Enhancing Community Detection in Social Network using Ontology

被引：0

作者：

Khattab, Salma ^{[1
]}

ElKorany, Abeer ^{[1
]}

Salah, Akram ^{[1
]}

机构：

[1] Cairo Univ, Fac Comp & Informat, Dept Comp Sci, Giza, Egypt

来源：

KEOD: PROCEEDINGS OF THE 8TH INTERNATIONAL JOINT CONFERENCE ON KNOWLEDGE DISCOVERY, KNOWLEDGE ENGINEERING AND KNOWLEDGE MANAGEMENT - VOL. 2 | 2016年

关键词：

Ontology; Semantic User Profile; Similarity; Modularity; Community Detection;

D O I：

10.5220/0006067801500156

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, social networks have been spread widely. Within social network, people tend to form communities in order to have more chances to share opinions, experiences and expertise. Users in social networks belong to the same community according to their behaviour and common interest. This paper presents a semantic approach for community extraction based on identifying the interest of user in order to group them into communities. An ontological user profile is created indicating user interest that is associated with items domain ontology. A set of experiments was applied using real dataset (BookCrossing) to measure the accuracy of the proposed semantic-based framework.

引用

页码：150 / 156

页数：7

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