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- Angelov, Krasimir, 1978, et al.
(författare)
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Typeful Ontologies with Direct Multilingual Verbalization
- 2012
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Ingår i: 2nd Workshop on Controlled Natural Languages, CNL 2010; Marettimo Island, Sicily; Italy; 13 September 2010 through 15 September 2010. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 0302-9743 .- 1611-3349. - 9783642311741 ; 7175, s. 1-20
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Konferensbidrag (refereegranskat)abstract
- We have developed a methodology for the representation of ontologies in a strictly typed language with dependent types. The methodology is supported by an experiment where we translated SUMO (Suggested Upper-Merged Ontology) to GF (Grammatical Framework). The representation of SUMO in GF preserves the expressivity of the original ontology, adding to this the advantages of a type system and built-in support for natural language generation. SUMO is the largest open-source ontology describing over 10,000 concepts and the relations between them, along with a number of first-order axioms, which are further on used in performing automated reasoning on the ontology. GF is a type-theoretical grammar formalism mainly used for natural language applications. Through the logical framework that it incorporates, GF allows a consistent ontology representation, and thanks to its grammatical features the ontology is directly verbalized in a number of controlled natural languages.
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| 3. |
- Caprotti, Olga, 1964, et al.
(författare)
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High-quality translation: Molto tools and applications
- 2012
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Ingår i: The fourth Swedish Language Technology Conference (SLTC).
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Konferensbidrag (refereegranskat)abstract
- MOLTO (Multilingual On Line Translation, FP7-ICT-247914, www.molto-project.eu) is a European project focusing on translation on the web. MOLTO targets translation that has production quality, that is, usable for quick and reliable dissemination of information. MOLTO’s main focus is to increase the productivity of such translation systems, building on the technology of GF (Grammatical Framework) and its Resource Grammar Library. But MOLTO also develops hybrid methods which increase the quality of Statistical Machine Translation (SMT) by adding linguistic information, or bootstrap grammatical models from statistical models. This paper gives a brief overview of MOLTO’s latest achievements, many of which are more thoroughly described in separate papers and available as web-based demos and as open-source software.
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