Towards A Question Answering System over Temporal Knowledge Graph Embeddings

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

*Kontaktforfatter

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer 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.

OriginalsprogEngelsk
TidsskriftCEUR Workshop Proceedings
Vol/bind3342
ISSN1613-0073
StatusUdgivet - 2022
Begivenhed2022 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2022 - Virtual, Online
Varighed: 24 okt. 2022 → …

Konference

Konference2022 Workshop on Deep Learning for Knowledge Graphs, DL4KG 2022
ByVirtual, Online
Periode24/10/2022 → …

Bibliografisk 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).

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