BourgeoisGentilV1, Hal, Corpus, bibRecord, 000016

QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

Identifieur interne : 000016 ( Hal/Corpus ); précédent : 000015; suivant : 000017

QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

Auteurs : Dennis Diefenbach ; Pedro Migliatti ; Omar Qawasmeh ; Vincent Lully ; Kamal Singh ; Pierre Maret

Source :

RBID : Hal:hal-02121400

English descriptors

mix :
- Multi-Knowledge, Multilingual, Question Answering, Semantic Web.

Abstract

We present QAnswer, a Question Answering system which queries at the same time 3 core datasets of the Semantic Web, that are relevant for end-users. These datasets are Wikidata with Lexemes, LinkedGeodata and Musicbrainz. Additionally, it is possible to query these datasets in English, German, French, Italian, Spanish, Pour-tuguese, Arabic and Chinese. Moreover, QAnswer includes a fall-back option to the search engine Qwant when the answer to a question cannot be found in the datasets mentioned above. These features make QAnswer as the first prototype of a Question Answering System over a considerable part of the LOD cloud.

Url:

https://hal.archives-ouvertes.fr/hal-02121400

DOI: 10.1145/3308558.3314124

Links to Exploration step

Hal:hal-02121400

Le document en format XML

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<front><div type="abstract" xml:lang="en">We present QAnswer, a Question Answering system which queries at the same time 3 core datasets of the Semantic Web, that are relevant for end-users. These datasets are Wikidata with Lexemes, LinkedGeodata and Musicbrainz. Additionally, it is possible to query these datasets in English, German, French, Italian, Spanish, Pour-tuguese, Arabic and Chinese. Moreover, QAnswer includes a fall-back option to the search engine Qwant when the answer to a question cannot be found in the datasets mentioned above. These features make QAnswer as the first prototype of a Question Answering System over a considerable part of the LOD cloud.</div>
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                    <surname>Migliatti</surname>
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                <author role="aut">                  <persName>                    <forename type="first">Vincent</forename>
                    <surname>Lully</surname>
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                  <email type="domain">sorbonne-universite.fr</email>
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                </author>
                <author role="aut">                  <persName>                    <forename type="first">Kamal</forename>
                    <surname>Singh</surname>
                  </persName>
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                  <email type="domain">univ-st-etienne.fr</email>
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                <author role="aut">                  <persName>                    <forename type="first">Pierre</forename>
                    <surname>Maret</surname>
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              <monogr>                <title level="m">WWW '19 The World Wide Web Conference</title>
                <meeting>                  <title>The Web Conference</title>
                  <date type="start">2019-05-13</date>
                  <settlement>San Francisco</settlement>
                  <country key="US">United States</country>
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            <abstract xml:lang="en">We present QAnswer, a Question Answering system which queries at the same time 3 core datasets of the Semantic Web, that are relevant for end-users. These datasets are Wikidata with Lexemes, LinkedGeodata and Musicbrainz. Additionally, it is possible to query these datasets in English, German, French, Italian, Spanish, Pour-tuguese, Arabic and Chinese. Moreover, QAnswer includes a fall-back option to the search engine Qwant when the answer to a question cannot be found in the datasets mentioned above. These features make QAnswer as the first prototype of a Question Answering System over a considerable part of the LOD cloud.</abstract>
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QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

QAnswer: A Question Answering prototype bridging the gap between a considerable part of the LOD cloud and end-users

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