EWEK - QA : Enhanced Web and Efficient Knowledge Graph Retrieval for Citation-based Question Answering Systems (2024)

Abstract: 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 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.

## methodology our approach consists of two main components: knowledge extraction ( §3.1) and answer composition ( §3.2), which operate sequentially. when presented with a question, we initiate the knowledge extraction phase to gather the pertinent information required for answering. subsequently, we employ an open-source llm to generate a cohesive final answer solely based on the collected information. see fig. 2 for the full pipeline. ## conclusion we introduce ewek-qa, an efficient and generic qa system that is capable of answering both open domain and multi-hop reasoning questions. contrary to prior works, our system relies on two external knowledge modalities: kgs and the web. we develop an adaptive web retriever to extract coherent and complete quotes from webpages. furthermore, our tog-e method eschews reliance on llms to extract the most relevant kg triples. extensive experimental results on a variety of dataset types show significant efficiency gains over baselines. for future work, we aim to further improve the kg subgraph extraction module through more powerful embedding methods and experiment with using bigger backbone llm models.