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    @Article{OJWT_2018v5i1n04_Cruz,
        title     = {Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers},
        author    = {Christophe Cruz and
                     Cyril Nguyen Van and
                     Laurent Gautier},
        journal   = {Open Journal of Web Technologies (OJWT)},
        issn      = {2199-188X},
        year      = {2018},
        volume    = {5},
        number    = {1},
        pages     = {23--30},
        note      = {Special Issue: Proceedings of the International Workshop on Web Data Processing \& Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence (KI) in Berlin, Germany.},
        url       = {http://nbn-resolving.de/urn:nbn:de:101:1-2018093019302313586232},
        urn       = {urn:nbn:de:101:1-2018093019302313586232},
        publisher = {RonPub},
        bibsource = {RonPub},
        abstract = {This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the twocommunities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, byapproach the similarity between words or by revealing hidden semantic relations. Thus, these controlledvocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the end user. The major aim is to find a non-expert vocabulary from semantic rules to enrich the knowledge of the ontology and improve the indexing of the items (i.e. wine) and the recommendation process.}
    }