An improving reasoning network for complex question answering over temporal knowledge graphs

Jiao, Songlin; Zhu, Zhenfang; Wu, Wenqing; Zuo, Zicheng; Qi, Jiangtao; Wang, Wenling; Zhang, Guangyuan; Liu, Peiyu

doi:10.1007/s10489-022-03913-6

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…We show that our approach outperforms the datasets MetaQA, WebQSP, and CompWebQSP, especially for complex datasets such as WebQSP and CompWebQ. In the future, we plan to investigate the following main issues: how to support dynamic application scenarios in open domains and the rapid updating of knowledge [30] and how to introduce external knowledge, such as knowledge from web pages, to expand the knowledge coverage of the system [31].…”

Section: Discussionmentioning

confidence: 99%

Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

et al. 2023

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Multi-hop knowledge graph question answer (KGQA) is a challenging task because it requires reasoning over multiple edges of the knowledge graph (KG) to arrive at the right answer. However, KGs are often incomplete with many missing links, posing additional challenges for multi-hop KGQA. Recent research on multi-hop KGQA attempted to deal with KG sparsity with relevant external texts. In our work, we propose a multi-hop KGQA model based on relation knowledge enhancement (RKE-KGQA), which fuses both label and text relations through global attention for relation knowledge augmentation. It is well known that the relation between entities can be represented by labels in the knowledge graph or texts in the text corpus, and multi-hop KGQA needs to jump across different entities through relations. First, we assign an activation probability to each entity, then calculate a score for the enhancement relation, and then transfer the score through the activated relations and, finally, obtain the answer. We carry out extensive experiments on three datasets and demonstrate that RKE-KGQA achieves the outperformance result.

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Section: Discussionmentioning

confidence: 99%

Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

et al. 2023

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“…They used a time neighborhood aggregation framework to model interactions between entities. Jiao et al [22] presented an enhanced Complex Temporal Reasoning Network that improves complex temporal reasoning for temporal reasoning problems and captures implicit temporal features and relation representations. Nie et al [23] introduced TAL-TKGC to capture the influence of time information on quaternion and node structure information.…”

Section: Related Workmentioning

confidence: 99%

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Ma,

Altenbek,

2024

CMC

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Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event representation and establish effective correlations, an independent recurrent neural network is employed to implement auto-regressive modeling. Furthermore, static attributes of entities in the entity-relation events are constrained and merged using a static graph constraint to obtain optimal entity representations. Finally, the evolution of entity and relation representations is utilized to predict events in the next subsequent step. On multiple real-world datasets such as Freebase13 (FB13), Freebase 15k (FB15K), WordNet11 (WN11), WordNet18 (WN18), FB15K-237, WN18RR, YAGO3-10, and Nell-995, the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks, which validates the effectiveness and robustness.

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“…In addition to the basic framework, researchers are continuously exploring new technologies and methods, such as attention mechanisms, multi-task learning, transfer learning, and semi-supervised learning [53], to improve the efficacy and generalization ability of the models. Furthermore, some language models, such as T5 [54] and GShard [55], have been proposed to integrate multiple tasks and languages during pre-training, which can further enhance the generalization ability and adaptability of models [56].…”

Section: What Was the First Novel Of The 2022mentioning

confidence: 99%

Advancements in Complex Knowledge Graph Question Answering: A Survey

Song,

Li,

Dai

et al. 2023

Electronics

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Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have been proposed to remedy this challenge. In this survey, we proposed two mainstream categories of methods for C-KGQA, which are divided according to their use for knowledge graph representation and construction, namely, graph metric (GM)-Based Methods and graph neural network (GNN)-based methods. Additionally, we also acknowledge the influence of ChatGPT, which has prompted further research into utilizing knowledge graphs as a knowledge source to assist in answering complex questions. We also introduced methods based on pre-trained models and knowledge graph joint reasoning. Furthermore, we have compiled research achievements from the past three years to make it easier for researchers with similar interests to obtain state-of-the-art research. Finally, we discussed the resources and evaluation methods for tackling C-KGQA tasks and summarized several research prospects in this field.

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An improving reasoning network for complex question answering over temporal knowledge graphs

Cited by 13 publications

References 25 publications

Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

IndRT-GCNets: Knowledge Reasoning with Independent Recurrent Temporal Graph Convolutional Representations

Advancements in Complex Knowledge Graph Question Answering: A Survey

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