Using Multiple RDF Knowledge Graphs for Enriching ChatGPT Responses
This addresses the issue of unreliable AI-generated content for users by integrating structured knowledge graphs, though it is incremental as it builds on existing technologies like ChatGPT and RDF KGs.
The authors tackled the problem of ChatGPT providing plausible but incorrect responses without evidence by developing GPToLODS, a prototype that enriches ChatGPT answers with information from hundreds of RDF Knowledge Graphs, enabling real-time fact-checking and entity annotation using integrated data from 400 RDF KGs and over 412 million entities.
There is a recent trend for using the novel Artificial Intelligence ChatGPT chatbox, which provides detailed responses and articulate answers across many domains of knowledge. However, in many cases it returns plausible-sounding but incorrect or inaccurate responses, whereas it does not provide evidence. Therefore, any user has to further search for checking the accuracy of the answer or/and for finding more information about the entities of the response. At the same time there is a high proliferation of RDF Knowledge Graphs (KGs) over any real domain, that offer high quality structured data. For enabling the combination of ChatGPT and RDF KGs, we present a research prototype, called GPToLODS, which is able to enrich any ChatGPT response with more information from hundreds of RDF KGs. In particular, it identifies and annotates each entity of the response with statistics and hyperlinks to LODsyndesis KG (which contains integrated data from 400 RDF KGs and over 412 million entities). In this way, it is feasible to enrich the content of entities and to perform fact checking and validation for the facts of the response at real time.