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Question Answering Over Temporal Knowledge Graphs

Saxena, Apoorv, Chakrabarti, Soumen, Talukdar, Partha·2021·Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
DOI 10.18653/v1/2021.acl-long.520
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Figure 1 The CRONKGQA method. (i) A temporal KG embedding model (Section 4) is used to generate embeddings for each timestamp and entity in the temporal knowledge graph (ii) BERT is used to get two question embeddings: qe ent and qe time . (iii) Embeddings of entity/time mentions in the question are combined with question embeddings using equations 4 and 5 to get score vectors for entity and time prediction. (iv) Score vectors are concatenated and softmax is used get answer probabilities. Please refer to Section 5 for details.
Figure 1 The CRONKGQA method. (i) A temporal KG embedding model (Section 4) is used to generate embeddings for each timestamp and entity in the temporal knowledge graph (ii) BERT is used to get two question embeddings: qe ent and qe time . (iii) Embeddings of entity/time mentions in the question are combined with question embeddings using equations 4 and 5 to get score vectors for entity and time prediction. (iv) Score vectors are concatenated and softmax is used get answer probabilities. Please refer to Section 5 for details.CC-BY · From the PDF
Figure 2 Model performance (hits@10) vs. training dataset size (percentage) for CRONKGQA and T-EaEadd. Solid line is for simple reasoning and dashed line is for complex reasoning type questions. For each dataset size, models were trained until validation hits@10 did not increase for 10 epochs. Please refer to Section 6.4 for details.
Figure 2 Model performance (hits@10) vs. training dataset size (percentage) for CRONKGQA and T-EaEadd. Solid line is for simple reasoning and dashed line is for complex reasoning type questions. For each dataset size, models were trained until validation hits@10 did not increase for 10 epochs. Please refer to Section 6.4 for details.CC-BY · From the PDF

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Research works printed but not linked: 21

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Metadata

Authors
Saxena, A., Chakrabarti, S., & Talukdar, P.
Venue
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2021
Pages
6663-6676
DOI
10.18653/v1/2021.acl-long.520
Cited by
91
References
11

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