| 8 | | == Deadlines == |
| | 8 | * Providing knowledge workers with tools for effective information organization and quick information access |
| | 9 | * Intelligent information search |
| | 10 | * Context-aware information assistance |
| | 11 | |
| | 12 | During the last decade, dozens of research projects worldwide were targeted to developing methodologies, technologies and tools directed to solve problems of knowledge workers. |
| | 13 | However, after years, semantic technologies are still not a part of daily work performed by office workers outside computer laboratories. Reasons are manifold: low user acceptance, huge modeling effort needed to bootstrap solutions. |
| | 14 | The goal of the workshop is to gather researchers who is working on integrating semantic technologies in habitual office environment to exchange information and knowledge about problems preventing successful implementation of nice looking semantic web ideas in real office environments and to discuss about possible solutions of these problems. |
| | 15 | |
| | 16 | Topics of interest include, but are not limited to: |
| | 17 | |
| | 18 | 1. Semantic annotations in COW |
| | 19 | * Tagging documents and e-mails using semantic data structures, e.g. Linked Data |
| | 20 | * Using RDFa in HTML Mails |
| | 21 | * Annotating e-mail attachments |
| | 22 | * Enriching document content with semantic mashups |
| | 23 | * Semantic e-mail markups in e-mail headers |
| | 24 | * Semantic annotation in Instant Messaging |
| | 25 | 1. Efficient search and navigation in semantically annotated collections of documents and e-mails |
| | 26 | * Semantic search in COW |
| | 27 | * Extending Microsoft search to support semantic search |
| | 28 | 1. Semi-automatic knowledge extraction from user interaction logs in office environment |
| | 29 | * Enriching semantic data structures by user interaction data |
| | 30 | * Supporting identified interaction habits based on semantic data structures |
| | 31 | 1. Semi-automatic document classification |
| | 32 | * E-mail/IM classification using background knowledge bases |
| | 33 | * Ontology-based classification |
| | 34 | * Classifying parts of bigger documents |
| | 35 | 1. Architectures supporting semantic technologies in computer-aided office work |
| | 36 | * Architectures for storing semantic document metadata (server-based vs. embedded) |
| | 37 | * Semantic indexing (documents, e-mail, IM) |
| | 38 | * Storing metadata related to parts of larger documents |
| | 39 | * Semantic data formats and vocabularies supporting e-mails and document annotations: RDF, eRDF, Linked Data |
| | 40 | * Semantic data structures to annotate user-system interaction in office environments [BSch] |
| | 41 | * Workflow-supportive architectures for digital collaborative work |
| | 42 | 1. Extending Office applications to utilize semantic technologies |
| | 43 | * Developing Plugins for integrating semantic technologies in MSOffice documents |
| | 44 | * Extending MSOffice SmartTags functionality to use semantic structures |
| | 45 | * PlugIns leveragig application interaction based on semantic technologies |
| | 46 | * Application integration based on semantic technologies |
| | 47 | * Semantic Communication Support Systems |
| | 48 | |
| | 49 | |
| | 50 | == Important Dates == |
