使用维基数据词条和项目从抽象表述中生成文本

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Semantic Web Pub Date : 2024-06-13 DOI:10.3233/sw-243564
Mahir Morshed
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引用次数: 3

摘要

Ninai/Udiron 是一个基于活函数的自然语言生成系统,它利用维基数据词条和项目中的知识,将事实陈述的抽象表述转化为人类可读的文本。组合系统首先根据这些抽象表述生成语法树(Ninai),然后根据这些语法树生成句子(Udiron)。该系统依靠单个词汇单位的信息和这些单位所代表概念的链接,以及用户可参与的各类函数中编码的规则,来决定要使用的词、短语和其他词素以及如何排列它们。各种系统设计选择都是为了高效地使用维基数据词目和条目中的信息,使不同的组件各自具有可贡献性和可扩展性,并使系统的整体输出结果具有可预期性和可分析性。这些目标与 Ninai/Udiron 最终为维基百科摘要项目提供动力以及托管于维基功能项目的意图相一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Wikidata lexemes and items to generate text from abstract representations
Ninai/Udiron, a living function-based natural language generation system, uses knowledge in Wikidata lexemes and items to transform abstract representations of factual statements into human-readable text. The combined system first produces syntax trees based on those abstract representations (Ninai) and then yields sentences from those syntax trees (Udiron). The system relies on information about individual lexical units and links to the concepts those units represent, as well as rules encoded in various types of functions to which users may contribute, to make decisions about words, phrases, and other morphemes to use and how to arrange them. Various system design choices work toward using the information in Wikidata lexemes and items efficiently and effectively, making different components individually contributable and extensible, and making the overall resultant outputs from the system expectable and analyzable. These targets accompany the intentions for Ninai/Udiron to ultimately power the Abstract Wikipedia project as well as be hosted on the Wikifunctions project.
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来源期刊
Semantic Web
Semantic Web COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
8.30
自引率
6.70%
发文量
68
期刊介绍: The journal Semantic Web – Interoperability, Usability, Applicability brings together researchers from various fields which share the vision and need for more effective and meaningful ways to share information across agents and services on the future internet and elsewhere. As such, Semantic Web technologies shall support the seamless integration of data, on-the-fly composition and interoperation of Web services, as well as more intuitive search engines. The semantics – or meaning – of information, however, cannot be defined without a context, which makes personalization, trust, and provenance core topics for Semantic Web research. New retrieval paradigms, user interfaces, and visualization techniques have to unleash the power of the Semantic Web and at the same time hide its complexity from the user. Based on this vision, the journal welcomes contributions ranging from theoretical and foundational research over methods and tools to descriptions of concrete ontologies and applications in all areas. We especially welcome papers which add a social, spatial, and temporal dimension to Semantic Web research, as well as application-oriented papers making use of formal semantics.
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