Workshop on "Personal Semantic Data" - Lisbon, Oct 11-15, 2010

Objectives

Personal information management (PIM) is an active area of interest for research and industry alike. While our time and energy resources remain constant, the amount of information that needs our attention grows exponentially with the advances in communications and information sharing tools.

The tools that we use to manage our personal information have evolved over time from the pen and paper day planners to their numerous digital replacements. The desktop used to be at the centre of the users' PIM universe, containing their contacts, emails, events, appointments, and to-do lists. However, as the amount of stored information and the number of applications available to handle it grew, desktop data became harder and harder to manage, as it was locked-in by applications and stored in application-specific formats. The Semantic Desktop is the result of applying Semantic Web technologies to the desktop, to better interlink personal data and make it easier to search, browse and organise. It lifted the data from the application silos and non-standard formats to a standard RDF-based representation, described using commonly agreed-upon ontologies.

Nowadays, the transition is made more and more towards mobile devices, the majority of which have Internet connectivity. This has lead to an increasing share of information, like calendar and email, being stored on users' various devices or in the cloud, because of hardware limitations like storage and processing power. Also, applications such as Chrome OS, Google Documents, or MS Office Live enable users to store personal documents in the Cloud, while many social relations are managed through social Web sites like Facebook or MySpace. In parallel, the Semantic Web has gained considerable momentum, especially through initiatives like Linking Open Data, that have generated a vast amount of structured data available on the Web. Furthermore, projects like FOAF and SIOC have enabled the publication of machine-readable information about people and their social interactions.

As more online services and applications become available to users and gain popularity, the boundaries between the desktop and the Web become less discernible. The desktop is no longer the single access point to personal information, but one of many personal information sources. Consequently, personal information is becoming more fragmented across multiple devices, requiring extra effort to synchronize, duplicate, search and browse. We believe that semantic technologies can improve significantly the user's experience and relieve some of the stress associated with managing disparate information.

Personal semantic data is scattered over several media, and while semantic technologies are already successfully deployed on the Web as well as on the desktop, data integration is not always straightforward. The transition from the desktop to a distributed system for PIM raises new challenges, which represent the subject of this workshop. Related research is being conducted in several disciplines like human-computer interaction, privacy and security, information extraction and matching. Through this workshop we would like to enable cross-domain collaborations to further advance the use of technologies from the Semantic Web and the Web of Data for Personal Information Management, and to explore and discuss approaches for improving PIM through the use of vast amounts of (semantic) information available online. In turn, this workshop is of interest to researchers in the areas of PIM, Linked Data, Web Sciences, Social Collaboration, and more.

The aim of this workshop is to bring together researchers and practitioners in the areas of Personal Information Management, user modeling, Semantic Web and Linked Data to share their visions, research achievements, and solutions, as well as to establish new collaborations in research and development. At the same time, we want to provide a platform for discussing research topics and challenges related to personal semantic data.
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