A semantic frame-based intelligent agent for topic detection

Chang, Yung Chun; Hsieh, Yu Lun; Chen, Cen Chieh; Hsu, Wen Lian

doi:10.1007/s00500-015-1695-4

Cited by 15 publications

(7 citation statements)

References 20 publications

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“…Afterwards, a partial matching algorithm is employed in harnessing the advantages of both rule- and machine learning-based approaches while surpassing their limitations. The method is proved to outperform in several distinct tasks such as topic detection and sentimental analysis ( 37 – 40 ).…”

Section: Enhanced Text Mining Systemmentioning

confidence: 99%

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

Chou

Shrestha

Yang

et al. 2017

Nucleic Acids Research

Self Cite

1,593

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MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

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Section: Enhanced Text Mining Systemmentioning

confidence: 99%

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

Chou

Shrestha

Yang

et al. 2017

Nucleic Acids Research

Self Cite

1,593

1,320

View full text Add to dashboard Cite

show abstract

“…Any failure in one unit will cause break down the whole system. Comparing with the conventional approach, multi-agent system has the advantages of providing efficient and robust responses to dynamic customer demands and providing explanatory insight on the collective behavior of agents (Barenji et al , 2017; Chang et al , 2017). In the multi-agent layer, it consists of two kinds of intelligent agents, namely, customer agents and scheduling agent.…”

Section: Proposed Methodologymentioning

confidence: 99%

Modeling of individual customer delivery satisfaction: an AutoML and multi-agent system approach

Wang

Barenji

et al. 2019

IMDS

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Purpose The purpose of this paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited resources. Design/methodology/approach An AutoML method is purposed to model delivery satisfaction of individual customer, and a heuristic method and multi-agent system are proposed to improve overall satisfaction under limited processing capability. A series of simulation experiments have been conducted to illustrate the effectiveness of the proposed methodology. Findings The simulated results show that the proposed method can effectively improve overall delivery satisfaction, especially when the demand of customer orders is highly fluctuating and when the customer satisfaction models are highly diversified. Practical implications The proposed framework provides a more dynamic and continuously improving way to model delivery satisfaction of individual customer, thereby supports companies to provide personalized services and develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency and effectiveness of delivery services. Originality/value The proposed methodology utilizes AutoML and multi-agent system to model individual customer delivery satisfaction and improve the overall satisfaction. It can cooperate with the existing delivery resource planning methods to further improve customer delivery satisfaction. The authors propose an AutoML approach to model individual customer delivery satisfaction, which enables continuous update and improvements. The authors propose multi-agent system and a heuristic method to improve overall delivery satisfaction. The numerical results show that the proposed method can improve overall delivery satisfaction with limited processing capability.

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“…Hernandez [30] employs semantic techniques for semantic topic detection focusing on the Spanish language. Other examples of semantic topic detection and information retrieval are proposed in [16,28,64]. Another method used for the construction of a semantic network based on the co-occurrences of tags with the aim of comparing the structure of the folksonomies network is proposed by [15].…”

Section: Related Workmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.

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A semantic frame-based intelligent agent for topic detection

Cited by 15 publications

References 20 publications

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

Modeling of individual customer delivery satisfaction: an AutoML and multi-agent system approach

Using a multimedia semantic graph for web document visualization and summarization

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