Towards Cloud-Based Knowledge Capturing Based on Natural Language Processing
| Autor: | Matthias Hemmje, Michael Kaufmann, Michael Fuchs, Matthäus Schmedding, Christian Nawroth, Holger Brocks |
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| Rok vydání: | 2015 |
| Předmět: |
business.industry
Computer science Collaborative knowledge Scientific Cloud Software development Cloud computing computer.software_genre Asset (computer security) Named Entity Recognition World Wide Web Knowledge Management Named-entity recognition Tacit knowledge Support Vector Machines Storage Cloud Personal knowledge management Faceted search General Earth and Planetary Sciences Asset management Artificial intelligence business computer Natural language processing Natural Language Processing General Environmental Science |
| Zdroj: | Cloud Forward Procedia Computer Science |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2015.09.236 |
| Popis: | The organized capturing and sharing of knowledge is very important, and a lot of tools, such as wikis, social communities and knowledge-management or e-learning portals, exist for supporting this purpose. The community content- and knowledge-capturing, management and sharing portal of the European project “Realising an Applied Gaming Eco-system” (RAGE)††www.rageproject.eu combines such tools. The goal of the RAGE project is to boost the collaborative knowledge asset management for software development in European applied gaming (AG) research and development (R&D). To support this process, the so-called RAGE ecosystem implements a portal to support the related asset, content and knowledge exchange between diverse actors in AG communities. Therefore, the community portal in RAGE is designed as a so-called ecosystem and is intended to provide its users different tools for the capturing, management, and sharing of knowledge. In this study, we rely on the term and model definition of spiraling knowledge exchange between explicit and tacit knowledge given by Nonaka and Takeuchi.1 To achieve the goal of extracting, i.e., externalizing and explicitly representing and sharing this knowledge to its users, we propose to generate a taxonomy for faceted search automatically by extracting named entities form the knowledge sources and to classify documents using Support Vector Machines (SVM). In this paper we present our architectural approach for the NLP-based IR concepts and discuss how cloud services based on data distribution and cloud computing can improve the outcome of our system. |
| Databáze: | OpenAIRE |
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