TY - CHAP

T1 - Knowledge Graph Question Answering Using Graph-Pattern Isomorphism

AU - Vollmers, Daniel

AU - Jalota, Rricha

AU - Moussallem, Diego

AU - Topiwala, Hardik

AU - Ngomo, Axel-Cyrille Ngonga

AU - Usbeck, Ricardo

PY - 2021

Y1 - 2021

N2 - Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.

AB - Knowledge Graph Question Answering (KGQA) systems are often based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.

KW - Informatics

KW - question answering

KW - Basic Graph Pattern

KW - isomorphism

KW - QUALD

UR - https://www.mendeley.com/catalogue/378e5114-69cf-31c9-b426-a8790999cd12/

U2 - 10.48550/arXiv.2103.06752

DO - 10.48550/arXiv.2103.06752

M3 - Chapter

SN - 978-1-64368-200-6

VL - 53

T3 - Studies on the Semantic Web

SP - 103

EP - 117

BT - Further with Knowledge Graphs - Proceedings of the 17th International Conference on Semantic Systems, SEMANTiCS 2017, Amsterdam, The Netherlands, September 6-9, 2021

A2 - Alam, Mehwish

A2 - Groth, Paul

A2 - Boer, Victor de

A2 - Pellegrini, Tassilo

A2 - Pandit, Harshvardhan J.

A2 - Montiel-Ponsoda, Elena

A2 - Rodríguez-Doncel, Víctor

A2 - McGillivray, Barbara

A2 - Meroño-Peñuela, Albert

PB - IOS Press BV

CY - Netherlands

ER -