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对于词汇关联数据的需要

The Need for Lexicalization of Linked Data
课程网址: http://videolectures.net/w3cworkshop2012_mccrae_data/  
主讲教师: John McCrae
开课单位: 爱尔兰国立大学
开课时间: 2012-07-12
课程语种: 英语
中文简介:
虽然链接数据通常独立于语言,但是对这些数据的解释需要自然的语言标识符,以便对最终用户有意义。对于链接数据的许多应用程序,特别是在多语言上下文中,有必要超越简单的字符串标签,提供对链接数据实体的词汇化的更丰富的描述,例如生成数据的自然语言描述。为了解决这个问题,我们提出了一个模型,我们称之为Lemon(本体的词汇模型),它在语义和句法层次上区分标签。Lemon的目标是建立在现有的表示词汇信息的模型上,但它是简洁的、描述性的和模块化的。此外,该模型旨在弥补现有关联数据云(以RDF(S)和OWL等格式描述)和快速增长的语言关联数据云(其中已经存在大量多语言数据)之间的差距。我将展示一些例子,说明我们如何使用协作编辑技术与Lemon模型一起创建此类数据,而不需要付出很大的努力,以及如何将这些技术应用于回答关于链接数据的自然语言问题等任务
课程简介: While linked data is frequently independent of language, the interpretation of this data requires natural language identifiers in order to be meaningful to the end user. For many applications of linked data, especially in multilingual contexts, it is necessary to go beyond the simple string label and provide a richer description of the lexicalization of the linked data entities, for example by generating natural language descriptions of the data. To address this gap we have proposed a model, which we call Lemon (Lexicon Model for Ontologies), that distinguishes the labels at both the semantic and syntactic levels. Lemon aims to build on existing models for representing lexical information, but is concise, descriptive and modular. Furthermore, this model is designed to bridge the gap between the existing linked data cloud, described in formats such as RDF(S) and OWL and the rapidly growing linguistic linked data cloud, where a significant amount of multilingual data already exists. I will show examples of how we can use collaborative editing techniques with the Lemon model to create such data without significant effort and how this can be applied to tasks such as answering natural language questions over linked data. 
关 键 词: 计算机科学; 多语种信息访问; 关联数据
课程来源: 视频讲座网
最后编审: 2021-12-21:liyy
阅读次数: 54