A Comparative Study of NLP based Semantic Web Standard model using SPARQL database

2021 International Conference on Computing Sciences (ICCS) Pub Date : 2021-12-01 DOI:10.1109/ICCS54944.2021.00010

C. Raghavendra Rao, J. Babu, S. J. Pimo, Asmita Dixit, Sushma Jaiswal, Aatif Jamshed

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引用次数: 1

Abstract

The work offers evidence of a philosophical idea that many sectors will draw considerable interest. A remote natural language interface (NLI) is the term for the issue of knowledge bases (KBs). The framework uses software from CoreNLP for natural language technology and allows KBs to use the SPARQL query language. Natural Language Processing (NLP) uses the semantics of a natural language query to interpret and produce related information. The query can be asked about KBs containing linked information with properly defined relationships. The related data fits the RDF model in terms of semantic threefold: subject-predicate-objet relationships. The RDF model. The data are connected to the RDF model. Any KB can be understood semantically with our NLI. AI will learn how to grasp the semantics of the RDF data stored in the KB by having the correct training data. Similar information derives from KB questions thanks to its capacity to understand RDF data. Questions can be translated into SPARQL and asked by relational expertise on the KB.

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基于SPARQL数据库的NLP语义Web标准模型的比较研究

这项工作为一个哲学思想提供了证据，即许多部门将引起相当大的兴趣。远程自然语言接口(NLI)是知识库问题的术语。该框架使用CoreNLP的软件进行自然语言技术，并允许KBs使用SPARQL查询语言。自然语言处理(NLP)使用自然语言查询的语义来解释和产生相关信息。可以查询包含具有正确定义关系的链接信息的KBs。相关数据在语义三重方面符合RDF模型:主体-谓词-客体关系。RDF模型。数据连接到RDF模型。任何知识库都可以用我们的NLI在语义上理解。AI将学习如何通过拥有正确的训练数据来掌握存储在知识库中的RDF数据的语义。由于KB具有理解RDF数据的能力，类似的信息可以从KB问题中派生出来。问题可以翻译成SPARQL，并由知识库上的关系专家提出。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 International Conference on Computing Sciences (ICCS)

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