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Blog Förderjahr 2022 Stipendien Call #17 ID: 6335

Basic concepts or technologies of the semantic web regarding question answering over knowledge graphs

With information exploding on the web, people prefer to get precise answers to their questions rather than exploring and identifying them from a massive amount of data. Thus, question-answering systems have a significant influence on daily life as well as industry setting.
The semantic web, an extended version of the current web, provides a large number of data in the form of knowledge graphs. A knowledge graph, made in RDF (Resource Description Framework) model, represents and stores data in a semantically structured way using RDF facts to illustrate real-world entities with meaningful relations. The standard way to retrieve and fetch the data from RDF knowledge graphs is to use query languages such as SPARQL (RDF Query Language). However, building SPARQL queries for people without familiarity with query languages and the structure of knowledge graphs is difficult.
Thus, question answering over knowledge graphs has emerged to assist end users in accessing knowledge graphs and expressing information needs by asking questions in a natural language manner and getting direct answers.
In recent years, the research trend has been to propose and design solutions to semantically parse natural language questions and convert them to SPARQL queries. Therefore, this blog post explains the primary concepts and technologies in the semantic web required to develop these question-answering systems.
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