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- item-1 hasContent maria-maleshkova.
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- item-2 hasContent lukas-zilka.
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- item-3 hasContent petr-knoth.
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- item-4 hasContent carlos-pedrinaci.
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- 8 creator alexander-oconnor.
- 8 creator david-lewis.
- 8 creator dominic-jones.
- 8 creator yalemisew-m.-abgaz.
- 8 type InProceedings.
- 8 label "A Semantic Model for Integrated Content Management, Localisation and Language Technology Processing".
- 8 sameAs 8.
- 8 abstract "Providers of products and services are faced with the dual challengeof supporting the languages and individual needs of the global customer whilealso accommodating the increasing relevance of user-generated content. As aresult, the content and localisation industries must now evolve rapidly frommanually processing predicable content which arrives in large jobs to the highlyautomated processing of streams of fast moving, heterogeneous andunpredictable content. This requires a new generation of digital contentmanagement technologies that combine the agile flow of content fromdevelopers to localisers and consumers with the data-driven languagetechnologies needed to handle the volume of content required to feed thedemands of global markets. Data-driven technologies such as statisticalmachine translation, cross-lingual information retrieval, sentiment analysis andautomatic speech recognition, all rely on high quality training content, which inturn must be continually harvested based on the human quality judgments madeacross the end-to-end content processing flow. This paper presents themotivation, approach and initial semantic models of a collection of researchdemonstrators where they represent a part of, or a step towards, documenting ina semantic model the multi-lingual semantic web.".
- 8 hasAuthorList authorList.
- 8 isPartOf proceedings.
- 8 keyword "Content Management".
- 8 keyword "Language Technology and Interoperability".
- 8 keyword "Localisation".
- 8 keyword "Multilingual Web".
- 8 title "A Semantic Model for Integrated Content Management, Localisation and Language Technology Processing".
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- item-1 hasContent dominic-jones.
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- item-2 hasContent alexander-oconnor.
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- item-3 hasContent yalemisew-m.-abgaz.
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- item-4 hasContent david-lewis.
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- 9 creator alan-rector.
- 9 creator fennie-liang.
- 9 creator robert-stevens.
- 9 type InProceedings.
- 9 label "OntoVerbal-M: a Multilingual Verbaliser for SNOMED CT".
- 9 sameAs 9.
- 9 abstract "OntoVerbal-M is an ontology verbaliser that transforms OWL intofluent natural language paragraphs in multiple languages. We describe theapplication of OntoVerbal-M to SNOMED CT, whereby SNOMED CT classesare presented as textual paragraphs in both English and Mandarin through theuse of natural language generation. SNOMED CT is a large description logicbased terminology for recording in electronic health records. Often, neither thelabels nor the description logic definitions in SNOMED CT are easy for usersto understand. Furthermore, information is increasingly being recorded, not justusing individual SNOMED CT concepts, but using dynamically createddescription logic expressions (“post-coordinated” concepts). Such postcoordinatedexpressions can have no pre-assigned labels. In this contextautomatic verbalisation into multiple languages will be useful both forunderstanding and quality assurance of SNOMED CT definitions, and forhelping different language-speaking-users to understand and share postcoordinatedexpressions.".
- 9 hasAuthorList authorList.
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- 9 keyword "Multilingual Generation".
- 9 keyword "Ontology Verbalisation".
- 9 keyword "Ontology verbaliser".
- 9 keyword "SNOMED verbalisation".
- 9 title "OntoVerbal-M: a Multilingual Verbaliser for SNOMED CT".
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- item-1 hasContent fennie-liang.
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- item-2 hasContent robert-stevens.
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- item-3 hasContent alan-rector.
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- 2 creator enrique-alba.
- 2 creator jorge-martinez-gil.
- 2 creator jose-f-aldana-montes.
- 2 type InProceedings.
- 2 label "Optimizing Ontology Alignments by Using Genetic Algorithms".
- 2 sameAs 2.
- 2 abstract "In this work we present GOAL (Genetics for Ontology Alignments) a new approach to compute the optimal ontology alignment function for a given ontology input set. Althought this problem could be solved by an exhaustive search when the number of similarity measures is low, our method is expected to scale better for a high number of measures. Our approach is a genetic algorithm which is able to work with several goals: maximizing the alignment precision, maximizing the alignment recall, maximizing the f-measure or reducing the number of false positives. Moreover, we test it here by combining some cutting-edge similarity measures over a standard benchmark, and the results obtained show several advantages in relation to other techniques.".
- 2 hasAuthorList authorList.
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- 2 keyword "genetic algorithms".
- 2 keyword "ontology alignment".
- 2 keyword "semantic integration".
- 2 title "Optimizing Ontology Alignments by Using Genetic Algorithms".