Automatic Knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Wu, Xindong; Wu, Jia; Fu, Xiaoyi; Li, Jiachen; Zhou, Peng; Jiang, Xu

doi:10.1109/icdm.2019.00204

Cited by 29 publications

(6 citation statements)

References 21 publications

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“…Because the building of knowledge graphs begins with a systematic description of concepts, entities, and their relationships in the objective world the correctness of information extraction of concepts, entities, and relationships is critical to the construction process.Information loss, redundancy, and overlap are often the most significant challenges to the construction of knowledge graphs. Information extraction, as the first step in knowledge graph construction, is critical to obtaining candidate knowledge units [1][2]. The completeness and accuracy of information extraction directly and explicitly affect the quality and efficiency of the subsequent knowledge graph construction steps and the quality of the final knowledge graph.…”

Section: Introductionmentioning

confidence: 99%

Research on information extraction technology applied for knowledge graphs

Zhou

2023

ACE

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Information extraction is an important part of natural language processing and is an important basis for building question and answer systems and knowledge graphs. A growing number of new technologies are being applied to information extraction with the development of deep learning techniques. As a first step, this paper introduces information extraction techniques and their main tasks, then describes the development history of information extraction techniques, and introduces the practice and application of different types of information extraction techniques in knowledge graph construction, including entity-extraction, relationship extraction and attribute extraction. Finally, some problems and research directions faced by information extraction techniques are discussed.

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Section: Introductionmentioning

confidence: 99%

Research on information extraction technology applied for knowledge graphs

Zhou

2023

ACE

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“…Thenceforth, the advertisement of using Knowledge Graphs in various large multinationals such as Facebook, Microsoft, LinkedIn, Amazon, etc, have popularized the term [16][17][18][19]. In 2019, IEEE released its International Conference on Knowledge Graph, which was mainly based on "Intelligent Computing and Data Mining" and "Big Knowledge" [20][21][22]. Figure 1 chronologically represents the history of Knowledge Graphs.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review on Knowledge Graphs Classiﬁcation and Their Various Usages

Mst. Mim Akter,

Rahoman

2023

Artificial Intelligence Evolution

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A Knowledge Graph is a directive graph where the nodes state the entities and the edges describe the relationships between the entities of data. It is also referred to as a Semantic Network. The massive amount of data generated every day can be transformed into knowledge via knowledge graphs for the effective use of these data. Knowing the classification of Knowledge Graphs is required to adapt to different requirements of Knowledge Graphs. Knowledge Graphs are primarily classified concerning their building techniques and their usages. In building techniques, it is considered how the Knowledge Graph is built. For example, the graph can be constructed as a triplet, quadruplet, etc., or created from structured data, e.g., database, or unstructured data, e.g., text, image, etc. On the other hand, Knowledge Graphs can be used for various purposes. For example, Knowledge Graphs can be used for Information Retrieval, Semantic Query, etc., or different types of data visualization. Nowadays, Knowledge Graph is one of the trending topics in the modern technology-dependent world. However, clear and specific discussions on the classifications of Knowledge Graphs and their various usages are less available. In this paper, we will describe the classification of knowledge graphs and their various usages in detail so that the readers can get a clear concept of this topic.

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“…Event extraction (EE) task aims to detect the event from texts and then extracts corresponding arguments as different roles, so as to provide a structural information for massive downstream applications, such as recommendation (Gao et al, 2016;Liu et al, 2017), knowledge graph construction (Wu et al, 2019;Bosselut et al, 2021) and intelligent question answering (Boyd-Graber and Börschinger, 2020;.…”

Section: Introductionmentioning

confidence: 99%

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

Liu¹,

Jiang²,

Yin³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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In document-level event extraction (DEE) task, event arguments always scatter across sentences (across-sentence issue) and multiple events may lie in one document (multi-event issue). In this paper, we argue that the relation information of event arguments is of great significance for addressing the above two issues, and propose a new DEE framework which can model the relation dependencies, called Relation-augmented Document-level Event Extraction (ReDEE). More specifically, this framework features a novel and tailored transformer, named as Relation-augmented Attention Transformer (RAAT). RAAT is scalable to capture multi-scale and multi-amount argument relations. To further leverage relation information, we introduce a separate event relation prediction task and adopt multi-task learning method to explicitly enhance event extraction performance. Extensive experiments demonstrate the effectiveness of the proposed method, which can achieve state-ofthe-art performance on two public datasets. Our code is available at https://github. com/TencentYoutuResearch/RAAT.

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Automatic Knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Cited by 29 publications

References 21 publications

Research on information extraction technology applied for knowledge graphs

Research on information extraction technology applied for knowledge graphs

A Systematic Review on Knowledge Graphs Classiﬁcation and Their Various Usages

RAAT: Relation-Augmented Attention Transformer for Relation Modeling in Document-Level Event Extraction

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