Knowledge Base Question Answering With a Matching-Aggregation Model and Question-Specific Contextual Relations

Lan, Yunshi; Wang, Shuohang; Jiang, Jing

doi:10.1109/taslp.2019.2926125

Cited by 49 publications

(21 citation statements)

References 31 publications

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“…Until now, there have been several KGs and research focusing on intelligent question answering engines [136][137][138][139]. However, there are few explorations on KG applied to intelligent customer service towards SGs.…”

Section: Intelligent Customer Service Robot Systemmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

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With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

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Section: Intelligent Customer Service Robot Systemmentioning

confidence: 99%

A survey on the development status and application prospects of knowledge graph in smart grids

Jian

Wang

et al. 2020

IET Generation Trans & Dist

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“…Knowledge base question answering (KBQA) is a task to gure out the entities as answers for an input question from a given knowledge base (KB) and has attracted many researchers to work on it [1][2][3][4][5][6][7][8][9][10]. It is a challenging academic task, especially when answering multi-hop questions.…”

Section: Introductionmentioning

confidence: 99%

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base

Zhang

Jin

et al. 2022

Computational Intelligence and Neuroscience

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Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way humans solve multi-hop questions, which do not consider the latent relation information contained in connected edges, and lack of measuring the correlation between specific relations and the input question. To address these challenges, we propose an edge-aware graph neural network for multi-hop path reasoning task. First, a query node is directly added to the candidate subgraph retrieved from the knowledge base, which constructs what we term a query graph. This graph construction strategy makes it possible to enhance the information flow between the question and the nodes for the subsequent message passing steps. Second, question-related information contained in the relations is added to the entity node representations during graph updating; meanwhile, the relation representations are updated. Finally, the attention mechanism is used to weight the contribution from neighbor nodes so that only the information of neighbor nodes related to the query can be injected into new node representations. Experimental results on MetaQA and PathQuestion-Large (PQL) benchmarks demonstrate that the proposed model achieves higher Hit@1 and F1 scores than the baseline methods by a large margin. Moreover, ablation studies show that both the graph construction and the graph update algorithm contribute to performance improvement.

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“…Named entity recognition (NER) is a core NLP technology employed to identify entities such as persons, locations, organizations, and dates in texts [5]. NER is the basis of entity-relationship extraction [6], knowledge graphs [7], and automatic question answering systems [8], which have been widely applied in many other fields [9][10]. Therefore, NER is a potentially practical approach for mining critical information from electrical facility malfunction texts.…”

Section: Introductionmentioning

confidence: 99%

Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

Kong

Yue²,

Shi

et al. 2021

IEEE Access

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Many electrical equipment malfunction text messages are collected during power system operation and maintenance procedures. These texts usually contain crucial information for maintenance and condition monitoring. Because these power system malfunction texts are characterized by multidomain vocabularies, complex-syntactic structures, and long sentences, it is challenging to for automated systems to capture their semantic meaning and essential information. To address this issue, we propose a hybrid natural language processing (hybrid-NLP) algorithm to extract entities that represent electrical equipment. This algorithm is composed of a dictionary-based method, a language technology platform (LTP) tool, and the bidirectional encoder representations from a transformers-conditional random field (BERT-CRF) model. Significantly, the softmax output layer of the bidirectional encoder representations from the transformers (BERT) model is replaced by the conditional random field (CRF) algorithm to strengthen the contextual relationships between words and thus solve the local optimization of the preferred word label. The effectiveness of the proposed hybrid-NLP method is verified on a realistic dataset. Moreover, a statistical analysis is conducted to provide a reference for the operation and maintenance of power systems. INDEX TERMS electrical equipment malfunction text, natural language processing, entity extraction, BERT-CRF model.

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Knowledge Base Question Answering With a Matching-Aggregation Model and Question-Specific Contextual Relations

Cited by 49 publications

References 31 publications

A survey on the development status and application prospects of knowledge graph in smart grids

A survey on the development status and application prospects of knowledge graph in smart grids

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base

Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

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