Building and Exploring an Enterprise Knowledge Graph for Investment Analysis

Ruan, Tong; Xue, Lijuan; Wang, Haofen; Hu, Fanghuai; Zhao, Liang; Jian, Ding

doi:10.1007/978-3-319-46547-0_35

Cited by 21 publications

(8 citation statements)

References 8 publications

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“…The reliance on human expertise constitutes a major impediment to the process. This is due to the sheer amount of data (Li et al 2020;Tamašauskaitė and Groth 2023), which makes manual knowledge graph creation labour-intensive (Ruan et al 2016), expensive (Ji et al 2022) and prone to human errors (Kejriwal 2019). At the same time, there is a shortage of relevant experts in many domains (Abu-Salih 2021).…”

Section: Related Work and Research Objectivementioning

confidence: 99%

An LLM-Aided Enterprise Knowledge Graph (EKG) Engineering Process

Laurenzi,

Mathys,

Martin

2024

AAAI-SS

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Conventional knowledge engineering approaches aiming to create Enterprise Knowledge Graphs (EKG) still require a high level of manual effort and high ontology expertise, which hinder their adoption across industries. To tackle this issue, we explored the use of Large Language Models (LLMs) for the creation of EKGs through the lens of a design-science approach. Findings from the literature and from expert interviews led to the creation of the proposed artefact, which takes the form of a six-step process for EKG development. Scenarios on how to use LLMs are proposed and implemented for each of the six steps. The process is then evaluated with an anonymised data set from a large Swiss company. Results demonstrate that LLMs can support the creation of EKGs, offering themselves as a new aid for knowledge engineers.

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Section: Related Work and Research Objectivementioning

confidence: 99%

An LLM-Aided Enterprise Knowledge Graph (EKG) Engineering Process

Laurenzi,

Mathys,

Martin

2024

AAAI-SS

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“…Among them, the top-down method refers to the pre-construction of the pattern diagram of the vertical knowledge graph through the ontology editor or manual construction method, and then the construction of the data graph. The bottom-up approach refers to the use of various extraction techniques to obtain entities, attributes, and relationships in the knowledge source when constructing the data graph, and to merge these highly confident extraction results into the knowledge graph 17 . In this way, the knowledge structure is designed and planned from the upper level, and the knowledge base has certain structural and semantic consistency through the constraints and classification of ontology.…”

Section: Construction Process Of Knowledge Graph Of Shui and Bouyei E...mentioning

confidence: 99%

Research on the knowledge organization and visualization of ethnic minority cultural information resources

Zhong,

Xia,

et al. 2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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Under the background of the "14th Five-Year Plan" to promote the digital level of public culture, this study discusses the knowledge organization and visualization of minority culture. The aim is to enrich the knowledge base of public culture and improve the digital level of public culture by constructing the knowledge graph of minority festivals. In this paper, the festival culture of the Shui nationality and Bouyei nationality is selected as the research object. Based on ontology theory, the relevant data is structured and processed, and then the data is visualized by the knowledge graph tool. This study successfully constructed a knowledge graph of Shui nationality and Bouyei nationality festival culture, which contains 353 entities and 951 relationships. The results show that the method proposed in this study can be effectively applied to the construction of a knowledge graph, and reveal tacit knowledge from it, providing support for the digitization of public culture.

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“…The domain-specific knowledge graph emphasizes the depth of knowledge, and the knowledge contained is highly targeted and professional. Representative specific fields include medical [20], economics [21], education [22], etc.…”

Section: A Knowledge Graph Constructionmentioning

confidence: 99%

Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis

Deng

Wang

et al. 2022

IEEE Access

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Knowledge graph technology has important guiding significance for efficient and orderly fault diagnosis of robot transmission system. Taking the historical robot maintenance logs of robot transmission system as the research object, a top-down fault diagnosis event logic knowledge graph construction method is proposed. Firstly, we define event arguments of fault phenomenon and fault cause events, define event argument classes and relation between classes, and construct an event logic knowledge ontology model. According to the event logic knowledge ontology, the fault diagnosis event argument entity and relation in the corpus are labeled, and an event logic knowledge extraction dataset is formed. Secondly, an event argument entity and relation joint extraction model is proposed. Using stacked bidirectional long shortterm memory(BiLSTM) to obtain deep context features of text. As a supplement to stacked BiLSTM, selfattention mechanism extracts character dependency features from multiple subspaces, and uses conditional random field(CRF) to realize entity recognition. The character dependency features are mapped to the entity label weight embedding, and spliced with deep context features to extract relations. Bidirectional graph convolutional network(BiGCN) is introduced for relation inference, graph convolution features are used to update deep context features to perform joint extraction in the second phase. Experimental results show that this method can improve the effect of event argument entity and relation joint extraction and is better than other methods. Finally, an event logic knowledge graph of robot transmission system fault diagnosis is constructed, which provides decision support for autonomous fault diagnosis of robot transmission system.

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Building and Exploring an Enterprise Knowledge Graph for Investment Analysis

Cited by 21 publications

References 8 publications

An LLM-Aided Enterprise Knowledge Graph (EKG) Engineering Process

An LLM-Aided Enterprise Knowledge Graph (EKG) Engineering Process

Research on the knowledge organization and visualization of ethnic minority cultural information resources

Research on Event Logic Knowledge Graph Construction Method of Robot Transmission System Fault Diagnosis

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