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A good knowledge search engine should consider both criteria - informativeness and confidence - and combine them into a single scoring and ranking measure. State-of-the-art ranking models in IR are based on statistical language models, LM’s for short. They have been successfully applied to passage retrieval for question answering, cross-lingual search, ranking elements in semistructured XML data, and other advanced IR tasks [14]. , qm } with several keywords qi is seen as a sample from this distribution.

The envisioned change in search UI’s may resemble QA systems, but has a very different search architecture. Today’s QA systems would not map questions into formal queries, but rather aim to classifiy questions into fine-grained topics, or map them to the most similar natural-language question for which the system already has answers. So the query processing for knowledge search, as discussed in this paper, is different from current QA technology and would be a major step forward towards better knowledge answers.

Modeling Documents as Mixtures of Persons for Expert Finding. W. ) ECIR 2008. LNCS, vol. 4956, pp. 309–320. Springer, Heidelberg (2008) 31. : Handbook on Ontologies, 2nd edn. Springer, Heidelberg (2009) 32. : EntityAuthority: Semantically Enriched Graph-Based Authority Propagation. In: WebDB 2007 (2007) 33. : YAGO: a Core of Semantic Knowledge. In: WWW 2007 (2007) 34. : YAGO: A Large Ontology from Wikipedia and WordNet. Journal of Web Semantics 6(39) (2008) 35. : SOFIE: a Self-Organizing Framework for Information Extraction.

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Attacking Digital Watermarks by Sion, Atallah

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