Ontology-Based Workflow Semantic Representation and Modeling Method

2010 ◽  
Vol 129-131 ◽  
pp. 50-54
Author(s):  
Wei Ping Shao ◽  
Chun Yan Wang ◽  
Yong Ping Hao ◽  
Peng Fei Zeng ◽  
Xiao Lei Xu
Keyword(s):  
Semantic Information ◽  
Semantic Representation ◽  
Modeling Method ◽  
Workflow Model ◽  
Structure Information ◽  
Ontology Modeling ◽  
Ontology Model ◽  
Representation Method

An ontology-based workflow (workflow-ontology) representation method was proposed after analyzing that not only structure information but also semantic information were needed in a workflow model. Workflow-ontology concepts were composed by class and subclass of the workflow. Concepts’ properties including their values and characteristics were redefined, and then, workflow-ontology modeling method was put forward based on the ontology expresses and definitions above. With the example of applying in products examined and approved workflows, the corresponding workflow-ontology model (WFO) was built.

Ontology-Based CRM for Bank Customers

2013 ◽  
Vol 380-384 ◽  
pp. 1402-1408
Author(s):  
Yu Xin Yang ◽  
Da Quan Tang
Keyword(s):  
Commercial Bank ◽  
Commercial Banks ◽  
Specific Model ◽  
Modeling Method ◽  
Commercial Use ◽  
Ontology Modeling ◽  
Ontology Model ◽  
Mapping Mechanism

This paper introduces an ontology-based CRM for the commercial use, which specifically is for commercial bank customers. By using the ontology modeling method, which includes competency questions, intuitions, axioms and specific model, this ontology can share the information between the commercial banks and their customers. Ontology for the commercial banks domain is being developed as relationships and facts of the architecture. The steps of developing such an ontology model and a mapping mechanism between the commercial bank's customers and ability of consuming are discussed.

A Novel Semantic Ontology Modeling Method

2014 ◽  
Vol 510 ◽  
pp. 271-277
Author(s):  
Min Hui Zhang ◽  
Jian Yang
Keyword(s):  
Semantic Representation ◽  
Modeling Method ◽  
Experimental Result ◽  
Service Ontology ◽  
System A ◽  
Ontology Modeling ◽  
Data Integration System ◽  
Core Ontology ◽  
Semantic Ontology ◽  
The Relationship

To solve the semantic representation in data integration system, a novel ontology modeling method was proposed. The relationship between deferent layers including the core ontology layer, the role ontology layer, the goal ontology layer, service ontology layer and the mediator ontology layer in the ontology framework was analyzed, and the implement specification of each layer was given based on UML specification. Experimental result shows that proposed method can improve the degree of accuracy of semantic representation, and has the advantage of good adaptability for structure instability and difference in user needs in the grid computing.

scholarly journals Network Embedding via a Bi-Mode and Deep Neural Network Model

2017 ◽  
Author(s):  
Yang Fang ◽  
Xiang Zhao ◽  
Zhen Tan
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Semantic Information ◽  
Dimensional Space ◽  
Relation Extraction ◽  
Network Embedding ◽  
Structure Information ◽  
Second Mode ◽  
Real World Datasets ◽  
Low Dimensional

Network Embedding (NE) is an important method to learn the representations of network via a low-dimensional space. Conventional NE models focus on capturing the structure information and semantic information of vertices while neglecting such information for edges. In this work, we propose a novel NE model named BimoNet to capture both the structure and semantic information of edges. BimoNet is composed of two parts, i.e., the bi-mode embedding part and the deep neural network part. For bi-mode embedding part, the first mode named add-mode is used to express the entity-shared features of edges and the second mode named subtract-mode is employed to represent the entity-specific features of edges. These features actually reflect the semantic information. For deep neural network part, we firstly regard the edges in a network as nodes, and the vertices as links, which will not change the overall structure of the whole network. Then we take the nodes' adjacent matrix as the input of the deep neural network as it can obtain similar representations for nodes with similar structure. Afterwards, by jointly optimizing the objective function of these two parts, BimoNet could preserve both the semantic and structure information of edges. In experiments, we evaluate BimoNet on three real-world datasets and task of relation extraction, and BimoNet is demonstrated to outperform state-of-the-art baseline models consistently and significantly.

The Study and Application of Workflow Management System Based on Multi-Role Playing

2013 ◽  
Vol 748 ◽  
pp. 1217-1222
Author(s):  
Zi Wei Zeng ◽  
Qiu Si Zhang
Keyword(s):  
Business Process ◽  
Management System ◽  
Process Management ◽  
Workflow Management ◽  
Role Playing ◽  
Workflow Management System ◽  
Modeling Method ◽  
Workflow Model ◽  
Workflow Modeling ◽  
Role Based

Workflow management system is playing more and more significant role in business process management (BPM) and office automation (OA) of enterprises[. It can improve the efficiency and manageability of an enterprises daily teamwork, control as well as coordinate the processes[. Traditionally, it is not easy for the workflow modeling method to describe the complex business process clearly and intuitively. In this paper we improved the role based workflow model, and proposed a workflow modeling method based on multi-role playing. It is possible to make the modeling easier and simpler when we come to the complex business process modeling problems. Finally, an example of a B/S based Enterprise Financial Reimbursement Management System (EFRMS) is demonstrated to prove the convenience and feasibility about the method mentioned.

Common Semantic Representation Method Based on Object Attention and Adversarial Learning for Cross-Modal Data in IoV

2019 ◽  
Vol 68 (12) ◽  
pp. 11588-11598 ◽  
Author(s):  
Feifei Kou ◽  
Junping Du ◽  
Wanqiu Cui ◽  
Lei Shi ◽  
Pengchao Cheng ◽  
...  
Keyword(s):  
Semantic Representation ◽  
Adversarial Learning ◽  
Modal Data ◽  
Object Attention ◽  
Representation Method

scholarly journals Phonetics and Ambiguity Comprehension Gated Attention Network for Humor Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaochao Fan ◽  
Hongfei Lin ◽  
Liang Yang ◽  
Yufeng Diao ◽  
Chen Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Semantic Information ◽  
Semantic Representation ◽  
Research Attention ◽  
Attention Network ◽  
Phonetic Information ◽  
The Neural Network ◽  
Ambiguous Words ◽  
Public Datasets

Humor refers to the quality of being amusing. With the development of artificial intelligence, humor recognition is attracting a lot of research attention. Although phonetics and ambiguity have been introduced by previous studies, existing recognition methods still lack suitable feature design for neural networks. In this paper, we illustrate that phonetics structure and ambiguity associated with confusing words need to be learned for their own representations via the neural network. Then, we propose the Phonetics and Ambiguity Comprehension Gated Attention network (PACGA) to learn phonetic structures and semantic representation for humor recognition. The PACGA model can well represent phonetic information and semantic information with ambiguous words, which is of great benefit to humor recognition. Experimental results on two public datasets demonstrate the effectiveness of our model.

scholarly journals SLAM: A Malware Detection Method Based on Sliding Local Attention Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jun Chen ◽  
Shize Guo ◽  
Xin Ma ◽  
Haiying Li ◽  
Jinhong Guo ◽  
...  
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
Attention Mechanism ◽  
Malicious Code ◽  
Feature Extraction Method ◽  
Structure Information ◽  
Execution Sequence ◽  
Window Method ◽  
Great Progress ◽  
Sliding Window Method

Since the number of malware is increasing rapidly, it continuously poses a risk to the field of network security. Attention mechanism has made great progress in the field of natural language processing. At the same time, there are many research studies based on malicious code API, which is also like semantic information. It is a worthy study to apply attention mechanism to API semantics. In this paper, we firstly study the characters of the API execution sequence and classify them into 17 categories. Secondly, we propose a novel feature extraction method based on API execution sequence according to its semantics and structure information. Thirdly, based on the API data characteristics and attention mechanism features, we construct a detection framework SLAM based on local attention mechanism and sliding window method. Experiments show that our model achieves a better performance, which is a higher accuracy of 0.9723.

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