  The emerging citation-based QA systems are gaining more attention especially
in generative AI search applications. The importance of extracted knowledge
provided to these systems is vital from both accuracy (completeness of
information) and efficiency (extracting the information in a timely manner). In
this regard, citation-based QA systems are suffering from two shortcomings.
First, they usually rely only on web as a source of extracted knowledge and
adding other external knowledge sources can hamper the efficiency of the
system. Second, web-retrieved contents are usually obtained by some simple
heuristics such as fixed length or breakpoints which might lead to splitting
information into pieces. To mitigate these issues, we propose our enhanced web
and efficient knowledge graph (KG) retrieval solution (EWEK-QA) to enrich the
content of the extracted knowledge fed to the system. This has been done
through designing an adaptive web retriever and incorporating KGs triples in an
efficient manner. We demonstrate the effectiveness of EWEK-QA over the
open-source state-of-the-art (SoTA) web-based and KG baseline models using a
comprehensive set of quantitative and human evaluation experiments. Our model
is able to: first, improve the web-retriever baseline in terms of extracting
more relevant passages (>20\%), the coverage of answer span (>25\%) and self
containment (>35\%); second, obtain and integrate KG triples into its pipeline
very efficiently (by avoiding any LLM calls) to outperform the web-only and
KG-only SoTA baselines significantly in 7 quantitative QA tasks and our human
evaluation.
