Background: Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively.
Abstract-Thispaper describes SIIP (Speaker Identification Integrated Project) a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusion of a series of speech analytic algorithms which includes speaker model recognition, gender identification, age identification, language and accent identification, keyword and taxonomy spotting. A full integrated system is proposed ensuring multisource data management, advanced voice analysis, information sharing and efficient and consistent man-machine interactions.
In this paper we present the Corese-NeLI semantic web browser dedicated to navigating resources in the infectious disease domain. We describe an overview of the semantic web browser and outline its functionality and the knowledge organization system used as a background knowledge for both the annotation and search processes. The evaluation of the vocabularybased annotation, essential for the semantic browser, uses the National eLectronic Library of Infection as a test bed and demonstrates over 96% correct annotations .
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