Unlabelled text mining methods based on two extension models of concept lattices

Chen, Xiaoyu; Qi, Jianjun; Zhu, Xiaomin; Wang, Xin; Wang, Zhen

doi:10.1007/s13042-019-00987-6

Cited by 17 publications

(4 citation statements)

References 38 publications

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“…ings can learn the underlying semantic representation of the nodes [19]. In the previous section, we generated a set of nodes on the IoT context graph by randomly wandering with bias.…”

Section: Semantic Linked Decision Analysis For the Internet Of Ingsmentioning

confidence: 99%

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Liu¹,

Lu²

2022

Mathematical Problems in Engineering

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This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.

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“…ings can learn the underlying semantic representation of the nodes [19]. In the previous section, we generated a set of nodes on the IoT context graph by randomly wandering with bias.…”

Section: Semantic Linked Decision Analysis For the Internet Of Ingsmentioning

confidence: 99%

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Liu¹,

Lu²

2022

Mathematical Problems in Engineering

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“…Different from previous research works on setting up forecasting models that focused heavily on the adoption of numerical messages, this study provides users with an overarching consideration from different perspectives and further utilizes textual messages [22][23][24][25][26]. Rönnqvist and Sarlin [27] also indicated that the signs of a change in corporate operations very likely will appear in textual format that precedes users who try to find any subtle differences in various numerical ratios.…”

Section: Introductionmentioning

confidence: 97%

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Huang

Lin

Hsu

2021

Axioms

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Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.

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“…As the core data structure of FCA, concept lattice can extract association information from massive data and intuitively represent the hierarchical relationship between concepts [8][9][10][11]. In order to increase users' understanding, researchers have proposed various association rule mining methods based on concept lattice to visualize association rules [12][13][14][15][16][17][18][19][20][21][22]. For instance, Wang et al [20] applied the quantitative concept lattice to obtain frequent itemsets, which method was found quite intuitive and the expression concise.…”

Section: Introductionmentioning

confidence: 99%

Association rule mining with fuzzy linguistic information based on attribute partial ordered structure

Pang

et al. 2023

Preprint

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Numerous linguistically valued facts from the actual world have been modeled using the fuzzy linguistic approaches. Data owners urgently need to mine attribute associations hidden in linguistic concept knowledge. In order to solve the problem of association rule mining with fuzzy linguistic information, this paper proposes an association rule mining approach based on fuzzy linguistic attribute partial ordered structure diagram. Firstly, complex relationships between linguistic values in association rule mining are represented by fuzzy linguistic association nodes and association paths via fuzzy linguistic attribute partial ordered structure diagram (FL-APOSD). On this basis, the maximum frequent attribute set is mined from the FL-APOSD, and then the non-redundancy association rules are extracted. Secondly, in order to show the information hidden in the rules and help users to deeply understand the mining results, a fuzzy linguistic association rule visualization approach is proposed to convert the association rules into the FL-APOSD-based knowledge representation. Finally, experimental results on real-world datasets show the proposed approach's high efficiency, outperforming two relevant state-of-the-art approaches.

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Unlabelled text mining methods based on two extension models of concept lattices

Cited by 17 publications

References 38 publications

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Association rule mining with fuzzy linguistic information based on attribute partial ordered structure

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