Towards A Question Answering System over Temporal Knowledge Graph Embeddings

Kristian Otte*, Kristian Simoni Vestermark, Huan Li, Daniele Dell'Aglio

*Corresponding author for this work

Research output: Contribution to journalConference article in JournalResearchpeer-review

Abstract

Question Answering (QA) over knowledge graphs is a vital topic within information retrieval. Questions with temporal intent are a special case of questions for QA systems that have received only limited attention so far. In this paper, we study using temporal knowledge graph embeddings (TKGEs) for temporal QA. Firstly, we propose a microservice-based architecture for building temporal QA systems on pre-trained TKGE models. Secondly, we present a Bayesian model average (BMA) ensemble method, where results of several link prediction tasks on separated TKGE models are combined to find better answers. Within the system built using the microservice-based architecture, the experiments on two benchmark datasets show that BMA provides better results than the individual models.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume3342
ISSN1613-0073
Publication statusPublished - 2022
Event2022 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2022 - Virtual, Online
Duration: 24 Oct 2022 → …

Conference

Conference2022 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2022
CityVirtual, Online
Period24/10/2022 → …

Bibliographical note

Publisher Copyright:
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Fingerprint

Dive into the research topics of 'Towards A Question Answering System over Temporal Knowledge Graph Embeddings'. Together they form a unique fingerprint.

Cite this