Privacy preserving content analysis, indexing and retrieval for social search applications

[no abstract

Providing Social Sharing Functionalities in LearnWeb2.0

Marenzi, I., Zerr, S., & Nejdl, W. (2008). Providing Social Sharing Functionalities in LearnWeb2.0. In R. Koper, K. Stefanov & D. Dicheva (Eds). Proceedings of the 5th International TENCompetence Open Workshop "Stimulating Personal Development and Knowledge Sharing" (pp. 9-14). October, 30-31, 2008, Sofia, Bulgaria: TENCompetence Workshop. [For the whole proceedings please see also http://hdl.handle.net/1820/1961 ]Within the TENCompetence project we are working on an open source infrastructure for the creation, storage and exchange of learning objects and knowledge resources. We implemented LearnWeb2.0 - a prototype, which provides appropriate functionalities for the aggregation and annotation of Web 2.0 resources for lifelong competence development activities. This paper focuses on the next steps planned, describing the main functionalities to be implemented in LearnWeb2.0: resource selection, batch annotation and sharing, notification using SpreadCrumbs, resource aggregation using GroupMe and sequencing, motivated by a knowledge sharing scenario at the University of Pavia.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Zerber+R: Top-k Retrieval from a Confidential Index

Zerr, S., Olmedilla, D., Nejdl, W., & Siberski, W. (2009). Zerber+R: Top-k Retrieval from a Confidential Index. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (pp. 439-449). March, 24-26, 2009, Saint Petersburg, Russia (ISBN: 978-1-60558-422-5).Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking. In this paper we present Zerber+R -- a ranking model which allows for privacy-preserving top-k retrieval from an outsourced inverted index. We propose a relevance score transformation function which makes relevance scores of different terms indistinguishable, such that even if stored on an untrusted server they do not reveal information about the indexed data. Experiments on two real-world data sets show that Zerber+R makes economical usage of bandwidth and offers retrieval properties comparable with an ordinary inverted index.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

DBpedia is at the core of the Linked Open Data Cloud and widely used in research and applications. However, it is far from being perfect. Its content suffers from many flaws, as a result of factual errors inherited from Wikipedia or incomplete mappings from Wikipedia infobox to DBpedia ontology. In this work we focus on one class of such problems, un-typed entities. We propose a hierarchical tree-based approach to categorize DBpedia entities according to the DBpedia ontology using human computation and paid microtasks. We analyse the main dimensions of the crowdsourcing exercise in depth in order to come up with suggestions for workflow design and study three different workflows with automatic and hybrid prediction mechanisms to select possible candidates for the most specific category from the DBpedia ontology. To test our approach, we run experiments on CrowdFlower using a gold standard dataset of 120 previously unclassified entities. In our studies human-computation driven approaches generally achieved higher precision at lower cost when compared to workflows with automatic predictors. However, each of the tested workflows has its merit and none of them seems to perform exceptionally well on the entities that the DBpedia Extraction Framework fails to classify. We discuss these findings and their potential implications for the design of effective crowdsourced entity classification in DBpedia and beyond

Towards analytics and collaborative exploration of social and linked media for technology-enchanced learning scenarios

Social Web applications such as "Flickr", "Youtube" and "Slideshare" offer a vast body of multimedial knowledge, discoverable through the appropriate search interfaces and API's. This extensive information source, however, is largely unstructured and the available metadata is typically limited to title, tags and description for a resource. On the other hand, Linked Web Data is both structured and well described through a variety of metadata. Combining those sources opens promising direction for knowledge discovery and, at the same time, new challenges for collaborative searching in various Technology-Enchanced Learning Scenarios. In this paper, we explore how to support (collaborative) search in such scenarios through an initial analysis of the Web data landscape and introduce early results from efforts on exploiting Linked Data techniques to solve critical issues in this context

ID5.21.1 - Integration of LearnWeb and KRService into TENCompetence - LearnWeb2.0 v.0.4

Mazzetti, A., Dicerto, M., Grigorov, A., Georgiev, A., Zerr, S., & Mendez, C. (2009). ID5.21.1: Integration of LearnWeb and KRService into TENCompetence - LearnWeb2.0 v.0.4 - Available under the three clause BSD licence, Copyright TENCompetence Foundation.This document accompanies the release of the version 0.4 of LearnWeb2.0, here on called for convenience: LearnWeb v.0.4 and KRService v.0.4. This document contains the Release Notes, the link to sources and the URL for running and installing the application.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

ID5.15: New Core specifications v2

Mazzetti, A., Dicerto, M., Grigorov, A., Zerr, S., De Coi, J. L., Kawase, R., Perez, M., Roldan, A., Mittal, P. (2009). ID5.15: New Core specifications v2.This document describes the new functionalities for LearnWeb/KRService, which will be implemented in the new version called V0.3 and which will be available in late spring 2009. The main topics cover: resource functionalities, integration with other TENCompetence tools and social functionalities. This document describes LearnWeb/KRService in terms of: ARCHITECTURE, USER INTERFACE, RESOURCE FUNCTIONALITIES, INTEGRATION FUNCTIONALITIES, SOCIAL FUNCTIONALITIES.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

D5.4 - Integration results, aggregates internal deliverables ID5.20, ID5.21.1, ID5.21.2, ID5.22

Mazzetti, A., Dicerto, M., Portelli, G., Grigorov, A., Georgiev, A., Yordanova, K., Zerr, S., Kawase, R., Kemkes, P., Perez, M., Mendez, C., & Merino, B. (2009). D5.4 - Integration results, aggregates internal deliverables ID5.20, ID5.21.1, ID5.21.2, ID5.22. TENCompetence.This deliverable aggregates the following internal deliverables (as described in DIP-4 Version 1.3): ID5.20: Final Specification of Integrated LearnWeb and KRService, ID5.21.1: Integration of LearnWeb and KRService into TENCompetence (LearnWeb2.0 v.0.4 for conference EC-TEL), ID5.21.2: Final version of LearnWeb and KRService integrated into TENCompetence (LearnWeb2.0 v.1.0), ID5.22: Final Evaluation of the Integrated LearnWeb and KRService.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org