  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.
