QUINT: Interpretable Question Answering over Knowledge Bases

2017-09-01 · EMNLP 2017 9 · Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, Gerhard Weikum

We present QUINT, a live system for question answering over knowledge bases. QUINT automatically learns role-aligned utterance-query templates from user questions paired with their answers. When QUINT answers a question, it visualizes the complete derivation sequence from the natural language utterance to the final answer. The derivation provides an explanation of how the syntactic structure of the question was used to derive the structure of a SPARQL query, and how the phrases in the question were used to instantiate different parts of the query. When an answer seems unsatisfactory, the derivation provides valuable insights towards reformulating the question.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Named Entity Recognition (NER)Question Answering Semantic Parsing

SQuINTing at VQA Models: Introspecting VQA Models with Sub-Questions

2020-01-20 · CVPR 2020 6 · Ramprasaath R. Selvaraju, Purva Tendulkar, Devi Parikh, Eric Horvitz 외

Existing VQA datasets contain questions with varying levels of complexity. While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities…

Visual Question Answering (VQA)

An Interpretable Reasoning Network for Multi-Relation Question Answering

2018-01-15 · COLING 2018 8 · Mantong Zhou, Minlie Huang, Xiaoyan Zhu

Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model call…

Question Answering Relation

Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models

2026-04-12 · Mehmet Can Şakiroğlu, H. Altay Güvenir, Kamer Kaya arxiv

Generating multiple-choice questions (MCQs) with difficulty estimation remains challenging in automated MCQ-generation systems used in adaptive, AI-assisted education. This study proposes a novel methodology for generati…

Knowledge Graphs

Large Language Models as Interpolated and Extrapolated Event Predictors

2024-06-15 · Libo Zhang, Yue Ning

Salient facts of sociopolitical events are distilled into quadruples following a format of subject, relation, object, and timestamp. Machine learning methods, such as graph neural networks (GNNs) and recurrent neural net…

Knowledge Graphs Question Answering Relation

On the Limits of Interpretable Machine Learning in Quintic Root Classification

2026-02-26 · Rohan Thomas, Majid Bani-Yaghoub arxiv

Can Machine Learning (ML) autonomously recover interpretable mathematical structure from raw numerical data? We aim to answer this question using the classification of real-root configurations of polynomials up to degree…

Interpretable Machine Learning Knowledge Distillation