MATQA: Microservice-based Architecture for Temporal Question Answering on Knowledge Graph Embeddings
Authors
Vestermark, Kristian Simoni ; Otte, Kristian
Term
4. term
Education
Publication year
2022
Submitted on
2022-06-16
Pages
11
Abstract
Question answering over knowledge graphs (QAKGs) is a vital topic within information retrieval. Questions with temporal intent are a special case of questions for question answering (QA) systems that has not received a large amount of attention in research. In this paper we propose using temporal knowledge graph embeddings (TKGEs) for temporal QA. We propose MATQA, a microservice-based architecture for building temporal QA systems on knowledge graph embeddings (KGEs). Furthermore, we present a variation of ensemble learning, Bayesian model averaging (BMA), where results of several link prediction tasks on separate different pre-trained TKGE models are combined and re-ranked, before being chosen as the final results. Our experiments on two datasets, ICEWS14 and ICEWS05-15, performed using this variation of ensemble, which we build using the microservicebased architecture, show that it provides better results, than using these TKGE models individually.
Question answering over knowledge graphs (QAKGs) is a vital topic within information retrieval. Questions with temporal intent are a special case of questions for question answering (QA) systems that has not received a large amount of attention in research. In this paper we propose using temporal knowledge graph embeddings (TKGEs) for temporal QA. We propose MATQA, a microservice-based architecture for building temporal QA systems on knowledge graph embeddings (KGEs). Furthermore, we present a variation of ensemble learning, Bayesian model averaging (BMA), where results of several link prediction tasks on separate different pre-trained TKGE models are combined and re-ranked, before being chosen as the final results. Our experiments on two datasets, ICEWS14 and ICEWS05-15, performed using this variation of ensemble, which we build using the microservicebased architecture, show that it provides better results, than using these TKGE models individually.
Keywords
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