Fusepool · Fusing and pooling information for product/service development and research (Fusepool)

Fusepool develops an user-adaptive «Living Knowledge Pool» for product development and research. Compared to existing search and knowledge management solutions, Fusepool provides two core benefits: the automated transformation of content from web-harvesting and participating organizations into structured Linked Open Data format and the automated group-specific optimization of knowledge finding and matching based on transfer learning from individual users. Instead of optimizing results only individually per user, Fusepool fuses anonymised user interactions to derive optimizations for specific user groups of users. Information mining and interlinking combine text mining, feature- and entity extraction with semantic web technologies. Content classification and entity identification enable automated enrichment and interlinking of information extracted from internal as well as web-harvested 'raw' content. In addition, Linked Open Data (LOD) from hundreds of data repositories such as Eurostat or DBPedia (Wikipedia) are accessed to pool knowledge related to the information need of the user. Moreover, 'raw' content that is transformed into machine-understandable content can be published as LOD for others to reuse it. Knowledge finding and matching refers to the semantics-aware search integrating content based on available metadata (e.g. classifications, entities) into a stream-lined application for finding and matching content to support the user's information needs. Advanced search features include refinement and filtering, query intent discovery, and proactive information gathering. In addition, recommendations provide the user with potentially relevant information and user dis/approval optimizes future recommendations. Visual analytics and graphical user interfaces present intuitively the complex information and analytical results. Users can develop and share layouts and even layouts are able to adapt to user needs based on past user interactions.