Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion

Han, Jiale; Cheng, Bo; Wang, Xu

doi:10.18653/v1/2020.findings-emnlp.133

Cited by 44 publications

(25 citation statements)

References 18 publications

(24 reference statements)

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“…Meanwhile, finer 368 PLMs can improve the semantic feature representations of question subgraphs and 369 achieve better results in question answering. This is also consistent with the view and 370 experimental results in[33].382dimensional hyper-graph with hierarchical structure (Figure6). The metaknowledge 383 network expressed by that type of graph model includes two dimensions: hierarchical 384 dimension and semantic dimension.…”

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confidence: 89%

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Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

Liu¹,

Xu²,

Duan³

et al. 2021

Preprint

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The commonly-used large-scale knowledge bases have been facing challenges in open domain question answering tasks which are caused by the loose knowledge association and weak structural logic of triplet-based knowledge. To find a way out of this dilemma, this work proposes a novel metaknowledge enhanced approach for open domain question answering. We design an automatic approach to extract metaknowledge and build metaknowledge network from Wiki documents. For the purpose of representing the directional weighted graph with hierarchical and semantic features, we present an original graph encoder GE4MK to model the metaknowledge network. Then a metaknowledge enhanced graph reasoning model MEGr-Net is proposed for question answering, which aggregates both relational and neighboring interactions comparing with R-GCN and GAT. Experiments have proved the improvement of metaknowledge over main-stream triplet-based knowledge. We have found that the graph reasoning models and pre-trained language models also have influences on the metaknowledge enhanced question answering approaches.

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supporting

confidence: 89%

“…Ref [34]. proposes an embedding framework MINES for multi-388 dimensional networks with hierarchical structure, which uses hierarchical structure for 389 multi-dimensional network embedding; Ref [33]. proposes an open domain question 390 answering method based on hyper-edge fusion.…”

mentioning

confidence: 99%

Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

Liu¹,

Xu²,

Duan³

et al. 2021

Preprint

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“…Sun et al [2018] and proposed to complement the subgraph extracted from incomplete KBs with extra question-related text sentences to form a heterogeneous graph and conduct reasoning on it. Instead of directly complementing sentences to question-specific graph as nodes, Xiong et al [2019] and Han et al [2020a] proposed to fuse extra textual information into the entity representation to supplement knowledge. They first encoded sentences and entities conditioned on questions, and then supplemented the incomplete KB by aggregating representations of sentences to enhance corresponding entity representations.…”

Section: Information Retrieval-based Methodsmentioning

confidence: 99%

“…Supplement KB with extra corpus [Sun et al, 2018;, fuse extra textual information into entity representations [Xiong et al, 2019;Han et al, 2020a] or leverage KB embeddings [Saxena et al, 2020].…”

Section: Reasoning Under Incomplete Kbmentioning

confidence: 99%

A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions

Lan

Jiang

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicated questions. In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA. We begin with introducing the background about the KBQA task. Next, we present the two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. We then review the advanced methods comprehensively from the perspective of the two categories. Specifically, we explicate their solutions to the typical challenges. Finally, we conclude and discuss some promising directions for future research.

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“…In recent years, many researchers have combined continuous text knowledge and knowledge graphs as a data source for KBQA. For example, Han et al [10] proposed a question-and-answer model based on text-enhanced knowledge graphs, which encoded entities in the KB through text information and applied graph convolutional networks [11] to perform reasoning on KB. Das et al [12] proposed a general model that used a combination of text and KB, combining with a memory network to complete question and answer.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Question Answering System of the Liao Dynasty Based on Knowledge Graph

Liu

Tan

Yang

et al. 2021

Int J Comput Intell Syst

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The Liao Dynasty was a minority regime established by the Khitan on the grasslands of northern China. To promote and spread the cultural knowledge of the Liao Dynasty, an intelligent question-and-answer system is constructed based on the knowledge graph in the historical and cultural field of the Liao Dynasty. In the traditional question answering system, the quality of answers was not high due to incomplete data and distinctive vocabulary. To solve this problem, a combination method of Liao Dynasty question-and-answer database and KB is proposed to realize knowledge graph question answering, and a joint model of Siamese LSTM and fusion MatchPyramid is proposed for semantic matching between questions in the question-and-answer database. With the joint model, it is easy to perform semantic matching by fusing sentence-level and word-level interactive features through LSTM and MatchPyramid. Furthermore, the question sentence with the same semantics as the user input question sentence is retrieved in the question-and-answer database, and the answer corresponding to the question sentence is returned as the result. The experimental results show that our proposed method has achieved relatively good performance in the historical domain of the Liao Dynasty and the open-domain knowledge graph, and improved the accuracy of question and answer.

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Open Domain Question Answering based on Text Enhanced Knowledge Graph with Hyperedge Infusion

Cited by 44 publications

References 18 publications

Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

Metaknowledge Enhanced Open Domain Question Answering with Wiki Documents

A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions

An Intelligent Question Answering System of the Liao Dynasty Based on Knowledge Graph

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