Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links

2023-09-11 · Minhao Zhang, Yongliang Ma, Yanzeng Li, Ruoyu Zhang, Lei Zou, Ming Zhou

Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions. To utilize multiple knowledge bases (KB), previous works merge all KBs into a single graph via entity alignment and reduce the problem to question-answering (QA) over the fused KB. In reality, various link relations between KBs might be adopted in QA over multi-KBs. In addition to the identity between the alignable entities (i.e. full link), unalignable entities expressing the different aspects or types of an abstract concept may also be treated identical in a question (i.e. partial link). Hence, the KB fusion in prior works fails to represent all types of links, restricting their ability to comprehend multi-KBs for QA. In this work, we formulate the novel Multi-KB-QA task that leverages the full and partial links among multiple KBs to derive correct answers, a benchmark with diversified link and query types is also constructed to efficiently evaluate Multi-KB-QA performance. Finally, we propose a method for Multi-KB-QA that encodes all link relations in the KB embedding to score and rank candidate answers. Experiments show that our method markedly surpasses conventional KB-QA systems in Multi-KB-QA, justifying the necessity of devising this task.

📄 PDF Abstract BibTeX arXiv:2309.05201

Code (0)

등록된 구현이 없습니다.

Tasks

Entity Alignment Question Answering

An Analysis of Visual Question Answering Algorithms

2017-03-28 · ICCV 2017 10 · Kushal Kafle, Christopher Kanan

In visual question answering (VQA), an algorithm must answer text-based questions about images. While multiple datasets for VQA have been created since late 2014, they all have flaws in both their content and the way alg…

Question Answering Visual Question Answering Visual Question Answering (VQA)

Learning to Order Sub-questions for Complex Question Answering

2019-11-11 · Yunan Zhang, Xiang Cheng, Yufeng Zhang, Zihan Wang 외

Answering complex questions involving multiple entities and relations is a challenging task. Logically, the answer to a complex question should be derived by decomposing the complex question into multiple simple sub-ques…

Question Answering Reinforcement Learning Reinforcement Learning (RL)

Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

2017-07-01 · CVPR 2017 7 · Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi 외

We introduce the task of Multi-Modal Machine Comprehension (M3C), which aims at answering multimodal questions given a context of text, diagrams and images. We present the Textbook Question Answering (TQA) dataset that i…

Question Answering Reading Comprehension Visual Question Answering Visual Question Answering (VQA)

Understanding Complexity in VideoQA via Visual Program Generation

2025-05-19 · Cristobal Eyzaguirre, Igor Vasiljevic, Achal Dave, Jiajun Wu 외

We propose a data-driven approach to analyzing query complexity in Video Question Answering (VideoQA). Previous efforts in benchmark design have relied on human expertise to design challenging questions, yet we experimen…

Code Generation Question Answering Video Question Answering Visual Question Answering

Knowledge Base Question Answering via Encoding of Complex Query Graphs

2018-10-01 · EMNLP 2018 10 · Kangqi Luo, Fengli Lin, Xusheng Luo, Kenny Zhu

Answering complex questions that involve multiple entities and multiple relations using a standard knowledge base is an open and challenging task. Most existing KBQA approaches focus on simpler questions and do not work …

Knowledge Base Question Answering Question Answering Semantic Parsing