scholarly journals A modern semantic similarity method using multiple resources for enhancing influenza detection

2022 ◽  
pp. 116466
Author(s):  
Abdullah Almuhaimeed ◽  
Mohammed A. Alhomidi ◽  
Mohammed N. Alenezi ◽  
Emad Alamoud ◽  
Saad Alqahtani
Keyword(s):  
Semantic Similarity ◽  
Multiple Resources ◽  
Similarity Method

scholarly journals Generating Requirement Dependency Graph Based on Class Dependency

2019 ◽  
pp. 19
Author(s):  
Hernawati Susanti Samosir ◽  
Daniel Siahaan
Keyword(s):  
Semantic Similarity ◽  
Cost Estimation ◽  
Management Development ◽  
Kappa Statistics ◽  
Software Project ◽  
Structure Generation ◽  
Relational Information ◽  
Kappa Value ◽  
Decision Making Processes ◽  
Similarity Method

Requirements association depicts inter-relation between two or more requirements within a software project. It provides necessary information for developers during decision-making processes, such as change management, development milestones, bug prediction, cost estimation, and work breakdown structure generation. Modeling association between requirements became a focus of software requirements researchers. Previous studies indicate that requirements association was pre-defined by requirements engineer based on their expert judgments. The judgments require knowledge on requirements and their class realizations. This paper introduces a method to generate a mapping between a set of requirement statements and a set of classes of a given project that realized the respected requirements. The method also generates associations among requirements based on information on associations between classes and the class-requirement mapping. The method utilizes element of relational information resided in a class diagram of respected project. A semantic similarity method was used to define the requirements with their realization classes. A class is considered realizing a requirement if and only if their semantic similarity is higher than a certain threshold. A set of experimentation on four different projects was conducted. The result of the approach was compared with the output produced by human annotators using kappa statistics. The approach is considered as having a fair agreement level (i.e. with kappa value 0.37) with the human annotators to identify and model requirement associations.

scholarly journals Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

2019 ◽  
Vol 2 (3) ◽  
pp. 334
Author(s):  
Imam Fahrurrozi ◽  
Estu Muh Dwi Admoko ◽  
Anang Susilo
Keyword(s):  
Collaborative Filtering ◽  
Semantic Similarity ◽  
Recommender System ◽  
Cold Start ◽  
Performance Tests ◽  
Online Commerce ◽  
Filtering Method ◽  
Similarity Method

Recommender system is a component which has been developed for online commerce purposes. In this issue, one of the popular methods that has been widely used is collaborative filtering. However, this method has some drawbacks and needs to be improved. Therefore, in this research a combination of Collaborative Filtering (CF) and semantic similarity method has been compare with original CF, and the result expected reducing some deficiencies on the original collaborative filtering method. Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue.

GO-Based Term Semantic Similarity

2011 ◽  
pp. 174-185 ◽  
Author(s):  
Marco A. Alvarez ◽  
Xiaojun Qi ◽  
Changhui Yan
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Data Sources ◽  
Future Research ◽  
External Data ◽  
Basic Concepts ◽  
Similarity Method ◽  
Go Terms

As the Gene Ontology (GO) plays more and more important roles in bioinformatics research, there has been great interest in developing objective and accurate methods for calculating semantic similarity between GO terms. In this chapter, the authors first introduce the basic concepts related to the GO and then briefly review the current advances and challenges in the development of methods for calculating semantic similarity between GO terms. Then, the authors introduce a semantic similarity method that does not rely on external data sources. Using this method as an example, the authors show how different properties of the GO can be explored to calculate semantic similarities between pairs of GO terms. The authors conclude the chapter by presenting some thoughts on the directions for future research in this field.

Semantic similarity method for keyword query system on RDF

2014 ◽  
Vol 146 ◽  
pp. 264-275 ◽  
Author(s):  
Minho Bae ◽  
Sanggil Kang ◽  
Sangyoon Oh
Keyword(s):  
Semantic Similarity ◽  
Keyword Query ◽  
Query System ◽  
Similarity Method

GO-Based Term Semantic Similarity

2013 ◽  
pp. 93-104
Author(s):  
Marco A. Alvarez ◽  
Xiaojun Qi ◽  
Changhui Yan
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Data Sources ◽  
Future Research ◽  
External Data ◽  
Basic Concepts ◽  
Similarity Method ◽  
Go Terms

As the Gene Ontology (GO) plays more and more important roles in bioinformatics research, there has been great interest in developing objective and accurate methods for calculating semantic similarity between GO terms. In this chapter, the authors first introduce the basic concepts related to the GO and then briefly review the current advances and challenges in the development of methods for calculating semantic similarity between GO terms. Then, the authors introduce a semantic similarity method that does not rely on external data sources. Using this method as an example, the authors show how different properties of the GO can be explored to calculate semantic similarities between pairs of GO terms. The authors conclude the chapter by presenting some thoughts on the directions for future research in this field.

Information Retrieval by Semantic Similarity

2011 ◽  
pp. 647-665 ◽  
Author(s):  
Angelos Hliaoutakis ◽  
Giannis Varelas ◽  
Epimenidis Voutsakis ◽  
Euripides G.M. Petrakis ◽  
Evangelos Milios
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Vector Space Model ◽  
Retrieval Method ◽  
Space Model ◽  
Performance Improvements ◽  
Reference Ontology ◽  
Information Retrieval Methods ◽  
Similarity Method ◽  
The Web

Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-theart semantic similarity retrieval methods utilizing ontologies.

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