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information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance
algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search
With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldnpsilat make users satisfied. This paper presents
presented. These vectors are defined by a matrix based on problems, Concept Unique Identifier (CUI) Keywords and CUI Categories. The CUIs are automatically received by the Unified Medical Language System Knowledge Server (UMLS-KS) and stored into ontologies. Finally, an example where twelve cardiology problems vectors are
Automatic question answering system is a hot issue in the field of natural language processing, and is playing an increasingly important role in the long-distance teaching through networks. This paper proposes an ontology-based automatic question answering system model, at first, build restricted area ontology, then
We present a knowledge-based system to extract product feature-orientation (sentiment) pairs from on-line product reviews. Unlike the vast majority of existing approaches, our system first extracts strong implicit opinions, before searching for explicit product feature keywords. We call this the "opinion (O) first
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