A Tutorial and Survey on Fault Knowledge Graph

Wang, XiuQing; Yang, Shunkun

doi:10.1007/978-981-15-1925-3_19

Cited by 4 publications

(2 citation statements)

References 57 publications

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“…KG aims to describe the concepts and entities, as well as their relationships, in the real world [17]. As an application of the semantic web, it is divided into three components: concepts, entities, and their relationship triples.…”

Section: Knowledge Graphmentioning

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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Section: Knowledge Graphmentioning

confidence: 99%

Advancements in Complex Knowledge Graph Question Answering: A Survey

Song,

Li,

Dai

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…The book of [Kejriwal, 2019] demonstrates a very general summary on domain-specific KG construction. One survey about fault domain knowledge graphs has been written by [Wang and Yang, 2019]. Another important scrutiny has been conducted by [Al-Moslmi et al, 2020] outlining preprocessing tools a la natural language processing, such as, named entity recognition, named entity disambiguation, and named entity linking, to enable the construction of a KG.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

Buchgeher¹,

Gabauer²,

Martinez‐Gil³

et al. 2020

Preprint

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Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals.However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying exiting research and by identifying gaps and opportunities for further research. To do that, we have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but seems to be still far from its peak.

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Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying existing research and by identifying gaps and opportunities for further research. We have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but still seems to be far from its peak.

show abstract

A Tutorial and Survey on Fault Knowledge Graph

Cited by 4 publications

References 57 publications

Advancements in Complex Knowledge Graph Question Answering: A Survey

Advancements in Complex Knowledge Graph Question Answering: A Survey

Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review

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