A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

2019 ◽  
Vol 38 (2) ◽  
pp. 399-419 ◽  
Author(s):  
M. Priya ◽  
Aswani Kumar Ch.
Keyword(s):  
Social Network ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Semantic Similarity Measure ◽  
Content Type ◽  
Ontology Merging ◽  
Academic Social Network

Purpose The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure. Design/methodology/approach The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part. Findings The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain. Practical implications In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers. Originality/value The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

Identification of substructures in complex networks using formal concept analysis

2018 ◽  
Vol 14 (3) ◽  
pp. 281-298 ◽  
Author(s):  
Sebastião M. Neto ◽  
Sérgio Dias ◽  
Rokia Missaoui ◽  
Luis Zárate ◽  
Mark Song
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Formal Concept Analysis ◽  
Computational Models ◽  
Behavior Pattern ◽  
Concept Analysis ◽  
Behavior Patterns ◽  
Formal Concept ◽  
Content Type ◽  
Access Patterns

Purpose In recent years, the increasing complexity of the hyper-connected world demands new approaches for social network analysis. The main challenges are to find new computational methods that allow the representation, characterization and analysis of these social networks. Nowadays, formal concept analysis (FCA) is considered an alternative to identifying conceptual structures in a social network. In this FCA-based work, this paper aims to show the potential of building computational models based on implications to represent and analyze two-mode networks. Design/methodology/approach This study proposes an approach to find three important substructures in social networks such as conservative access patterns, minimum behavior patterns and canonical access patterns. The present study approach considered as a case study a database containing the access logs of a cable internet service provider. Findings The result allows us to uncover access patterns, conservative access patterns and minimum access behavior patterns. Furthermore, through the use of implications sets, the relationships between event-type elements (websites) in two-mode networks are analyzed. This paper discusses, in a generic form, the adopted procedures that can be extended to other social networks. Originality/value A new approach is proposed for the identification of conservative behavior in two-mode networks. The proper implications needed to handle minimum behavior pattern in two-mode networks is also proposed to be analyzed. The one-item conclusion implications are easy to understand and can be more relevant to anyone looking for one particular website access pattern. Finally, a method for a canonical behavior representation in two-mode networks using a canonical set of implications (steam base), which present a minimal set of implications without loss of information, is proposed.

Analysis of tweets to find the basis of popularity based on events semantic similarity

2018 ◽  
Vol 14 (4) ◽  
pp. 438-452 ◽  
Author(s):  
Rajat Kumar Mudgal ◽  
Rajdeep Niyogi ◽  
Alfredo Milani ◽  
Valentina Franzoni
Keyword(s):  
Data Structure ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Design Methodology ◽  
Data Set ◽  
Semantic Similarity Measure ◽  
Web Based ◽  
Content Type ◽  
Social Trends ◽  
Event Information

PurposeThe purpose of this paper is to propose and experiment a framework for analysing the tweets to find the basis of popularity of a person and extract the reasons supporting the popularity. Although the problem of analysing tweets to detect popular events and trends has recently attracted extensive research efforts, not much emphasis has been given to find out the reasons behind the popularity of a person based on tweets.Design/methodology/approachIn this paper, the authors introduce a framework to find out the reasons behind the popularity of a person based on the analysis of events and the evaluation of a Web-based semantic set similarity measure applied to tweets. The methodology uses the semantic similarity measure to group similar tweets in events. Although the tweets cannot contain identical hashtags, they can refer to a unique topic with equivalent or related terminology. A special data structure maintains event information, related keywords and statistics to extract the reasons supporting popularity.FindingsAn implementation of the algorithms has been experimented on a data set of 218,490 tweets from five different countries for popularity detection and reasons extraction. The experimental results are quite encouraging and consistent in determining the reasons behind popularity. The use of Web-based semantic similarity measure is based on statistics extracted from search engines, it allows to dynamically adapt the similarity values to the variation on the correlation of words depending on current social trends.Originality/valueTo the best of the authors’ knowledge, the proposed method for finding the reason of popularity in short messages is original. The semantic set similarity presented in the paper is an original asymmetric variant of a similarity scheme developed in the context of semantic image recognition.

A Method of Social Network Analysis Using Formal Concept Analysis and Ego-Centric Networks

2014 ◽  
Vol 29 (1) ◽  
pp. 177-181 ◽  
Author(s):  
Hiroki Hayashi ◽  
Tatsuki Itou ◽  
Noriaki Nishio ◽  
Atsuko Mutoh ◽  
Nobuhiro Inuzuka
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept

scholarly journals Detection Of Maximal Balanced Clique In Signed Networks Based On Improved Three-Way Concept Lattice and Modified Formal Concept Analysis

2021 ◽  
Author(s):  
Yixuan Yang ◽  
Doo-Soon Park ◽  
Fei Hao ◽  
Sony Peng ◽  
Min-Pyo Hong ◽  
...  
Keyword(s):  
Social Network ◽  
Formal Concept Analysis ◽  
Industrial Revolution ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Signed Networks ◽  
Social Network Structure ◽  
Real World Datasets

Abstract In the era of artificial intelligence including the fourth industrial revolution, social networks analyzing is a significant topic in big data analysis. Clique detection is a state-of-the-art technique in social network structure mining, which is widely used in a particular social network like signed network. There are positive and negative relationships in signed networks which detect not only cliques or maximal cliques but also maximal balanced cliques.In this paper, two algorithms have been addressed to the problems. First, we modify three-way concept lattice algorithm using a modified formal context and supplement formal context to obtain an object-induced three-way concept lattice (OE-concept) to detect the maximal balanced cliques. Second, in order to improve the cost of memory and efficiency, we modify formal concept analysis algorithm by using modified formal context combine with supplement formal context to find the maximal balance cliques. Additionally, we utilized four real-world datasets to test our proposed approaches as well as the running time in the experimental section.

Minimal implications base for social network analysis

2018 ◽  
Vol 14 (1) ◽  
pp. 62-77
Author(s):  
Paula Raissa ◽  
Sérgio Dias ◽  
Mark Song ◽  
Luis Zárate
Keyword(s):  
Social Network ◽  
Concept Analysis ◽  
Reduced Form ◽  
Access Time ◽  
Formal Context ◽  
Formal Concept ◽  
Minimal Set ◽  
Data Set ◽  
Content Type ◽  
Closed Sets

Purpose Currently, social network (SN) analysis is focused on the discovery of activity and social relationship patterns. Usually, these relationships are not easily and completely observed. Therefore, it is relevant to discover substructures and potential behavior patterns in SN. Recently, formal concept analysis (FCA) has been applied for this purpose. FCA is a concept analysis theory that identifies concept structures within a data set. The representation of SN patterns through implication rules based on FCA enables the identification of relevant substructures that cannot be easily identified. The authors’ approach considers a minimum and irreducible set of implication rules (stem base) to represent the complete set of data (activity in the network). Applying this to an SN is of interest because it can represent all the relationships using a reduced form. So, the purpose of this paper is to represent social networks through the steam base. Design/methodology/approach The authors’ approach permits to analyze two-mode networks by transforming access activities of SN into a formal context. From this context, it can be extracted to a minimal set of implications applying the NextClosure algorithm, which is based on the closed sets theory that provides to extract a complete, minimal and non-redundant set of implications. Based on the minimal set, the authors analyzed the relationships between premises and their respective conclusions to find basic user behaviors. Findings The experiments pointed out that implications, represented as a complex network, enable the identification and visualization of minimal substructures, which could not be found in two-mode network representation. The results also indicated that relations among premises and conclusions represent navigation behavior of SN functionalities. This approach enables to analyze the following behaviors: conservative, transitive, main functionalities and access time. The results also demonstrated that the relations between premises and conclusions represented the navigation behavior based on the functionalities of SN. The authors applied their approach for an SN for a relationship to explore the minimal access patterns of navigation. Originality/value The authors present an FCA-based approach to obtain the minimal set of implications capable of representing the minimum structure of the users’ behavior in an SN. The paper defines and analyzes three types of rules that form the sets of implications. These types of rules define substructures of the network, the capacity of generation users’ behaviors, transitive behavior and conservative capacity when the temporal aspect is considered.

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