Industry Chain Graph Building Based on Text Semantic Association Mining

2021 ◽  
Author(s):  
Jipeng Li ◽  
Yujing Sun ◽  
Chenhui Li ◽  
Yanpeng Hu ◽  
Changbo Wang
Keyword(s):  
Association Mining ◽  
Chain Graph ◽  
Semantic Association ◽  
Industry Chain

Approaches for Semantic Association Mining and Hidden Entities Extraction in Knowledge Base

2010 ◽  
pp. 119-141 ◽  
Author(s):  
Thabet Slimani ◽  
Boutheina Ben Yaghlane ◽  
Khaled Mellouli
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
Information And Communications Technology ◽  
Association Mining ◽  
Semantic Association ◽  
Real World Data ◽  
Semantic Associations ◽  
Hyperclique Pattern ◽  
Flow Of Information

Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness.

scholarly journals The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

2021 ◽  
Author(s):  
Yan Guo ◽  
Xiaonan Hu ◽  
Zepeng Wang ◽  
Wei Tang ◽  
Deyu Liu ◽  
...  
Keyword(s):  
Price Change ◽  
Agricultural Products ◽  
Agricultural Product ◽  
Association Mining ◽  
Price Forecasting ◽  
Time And Space ◽  
Product Price ◽  
Industry Chain ◽  
Price Prediction ◽  
Butterfly Effect

With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

scholarly journals Adaptive Study Design Through Semantic Association Rule Analysis

2011 ◽  
Vol 3 (2) ◽  
pp. 34-48 ◽  
Author(s):  
Ping Chen ◽  
Wei Ding ◽  
Walter Garcia
Keyword(s):  
Study Design ◽  
Association Rule ◽  
Semantic Network ◽  
Association Mining ◽  
Medical Studies ◽  
Analysis Model ◽  
Semantic Association ◽  
Rule Analysis ◽  
Adaptive Study Design

Association mining aims to find valid correlations among data attributes, and has been widely applied to many areas of data analysis. This paper presents a semantic network-based association analysis model including three spreading activation methods. It applies this model to assess the quality of a dataset, and generate semantically valid new hypotheses for adaptive study design especially useful in medical studies. The approach is evaluated on a real public health dataset, the Heartfelt study, and the experiment shows promising results.

Improved Diagnosis-Medication Association Mining to Reduce Pseudo-associations

2021 ◽  
pp. 106181
Author(s):  
Ching-Huan Wang ◽  
Phung Anh Nguyen ◽  
Yu Chuan (Jack) Li ◽  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
...  
Keyword(s):  
Association Mining
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