Manhattan Based Hybrid Semantic Similarity Algorithm for Geospatial Ontologies

2013 ◽  
pp. 1-10
Author(s):  
K. Saruladha ◽  
E. Thirumagal ◽  
J. Arthi ◽  
G. Aghila
Keyword(s):  
Semantic Similarity ◽  
Similarity Algorithm ◽  
Geospatial Ontologies

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

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.

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.

scholarly journals An improved semantic similarity algorithm based on HowNet and CiLin

2020 ◽  
Vol 309 ◽  
pp. 03004
Author(s):  
Ying Wang ◽  
Xiwei Feng ◽  
Yue Zhang ◽  
Haiming Chen ◽  
Lijie Xing
Keyword(s):  
Semantic Similarity ◽  
Calculation Method ◽  
Improved Method ◽  
Data Set ◽  
Specific Calculation ◽  
Word Similarity ◽  
Pearson Coefficient ◽  
Similarity Calculation ◽  
Similarity Algorithm

This paper explores an improved method for the semantic similarity calculation of words combined with HowNet and CiLin. Firstly, we designing the algorithm based on HowNet’s sememe similarity improvement calculation, comprehensively considering the influence of each part of sememe on the overall meaning, and improving the calculation of word similarity based on HowNet by changing the specific calculation method of each part of sememe. At the same time, we adopt different strategies for the different results obtained in the similarity calculation of CiLin. The experimental RG data set proves that the modified Pearson coefficient of the method reaches 0.87.

The Semantic Retrieval Model of Manufacturing Resource Based on Rules and Similarity

2011 ◽  
Vol 130-134 ◽  
pp. 483-486
Author(s):  
Jun Ying Wei ◽  
Pei Si Zhong
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Semantic Retrieval ◽  
Retrieval Model ◽  
Node Density ◽  
Weighted Distance ◽  
Similarity Algorithm ◽  
Manufacturing Resource ◽  
The Common ◽  
Concept Attribute

Based on the common similarity algorithm between ontology concepts, this paper makes use of semantic weighted distance and introduces such factors as node density, concept attribute and concept information content, and presents an improved semantic similarity algorithm to make the measuring semantic similarity more accurate. Combined the similarity algorithm with SWRL, this paper establishes a semantic retrieval model of manufacturing resource based on rules and similarity , which applies the rules of SWRL to carry on conditional matching in the ontology concepts of manufacturing resource in order to retrieve more eligible results.

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