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Question Answering System Over Semantic Web
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AI
DOI：10.1109/ACCESS.2021.3067942.png)
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摘要
En 中文
The Semantic Web contains a large amount of data in the form of knowledge bases. Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions. Although many systems have been developed over the years, it remains a challenge that most systems yet to require improvements to increase the accuracy for correct interpretation of the question and provide an answer. Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query templates for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD-8 dataset present good performance and can help users to find answers to their questions.
Keyword:
Semantics
Knowledge discovery
Knowledge based systems
Syntactics
Resource description framework
Complexity theory
Feature extraction
Question answering
natural language processing
knowledge base
DBpedia
SPARQL
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
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引用论文
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PREAI
分享收藏DBpedia - A large-scale, multilingual knowledge base extracted from WikipediaDBpedia-从维基百科中提取的大规模多语言知识库.png)
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SEMANTIC WEBIF2.9
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