Streamlining DBpedia Queries with Natural Language Using Large Language Models

The capability to query knowledge bases like DBpedia using natural language is an emerging approach in the semantic web and linked data. This presentation highlights the use of GPT, a large language model (LLM), to examine its potential for interpreting natural language queries and retrieving information from linked data repositories. Think of the convenience of querying DBpedia with questions such as "Where was Albert Einstein born?" or "Who won the Nobel Prize in Literature?". To retrieve such information today, one must understand and write SPARQL queries. LLMs, like GPT-4, have the potential to translate these natural language queries into SPARQL, thereby making DBpedia more accessible to those without technical expertise in SPARQL. This approach improves the search experience and paves the way for more intuitive interaction with linked data. While there are challenges to this approach, including ensuring the accuracy of generated SPARQL queries and handling ambiguous natural language inputs, the integration of GPT-4 with DBpedia opens up a new avenue in information retrieval. This presentation will explore this promising approach, demonstrating its potential to modify our interaction with linked data and influence its practical use in the future.