The Ontology Mapping Technology Based on the Information Coding Ontology Model

2010 ◽  
Vol 139-141 ◽  
pp. 1022-1027
Author(s):  
Jian Jun Jiang ◽  
Jun Biao Wang ◽  
Nan Liu
Keyword(s):  
Semantic Similarity ◽  
Information Integration ◽  
Implementation Process ◽  
Information Coding ◽  
Mapping Algorithm ◽  
Ontology Model ◽  
Similarity Algorithm ◽  
Mapping Technology ◽  
Model Section ◽  
Semantic Similarity Analysis

To our knowledge, there is no paper in the open literature dealing with the topic in the title. This paper provides an Information Coding Ontology Model (ICOM) to shield semantic heterogeneous of information resources, and to achieve information integration. Sections 1 and 2 of the full paper present the information coding ontology model and its formal definition, then design the mapping framework of the information coding ontology model. Section 3 discusses the mapping algorithm of the information coding ontology model from three aspects: semantic similarity analysis, obtaining semantic similarity algorithm, and semantic mapping and its implementation. Section 4 gives Fig. 7 as the mapping flow chart of the information coding ontology model and uses a numerical example to illustrate the four-step procedure of the mapping implementation process.

scholarly journals Event Detection through Lexical Chain Based Semantic Similarity Algorithm

2021 ◽  
Vol 1166 (1) ◽  
pp. 012016
Author(s):  
Swati Jain ◽  
Suraj Prakash Narayan ◽  
Nalini Meena ◽  
Rupesh Kumar Dewang ◽  
Utkarsh Bhartiya ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Event Detection ◽  
Similarity Algorithm ◽  
Lexical Chain

scholarly journals An weighted ontology-based semantic similarity algorithm for web service

2009 ◽  
Vol 36 (10) ◽  
pp. 12480-12490 ◽  
Author(s):  
Min Liu ◽  
Weiming Shen ◽  
Qi Hao ◽  
Junwei Yan
Keyword(s):  
Web Service ◽  
Semantic Similarity ◽  
Similarity Algorithm

scholarly journals Colour Recognition Algorithm Based on Colour Mapping Knowledge for Wooden Building Image

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Siyi Jia ◽  
Heng Chen
Keyword(s):  
Mutual Influence ◽  
Block Diagram ◽  
Recognition Algorithm ◽  
Linear Mapping ◽  
Display Device ◽  
Mapping Algorithm ◽  
Image Reproduction ◽  
Reproduction Process ◽  
Mapping Technology ◽  
Image Pixels

In the cross-media image reproduction technology, the accurate transfer and reproduction of colour between different media are an important issue in the reproduction process, and the colour mapping technology is the key technology to effectively maintain the image details and improve the level of colour reproduction. Wooden structure in the image colour and colour piece is different, the image of each colour of visual perception is not independent, and every colour in the image pixels is affected by the surrounding pixels, but in the process of image map, without thinking of the pixel space, adjacent pixels of mutual influence in particular, do not let a person particularly be satisfied with the resulting map figure. In the process of image processing by traditional colour mapping algorithm, the colour distortion caused by colour component is ignored and the block diagram of colour mapping system is constructed. With the continuous development of mapping recognition algorithms, the maximum and minimum brightness values in the image are mapped to the maximum and minimum brightness values of the display device by linear mapping algorithm according to the flow of the established recognition algorithm. By establishing the colour adjustment method of the colour mapping image, the processing effect of the mapping algorithm is analysed. The results show that the brightness deviation of the image is reduced and the colour resolution is improved by the colour brightness compensation.

Research on the Subjective Topic Grading Agorithm Based on the Computer Course

2014 ◽  
Vol 1049-1050 ◽  
pp. 1514-1517
Author(s):  
Sai Dong Lv ◽  
Ji Li Xie
Keyword(s):  
Semantic Similarity ◽  
Calculation Method ◽  
Original Method ◽  
Sentence Similarity ◽  
Similarity Calculation ◽  
Similarity Algorithm ◽  
Computer Course ◽  
Calculation Results ◽  
Multi Level ◽  
Level Fusion

Subjective question marking system at present is affected by the attention of people, the subjective topic grading principles are common contrast degree of exam questions similar to those of the reference answer, and based on the improved semantic similarity algorithm, calculation of sentence similarity, the similarity degree of exam questions and reference answer is obtained, thus give scores.And design based on semantic similarity experiment, the experiment results show that the proposed multi-level fusion similarity calculation method to improve the original method, on the basis of integration advantages of various methods, make the calculation results meet the requirements of the scoring system.

Workshop Product Manufacturing Process Information Integration Based on USBOM

2014 ◽  
Vol 607 ◽  
pp. 89-94
Author(s):  
Ai Ming Xu ◽  
Jian Min Gao ◽  
Kun Chen ◽  
Fu Min Chen ◽  
Zhao Wang
Keyword(s):  
Information System ◽  
Information Integration ◽  
Manufacturing Process ◽  
Integration Method ◽  
Heterogeneous Systems ◽  
Semantic Description ◽  
Process Information ◽  
Integration Framework ◽  
Ontology Model ◽  
Product Manufacturing

Workshop is gathered place and exchange centre of product manufacturing process information (PMPI), which is divided by enterprise heterogeneous systems. This led to the integration and sharing difficulty of PMPI and constrains the information system application in workshop, like MES. To solve this problem an information integration method based on semantic BOM was proposed. Firstly, a global ontology model USBOM of PMPI was constructed which given the unified semantic description of PMPI. Based on USBOM a PMPI integration framework was proposed. Finally, a workshop product manufacturing monitoring system was used as an example to verify the feasibility of this approach.

A GRAPH-BASED SEMANTIC SIMILARITY MEASURE FOR THE GENE ONTOLOGY

2011 ◽  
Vol 09 (06) ◽  
pp. 681-695 ◽  
Author(s):  
MARCO A. ALVAREZ ◽  
CHANGHUI YAN
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Common Ancestor ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Similarity Score ◽  
Gene Products ◽  
Semantic Similarity Measure ◽  
Similarity Algorithm ◽  
Go Terms

Existing methods for calculating semantic similarities between pairs of Gene Ontology (GO) terms and gene products often rely on external databases like Gene Ontology Annotation (GOA) that annotate gene products using the GO terms. This dependency leads to some limitations in real applications. Here, we present a semantic similarity algorithm (SSA), that relies exclusively on the GO. When calculating the semantic similarity between a pair of input GO terms, SSA takes into account the shortest path between them, the depth of their nearest common ancestor, and a novel similarity score calculated between the definitions of the involved GO terms. In our work, we use SSA to calculate semantic similarities between pairs of proteins by combining pairwise semantic similarities between the GO terms that annotate the involved proteins. The reliability of SSA was evaluated by comparing the resulting semantic similarities between proteins with the functional similarities between proteins derived from expert annotations or sequence similarity. Comparisons with existing state-of-the-art methods showed that SSA is highly competitive with the other methods. SSA provides a reliable measure for semantics similarity independent of external databases of functional-annotation observations.

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