdsourcing. In a survey of data interlinking tools we identify several aspects of the interlinking process that crucially rely on human contributions and explain how these aspects could be subject to a semantically enabled human computation architecture that can be set-up by extending interlinking platforms such as Silk with direct interfaces to popular microtask platforms such as Amazon's Mechanical Turk.