Abstract
We present a lightweight framework for processing uncertain emergent knowledge that comes from multiple resources with varying relevance. The framework is essentially RDF-compatible, but allows also for direct representation of contextual features (e.g., provenance). We support soft integration and robust querying of the represented content based on well-founded notions of aggregation, similarity and ranking. A proof-of-concept implementation is presented and evaluated within large scale knowledge-based search in life science articles. © Springer-Verlag Berlin Heidelberg 2009.
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CITATION STYLE
Nováček, V., & Decker, S. (2009). Towards lightweight and robust large scale emergent knowledge processing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5823 LNCS, pp. 456–472). Springer Verlag. https://doi.org/10.1007/978-3-642-04930-9_29
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