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        <title>Transforming the Cologne Digital Sanskrit Dictionaries into OntoLex-Lemon</title>
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    <name type="personal">
        <namePart type="given">Francisco</namePart>
        <namePart type="family">Mondaca</namePart>
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        <namePart type="given">Felix</namePart>
        <namePart type="family">Rau</namePart>
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            <title>Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)</title>
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        <name type="personal">
            <namePart type="given">Maxim</namePart>
            <namePart type="family">Ionov</namePart>
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        <name type="personal">
            <namePart type="given">John</namePart>
            <namePart type="given">P</namePart>
            <namePart type="family">McCrae</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Chiarcos</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Thierry</namePart>
            <namePart type="family">Declerck</namePart>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Bosque-Gil</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jorge</namePart>
            <namePart type="family">Gracia</namePart>
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    <abstract>The Cologne Digital Sanskrit Dictionaries (CDSD) is a large collection of complex digitized Sanskrit dictionaries, consisting of over thirty-five works, and is the most prominent collection of Sanskrit dictionaries worldwide. In this paper we evaluate two methods for transforming the CDSD into Ontolex-Lemon based on a modelling exercise. The first method that we evaluate consists of applying RDFa to the existent TEI-P5 files. The second method consists of transforming the TEI-encoded dictionaries into new files containing RDF triples modelled in OntoLex-Lemon. As a result of the modelling exercise we choose the second method: to transform TEI-encoded lexical data into Ontolex-Lemon by creating new files containing exclusively RDF triples.</abstract>
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            <start>11</start>
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%0 Conference Proceedings
%T Transforming the Cologne Digital Sanskrit Dictionaries into OntoLex-Lemon
%A Mondaca, Francisco
%A Rau, Felix
%Y Ionov, Maxim
%Y McCrae, John P.
%Y Chiarcos, Christian
%Y Declerck, Thierry
%Y Bosque-Gil, Julia
%Y Gracia, Jorge
%S Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020)
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-36-8
%G English
%F mondaca-rau-2020-transforming
%X The Cologne Digital Sanskrit Dictionaries (CDSD) is a large collection of complex digitized Sanskrit dictionaries, consisting of over thirty-five works, and is the most prominent collection of Sanskrit dictionaries worldwide. In this paper we evaluate two methods for transforming the CDSD into Ontolex-Lemon based on a modelling exercise. The first method that we evaluate consists of applying RDFa to the existent TEI-P5 files. The second method consists of transforming the TEI-encoded dictionaries into new files containing RDF triples modelled in OntoLex-Lemon. As a result of the modelling exercise we choose the second method: to transform TEI-encoded lexical data into Ontolex-Lemon by creating new files containing exclusively RDF triples.
%U https://aclanthology.org/2020.ldl-1.2
%P 11-14

Markdown (Informal)

[Transforming the Cologne Digital Sanskrit Dictionaries into OntoLex-Lemon](https://aclanthology.org/2020.ldl-1.2) (Mondaca & Rau, LDL 2020)

• Transforming the Cologne Digital Sanskrit Dictionaries into OntoLex-Lemon (Mondaca & Rau, LDL 2020)

ACL

• Francisco Mondaca and Felix Rau. 2020. Transforming the Cologne Digital Sanskrit Dictionaries into OntoLex-Lemon. In Proceedings of the 7th Workshop on Linked Data in Linguistics (LDL-2020), pages 11–14, Marseille, France. European Language Resources Association.