TY - CHAP

T1 - Bridge-Generate: Scholarly Hybrid Question Answering

AU - Taffa, Tilahun Abedissa

AU - Usbeck, Ricardo

N1 - Publisher Copyright: © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

PY - 2025/5/23

Y1 - 2025/5/23

N2 - Answering scholarly hybrid questions requires access to bibliographic facts stored in structured data, such as a Knowledge Graph (KG) and textual information. Existing Scholarly Hybrid Question Answering (SHQA) approaches rely on retrieving KG triples and documents from the Wikipedia text corpus and prompt an LLM (Large Language Model) for answers. However, the retrieval is heavily keyword-based, introducing noise into the context. Furthermore, despite detecting the entities in the question, the models do not attempt any question analysis. Therefore, we propose a new SHQA system that employs a bridge-generate approach. During the bridge phase, our system recursively identifies entity-encapsulating phrases within the question and resolves the entities leveraging the underlying KGs. It then formulates assertion statements based on the resolved entities and their corresponding phrases. In the generation phase, the system auto-generates context guided by the question and the assertions. Finally, it returns an answer prompting an LLM with the generated context, the assertions, and the question. Our approach outperforms previous approaches, addressing the identified gaps.

AB - Answering scholarly hybrid questions requires access to bibliographic facts stored in structured data, such as a Knowledge Graph (KG) and textual information. Existing Scholarly Hybrid Question Answering (SHQA) approaches rely on retrieving KG triples and documents from the Wikipedia text corpus and prompt an LLM (Large Language Model) for answers. However, the retrieval is heavily keyword-based, introducing noise into the context. Furthermore, despite detecting the entities in the question, the models do not attempt any question analysis. Therefore, we propose a new SHQA system that employs a bridge-generate approach. During the bridge phase, our system recursively identifies entity-encapsulating phrases within the question and resolves the entities leveraging the underlying KGs. It then formulates assertion statements based on the resolved entities and their corresponding phrases. In the generation phase, the system auto-generates context guided by the question and the assertions. Finally, it returns an answer prompting an LLM with the generated context, the assertions, and the question. Our approach outperforms previous approaches, addressing the identified gaps.

KW - Business informatics

KW - Question Answering

KW - Hybrid Question Answering

KW - cholarly Ques- tion Answering

KW - Scholarly Hybrid Question Answering

UR - http://www.scopus.com/inward/record.url?scp=105009234715&partnerID=8YFLogxK

U2 - 10.1145/3701716.3715459

DO - 10.1145/3701716.3715459

M3 - Article in conference proceedings

SP - 1321

EP - 1325

BT - WWW Companion 2025 - Companion Proceedings of the ACM Web Conference 2025

A2 - Long, Guodong

A2 - Blumestein, Michale

A2 - Chang, Yi

A2 - Lewin-Eytan, Liane

A2 - Huang, Helen

A2 - Yom-Tov, Elad

PB - Association for Computing Machinery, Inc

CY - New York

T2 - ACM Web Conference 2025

Y2 - 28 April 2025 through 2 May 2025

ER -