With the growth of the Semantic Web, the requirements on storing and querying RDF has become more sophisticated. When a larger amount of data has to be managed, queries in structured query languages, such as SPARQL, are not always powerful enough. Use of additional keywords for querying can further reduce the result set towards the actual relevant answers, however, SPARQL only provides complete string matching or filtering based on regular expressions, which is a very slow operation. In contrast, state of the art Information Retrieval (IR) techniques provide sophisticated features such as keyword search, lemmatisation, stemming and ranking. In this paper we present a combination of structured RDF queries and full-text search. It is implemented as an extension of an established RDF store (Sesame) with IR capabilities using the text search library Lucene, without requiring modifications to existing RDF query languages.

The Semantic Desktop is a means to support users in Personal Information Management (PIM). Using the open source software prototype Gnowsis, we evaluated the approach in a two month case study in 2006 with eight participants. Two participants continued using the prototype and were interviewed after two years in 2008 to show their long-term usage patterns. This allows us to analyse how the system was used for PIM. Contextual interviews gave insights on behaviour, while questionnaires and event logging did not. We discovered that in the personal environment, simple has-Part and is-related relations are sufficient for users to file and re-find information, and that the personal semantic wiki was used creatively to note information.

The Semantic Desktop is a means to support users in Personal Information Management (PIM). It provides an excellent test bed for Semantic Web technology: resources (e. g., persons, projects, messages, documents) are distributed amongst multiple systems, ontologies are used to link and annotate them. Finding information is a core element in PIM. For the end user, the search interface has to be intuitive to use, natural language queries provide a simple mean to express requests. State of the art semantic search engines focus on fact retrieval or on semantic document retrieval. We combine both approaches to search the Semantic Desktop exploiting all available information. Our semantic search engine, built on semantic teleporting and spreading activation, is able to answer natural language queries with facts, e. g., a specific phone number, and/or relevant documents. We evaluated our approach on ESWC 2007 data in comparison with Google site search.

The Semantic Desktop is a means to support users in Personal Information Management (PIM). It provides an excellent test bed for Semantic Web technology: resources (e. g., persons, projects, messages, documents) are distributed amongst multiple systems, ontologies are used to link and annotate them. Finding information is a core element in PIM. For the end user, the search interface has to be intuitive to use, natural language queries provide a simple mean to express requests. State of the art semantic search engines focus on fact retrieval or on semantic document retrieval. We combine both approaches to search the Semantic Desktop exploiting all available information. Our semantic search engine, built on semantic teleporting and spreading activation, is able to answer natural language queries with facts, e. g., a specific phone number, and/or relevant documents. We evaluated our approach on ESWC 2007 data in comparison with Google site search.

ConTag is an approach to generate semantic tag recommendations for documents based on Semantic Web ontologies and Web 2.0 services. We designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic Web ontology. Due to ConTag we are able to show that the information provided by Web 2.0 services in combination with a Semantic Web ontology enables the generation of relevant semantic tag recommendations for documents. The main contribution of this work is a semantic tag recommendation process based on a choreography of Web 2.0 services.

This paper introduces the NEPOMUK project which aims to create a standard and reference implementation for the Social Semantic Desktop. We outline the requirements and functionalities that were identified for a useful Semantic Desktop system and present an architecture that fulfills these requirements which was acquired by incremental refinement of the architecture of existing Semantic Desktop prototypes. The NEPOMUK project is primarily motivated by three real-life industrial use-cases, we briefly outline these and the processes used to extract required functionalities from the people working in these areas today, and we present a selection of typical tasks where the Semantic Desktop could be of benefit.

This paper examines issues on Philosophy of Artificial Intelligence (AI), Cognitive Science and Mental Models. The paper provides a philosophical grounding for the researchers in Personal Information Management (PIM). An overview is given on various philosophical aspects of computer-based activities. Discussions on the theories relevant to understand the goals for the Semantic Desktop community are elicited. Philosophical theories are not immediately transparent to the programmers, but the ideas discussed here are intended to emphasize a theoretical foundation, with respect to Semantic Desktop long term goals. The goal of this paper is to examine the theories of Philosophy and to provide a conceptual idea to design user-intuitive Semantic Desktops. The paper tries to induce scientific curiosity among the Semantic Desktop researchers as well as to develop the future Semantic Desktops to realize Weak AI.

The Semantic Desktop brings the ideas and the technologies of the Semantic Web into the personal computer desktop. As a prerequisite for applying Semantic Web technologies to a certian domain of knowledge an ontological model of the domain is required. In the Gnowsis Semantic Desktop, the PIMO (Personal Information Model Ontology) addresses this problem by providing a generic lightweight ontology whose classes model the mian concepts involved in the daily activities of a person: places, organizations, persons, etc. But in order to be fully useful for a certain user, this generic model needs to be personalized and populated, adding more classes and concrete instances of the existent classes. As the process of manual population could be tedious and time consuming, in this paper we propose an alternative which tries to exploit the information that the user provides while performing Web searches. Apart from populating the PIMO, our approach is useful in resource annotation.

iMapping is a technique for visually structuring information objects. It supports the full range from informal note taking over semi-structured personal information management to formal knowledge models. With iMaps, users can easily go from overview to fine-grained structures while browsing, editing or refining the knowledge base in one comprehensive view. An iMap is comparable to a large white-board where information items can be positioned like post-its but also nested into each other. Spatial browsing and zooming as well as graphical editing facilities make it easy to structure content in an intuitive way. iMapping builds on a zooming user interface approach to facilitate navigation and to help users maintain an overview in the knowledge space. While a first implementation is being developed, iMapping is still in a conceptual stage. In this paper we describe the iMapping approach and how it tries to combine and extend the advantages of other approaches.

The Semantic Desktop needs a well-thought use of ontologies and ontology languages. Existing ontology languages like RDF/S, OWL, SKOS and Topic Maps are very well suited for certain application areas, but do not fulfill some of the requirements given on the Semantic Desktop. In this report, a new ontology language, extending RDF/S, is proposed, the PIMO ontology language, which addresses the requirements of the Semantic Desktop and uses existing solutions as an inspiration to build a suitable solution. The language contains a core upper ontology, defining basic classes for things, concepts, resources, persons, etc. and also stops at these basic entities. Extending the ontology definitions of classes and relations is possible by PIMO-domain ontologies. The core application area of the PIMO-language is to allow individual persons to express their own mental models in a structured way, the different mental models can then be integrated based on matching algorithms or on domain ontologies. Based on the core upper ontology elements, each user can extend his personal mental model in an open manner. Accompanying to the description of the ontology is a RDF/S version of the ontology language, created using the popular Protégé tool. An example of the mental model of a user is given, the fictional user "Paul" is further described. With the gnowsis-beta open source software, an implementation based on the PIMO language exists, that allows validating ontology files. A web-service for that will be provided soon. The ontology language, this document, the open-source reference implementation and the example documents can provide a stable basis for discussions on this topic and allow you to extend your own work.

In this paper we present lessons learned from building a Semantic Desktop system, the gnowsis beta. On desktop computers, semantic software has to provide stable services and has to reflect the personal view of the user. Our approach to ontologies, the Personal Information Model PIMO allows to create tagging services like del.icio.us on the desktop. A semantic wiki allows further annotations. Continuous evaluations of the system helped to improve it. These results were created in the EPOS research project and are available in the open source projects Aperture, kaukoluwiki, and gnowsis and will be continued in the Nepomuk project. By using these components, other developers can create new desktop applications the web 2.0 way.