Relation Extraction in Knowledge Base Question Answering: From General-Domain to the Catering Industry

Chen, Hung-Chen; Chen, Ziyuan; Huang, Sin-Yi; Ku, Lun-Wei; Chiu, Yu-Shian; Yang, Wei-Jen

doi:10.1007/978-3-319-91716-0_3

“…At present, the question answering method of knowledge base in Chinese domain is mainly improved based on information retrieval and vector modeling. Lai et al [8] used convolutional neural network to identify semantic features in questions and determined the results through the matching degree of answers and questions; Dai et al [9] proposed a method, which first carries out named entity recognition, then carries out attribute mapping through two-way LSTM [10] based on attention mechanism, and finally selects the answer from the knowledge base based on the results of the first two steps; Chen et al [11] proposed a relationship extraction method integrating artificial rules to improve the accuracy of relationship recognition.…”

Section: Related Workmentioning

confidence: 99%

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

Sun

¹

,

Li

²

,

Sun³

2022

Wireless Communications and Mobile Computing

2

0

1

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In today’s era, the English level of college students is very important. Different English classes can cultivate different English abilities. Smarter classroom is a concept put forward in the new century. This paper proposes the construction of a smarter classroom for college English with artificial intelligence and big data and proposes a deep neural network text semantic matching original model (OM), which uses different lexical information to match different English digital information. Combined with the K-means clustering method, different lexical semantic information is matched. After the comparison of experiments, the combination of the research theory and the algorithm in this paper is effective and has good use value.

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“…Here the candidates can be either entities or relations. Some use embedding-based models to predict answers directly (Dong et al, 2015;Bast and Haussmann, 2015;Hao et al, 2017;Zhou et al, 2018;Lukovnikov et al, 2017), whereas others focus on extracting relation paths and require further procedures to select the answer entity (Bordes et al, 2015;Xu et al, 2016;Yin et al, 2016;Yu et al, 2017;Zhang et al, 2018a;Yu et al, 2018;Chen et al, 2018a;Shen et al, 2018). Our work follows the latter methods in focusing on predicting relation paths, but we seek to eliminate the need to assume in advance a maximum number of hops.…”

Section: Related Workmentioning

confidence: 99%

Untitled

Chen

¹

,

Chang

²

,

Chen

³

et al. 2019

Proceedings of the 2019 Conference of the North

Self Cite

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In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called "one hop". In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one. We conduct experiments on conventional 1-and 2hop questions as well as lengthy questions, including datasets such as WebQSP, PathQuestion, and Grid World. Results show that the proposed framework enables the ability to halt, works well with state-of-the-art models, achieves competitive performance without exhaustive searches, and opens the performance gap for long relation paths.

show abstract

“…Here the candidates can be either entities or relations. Some use embedding-based models to predict answers directly (Dong et al, 2015;Bast and Haussmann, 2015;Hao et al, 2017;Zhou et al, 2018;Lukovnikov et al, 2017), whereas others focus on extracting relation paths and require further procedures to select the answer entity (Bordes et al, 2015;Xu et al, 2016;Yin et al, 2016;Yu et al, 2017;Zhang et al, 2018a;Yu et al, 2018;Chen et al, 2018a;Shen et al, 2018). Our work follows the latter methods in focusing on predicting relation paths, but we seek to eliminate the need to assume in advance a maximum number of hops.…”

Section: Related Workmentioning

confidence: 99%

UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering

Chen¹,

Chang²,

Chen³

et al. 2019

Preprint

Self Cite

6

0

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In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called "one hop". In related work, an exhaustive search from all one-hop relations, two-hop relations, and so on to the max-hop relations in the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one. We conduct experiments on conventional 1-and 2hop questions as well as lengthy questions, including datasets such as WebQSP, PathQuestion, and Grid World. Results show that the proposed framework enables the ability to halt, works well with state-of-the-art models, achieves competitive performance without exhaustive searches, and opens the performance gap for long relation paths.

show abstract

Relation Extraction in Knowledge Base Question Answering: From General-Domain to the Catering Industry

Cited by 8 publications

References 24 publications

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

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UHop: An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based Question Answering

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