Models to represent linguistic linked data
Models to represent linguistic linked data
复制标题
表示语言关联数据的模型
DOI：
--
复制
发表时间：
2018
影响因子：
2.5
通讯作者：
Asunción Gómez
中科院分区：
文献类型：
--
作者：
Julia Bosque;Jorge Gracia;Elena Montiel;Asunción Gómez
摘要随着语义网和计算语言学社区对语言关联数据（LLD）的兴趣不断增加，以及对LLD的贡献数量迅速增长，对LLD资源开发感兴趣的学者（特别是语言学家）有时会发现很难确定哪种机制适合他们的需求，以及哪些挑战已经得到解决。这篇评论旨在介绍最先进的模型，本体论及其扩展，以表示语言资源LLD的语言内容的性质，他们旨在编码为重点。在这项工作中，四个基本组的模型进行了区分：模型表示的主要元素的词汇资源（组1），词汇表开发的扩展模型组1和本体，提供更多的粒度上的特定级别的语言分析（组2），语言数据类别的目录（组3）和其他模型，如语料库模型或面向服务的（组4）。包含在这四个群体的贡献进行了描述，突出了他们的重用，由社会和建模的挑战，仍然要面对的。
Abstract As the interest of the Semantic Web and computational linguistics communities in linguistic linked data (LLD) keeps increasing and the number of contributions that dwell on LLD rapidly grows, scholars (and linguists in particular) interested in the development of LLD resources sometimes find it difficult to determine which mechanism is suitable for their needs and which challenges have already been addressed. This review seeks to present the state of the art on the models, ontologies and their extensions to represent language resources as LLD by focusing on the nature of the linguistic content they aim to encode. Four basic groups of models are distinguished in this work: models to represent the main elements of lexical resources (group 1), vocabularies developed as extensions to models in group 1 and ontologies that provide more granularity on specific levels of linguistic analysis (group 2), catalogues of linguistic data categories (group 3) and other models such as corpora models or service-oriented ones (group 4). Contributions encompassed in these four groups are described, highlighting their reuse by the community and the modelling challenges that are still to be faced.