Information Retrieval Based on the Extracted Social Network

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Advances in Intelligent Systems and Computing

Volume 662

Series editor

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About this Series

The series“Advances in Intelligent Systems and Computing”contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing.

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Hani Hagras, University of Essex, Colchester, UK e-mail: hani@essex.ac.uk

Lá_szló_{T. K}ó_{czy, Sz}é_{chenyi Istv}á_{n University, Gy}ő_{r, Hungary}

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Radek Silhavy

•

Petr Silhavy

Zdenka Prokopova

Editors

Applied Computational

Intelligence and

Mathematical Methods

Computational Methods in Systems

and Software 2017, vol. 2

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Editors

Radek Silhavy

Faculty of Applied Informatics Tomas Bata University in Zlí_n

Zlí_n

Czech Republic

Petr Silhavy

Zlí_n

Czech Republic

Zdenka Prokopova

Zlí_n

Czech Republic

ISSN 2194-5357 ISSN 2194-5365 (electronic)

Advances in Intelligent Systems and Computing

ISBN 978-3-319-67620-3 ISBN 978-3-319-67621-0 (eBook) DOI 10.1007/978-3-319-67621-0

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Preface

This book constitutes the refereed proceedings of the Computational Methods in Systems and Software 2017 (CoMeSySo 2017), which will be held on September 2017.

CoMeSySo 2017 conference intends to provide an international forum for the discussion of the latest high-quality research results in all areas related to intelligent systems. The addressed topics are the theoretical aspects and applications of Software Engineering in Intelligent Systems, Cybernetics and Automation Control Theory, Econometrics, Mathematical Statistics in Applied Sciences and Computational Intelligence.

CoMeSySo 2017 has received (all sections) 129 submissions, 70 of them were accepted for publication. More than 40% of accepted submissions were received from Europe, 30% from Asia, 17% from Africa, and 12% from America. Researches from 27 countries participated in CoMeSySo conference.

The volume Applied Computational Intelligence and Mathematical Methods brings new approaches and methods to real-world problems and exploratory research that describes novel approaches in the mathematical methods, computa-tional intelligence methods, statistics, and software engineering in the scope of the intelligent systems.

The editors believe that readers willﬁnd following proceedings interesting and useful for their own research work.

July 2017 Radek Silhavy

Petr Silhavy Zdenka Prokopova

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Organization

Program Committee

Program Committee Chairs

Petr Silhavy Department of Computers and Communication Systems, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic Radek Silhavy Department of Computers and Communication

Systems, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic Zdenka Prokopova Associate Professor at Department of Computers

and Communication Systems, Tomas Bata University in Zlin, Czech Republic Krzysztof Okarma Faculty of Electrical Engineering, West

Pomeranian University of Technology, Szczecin, Poland

Roman Prokop Department of Mathematics, Tomas Bata University in Zlin, Czech Republic

Viacheslav Zelentsov Doctor of Engineering Sciences, Chief Researcher of St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS), Russian Federation Lipo Wang School of Electrical and Electronic Engineering,

Nanyang Technological University, Singapore Silvie Belaskova Head of Biostatistics, St. Anne’s University

Hospital Brno, International Clinical Research Center, Czech Republic

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International Program Committee Members

Pasi Luukka North European Society for Adaptive

and Intelligent Systems & School of Business and School of Engineering Sciences

Lappeenranta University of Technology, Finland Ondrej Blaha Louisiana State University Health Sciences Center

New Orleans, New Orleans, USA Izabela Jonek-Kowalska Faculty of Organization and Management,

The Silesian University of Technology, Poland Maciej Majewski Department of Engineering of Technical

and Informatic Systems, Koszalin University of Technology, Koszalin, Poland

Alena Vagaska Department of Mathematics, Informatics and Cybernetics, Faculty of Manufacturing Technologies, Technical University of Kosice, Slovak Republic

Boguslaw Cyganek Department of Computer Science, University of Science and Technology, Krakow, Poland Piotr Lech Faculty of Electrical Engineering, West

Pomeranian University of Technology, Szczecin, Poland

Monika Bakosova Institute of Information Engineering, Automation and Mathematics, Slovak University

of Technology, Bratislava, Slovak Republic Pavel Vaclavek Faculty of Electrical Engineering

and Communication, Brno University of Technology, Brno, Czech Republic Miroslaw Ochodek Faculty of Computing, Poznan University

of Technology, Poznan, Poland Olga Brovkina Global Change Research Centre Academy

of Science of the Czech Republic, Brno, Czech Republic

Elarbi Badidi College of Information Technology,

United Arab Emirates University, Al Ain, United Arab Emirates

Gopal Sakarkar Shri. Ramdeobaba College of Engineering and Management, Republic of India V.V. Krishna Maddinala GD Rungta College of Engineering

and Technology, Republic of India

Anand N. Khobragade Maharashtra Remote Sensing Applications Centre, Republic of India

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Abdallah Handoura Computer and Communication Laboratory, Telecom Bretagne– France

Organizing Committee Chair

Radek Silhavy Tomas Bata University in Zlin, Faculty of Applied Informatics

Conference Organizer (Production)

OpenPublish.eu s.r.o.

Web:http://comesyso.openpublish.eu Email: comesyso@openpublish.eu

Conference Website, Call for Papers

http://comesyso.openpublish.eu

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Information Retrieval Based on the Extracted

Social Network

Mahyuddin K. M. Nasution,Rahmad Syah, and Maria Elfida

Information Technology Department,

Fakultas Ilmu Komputer dan Teknologi Informasi (Fasilkom-TI), and

Information system Centre,

Universitas Sumatera Utara, Medan 1500 USU, Sumatera Utara, Indonesia

mahyuddin@usu.ac.id

Abstract. It is possible that a technology affects other technologies. In this paper, explored the possibility to reveal improved information retrieval performance through the extraction method of social network. Any extracted social network structurally is not a perfect graph so it is possible to build the star social networks as the optimal form of graph, which guides to model the implication of information retrieval: the for-mulation of recall and the precision, which generally show better perfor-mance on average over 90% and 58%, respectively.

Key words: information space, webpages, recall, precision, graph, de-gree.

1 Introduction

The social network as a resultant of extraction method from Web is a repre-sentation of relationship between web pages through any search engine [1]. The resultant based on occurrence and co-occurrence [2]. It depends not only on the logically relevance between the query and the webpage but also the similarity between the query and the information available, i.e. the answer to the required information as accurately as possible [3]. The latter case has become the con-centration of information retrieval that is knowledge technology that focuses on the effectiveness and efficiency for retrieving information from information space like Web [4].

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2 M. K. M. Nasution, R. Syah, and M. Elfida

2 Problem Definition

Completeness of social networks is generally expressed in graph G(V, E). The set of vertices V ={vi|i= 1, . . . , I}as visual representations of a set of social

actors A = {ai|i = 1, . . . , I}, ai are the names of possible social actors from

information space Ω, hit count |Ωai| as the cardinality of ai [8]. The set of

edges E ={ej|j = 1, . . . , J}as a visual representation of relation between two

social actors ai, aj ∈ A in the information space Ω, hit counts |Ωai ∩Ωaj| as

the cardinality ofai Andaj [9]. Hit count of two occurrences and co-occurrence

computationally within similarity distance [10], e.g. using

simour(ai, aj) =

is to give weight to each relationship. The weight not only determines the rank of any relation between social actors, but encourages the growth of social networks. Moreover, semantically the weights indicate the proximity of webpages that are not actually interlinked with each other [11]. Thus, social structures indirectly form other structures of documents scattered within the information space, the different structures than links built into webpages or within the server in which the webpage resides.

In general, if a webpage is accessed then another webpage that is interlinked to it will indirectly impacted by the click event. Likewise, if one of social actor’s name becomes the keyword of another social actor’s name in the co-occurrence form, through the extracted social network, then the different webpages although having one of the social actor names will be clustered by search engine into appropriate cluster.

Proposition 1. If a vertex is representation of a social actor in the extracted social network, then a cluster of webpages is representation of a social actor in Web.

Proof. Based on Eq. (1) To get information about a social actor a ∈ A from the Web is assigned a query q, and it generates a collection of webpages Ωa =

{ωk|k= 1, . . . , Ka}as a composition of information: a hit count|Ωa|and list of

snippets Lsa. Formally,|Ωa| ←q =a or Lsa ←q =a. In other words, for all

vi∈V we have

vi=q(ai) =Ωai={ωj|j= 1, . . . , mi} (2)

where Ωai is a cluster of webpages.

Proposition 2. If an edge is representation of the relation between two social actors in the extracted social network, then a cluster of webpages is representation of the relation in Web.

Proof. Eq. (1) have shown for getting information of the relation between two social actorsaiaj∀ai, aj∈Afrom the Web is assigned a queryq, and it produces

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Information Retrieval Based on the Extracted Social Network 3

compotision of information: a hit count|Ωai∩Ωaj|and list of snippetsLsaisaj.

In other words, for allel∈Ewe have

el=q(aiaj) =Ωaiaj =Ωai∩Ωaj ={ωk|k= 1, . . . , ml} (3)

where Ωaiaj is a cluster of webpages.

The cluster of webpages in either occurrence or co-occurrence consists of webpages in general conical to intersection between a cluster based on ai and

a cluster based on aj. Therefore, to obtain a reliable technology it is necessary

to assess the performance of social network extraction methods. Assessment is done on the side of getting trusty information and getting side of technology associated with it. Assume Dr is the set of documents relevant to the query

andDc is an accomplished documents, then the commonly used the assesment

measures are recallRecand precision P resas follows.

1. To measure the permissibility (approximate ability) of reaching approach to all relevant documents based on query,

Rec=|Dr∩Dc|

|Dr|

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2. To measure the ability of the approach to achieve only documents relevant to the query and may reject irrelevant documents,

P res=|Dr∩Dc|

|Dc|

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Theorem 1. If a social network is representation of social structure, then re-sultant of method for extracting social network from Web is representation of webpages structure.

3 An Approach

If the social network extracted from the information source consists ofn social actors, then in general the social structure is formed can be expressed through the degree of social actors. In this case, the social network theoretically consist of two categories as defined below.

Definition 1. A social network is completely shaped or abbreviated a complete social network consistsn vertices and n(n−1) edges. Hereinafter it denoted as SN(n, n(n−1)).

Definition 2. A star-shaped social network or abbreviated astar social network

consistsn vertices and n−1edges. Hereinafter it denoted asSN(n, n−1).

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Fig. 1.Star and complete social network in comparison

Lemma 1. If social actor has the highest degree in social network, then the social actor become candidate of center in social network.

Proof. Suppose there are m social actors within social networks within each degree canbe sorted as followd(v1)> d(v2)≥d(v3)≥ . . .≥d(vm). It is clear

that the social actor with the highest degree d(v1) is center candidate in the social network.

Proposition 3. If there is more than one social actor is of the highest degree and likely to have multiple leaves, the social actors are the candidates of center in subs of social network.

Proof. Based on Lemma 1, if vertices with the highest degree in social network are eliminated but each vertex still has leaves, it is clear that the social actors represented by the vertices become the candidates of center in subs of social network.

In graph theory, a tree is optimal-shaped of graph. The star is one of tree forms.

Lemma 2. If there are vertices with the highest degree in social network, then the subs of social network with social actors as the center are the optimal form of social networks.

Proof. Based on Proposition 3, the subs of social networks have a center of candidate that makes them be a sub social network independently. Suppose that on each sub social network there is mi vertices, by eliminating the edges that

does not cover the center and leaf vertices, or eliminating the leaf-linking edges, it produces mi−1 edges within the sub social network. A sub social network

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This social network is denoted bySNs(mi, mi−1) be subs of star-shaped social

network or the star social networks.

Proposition 4. If there are the star social networks in a social network, then the collection of information spaces about social actors is conjoined into the social actors’ information space as the center of star social networks.

Proof. In formally,SNs(mi, mi−1)⊂SN(n, n(n−1)) are star social networks,

So based on Eq. 1, whereby Proposition 1, 2 and Proposition 4 take the appropriate role to structure the webpages, then Theorem 1 is proved.

Table 1.Statistic of DatasetDr

Personal Name Position Number of Documents

Abdul Razak Hamdan Professor (A1) 103

Abdullah Mohd Zin Professor (A2) 105

Shahrul Azman Mohd Noah Professor (A3) 160 Tengku Mohd Tengku Sembok Professor (A4) 210

Md Jan Nordin Professor (A5) 70

4 Information Retrieval

As the application of Theorem 1, we modelled the structure of webpages for information retrieval in social network extraction perspective based on Eq. 4 and Eq. 5. The documents setDr is modeled as a collection of all the collected

webpages based on the academic actor (professor), in which each document has the unique identity that is URL address. From a collection of academic actors, there are the star social networks in which each professor as a center and an other academic actors (who is not a professor) as a leaf, and each query is built from a pair of such social actors. The documents Dc was obtained based on the

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Personal Name Disambiguation Star Social Network

(Professor) Recall Precision Recall Precision

A1 47/103 (45.63%) 47/154(30.52%) 99/103 (96.12%) 99/194(51.03%) A2 49/105 (46.67%) 49/158(31.01%) 101/105 (96.19%) 101/188(33.72%) A3 72/160 (45.00%) 72/152(47.37%) 155/160 (96.88%) 155/222(69.82%) A4 92/210 (43.81%) 92/148(62.16%) 200/210 (95.24%) 200/258(77.52%) A5 32/70 (45.71%) 32/154(20.78%) 68/70 (97.14%) 68/164(41.46%)

of 648 webpages like Table 1. By using concept the name disambiguation, Eq. 4 and Eq. 5 produce Table 2.

In general, there is an increase in the performance of the access process by means of involving social networks whereby the webpages of influential social actors will be dug up by other social actors as keywords rather than keywords that are not the names of social actors. Thus, the formulation of recall and precision based on Proposition 4 are as follows:

Reca=

where mis degree of center in star social network.

5 Conclusion and Future Work

By studying the principle of social network extraction, from the extraction method, there is a change in formulation on the implication of information re-trieval that is recall and precision, which generally have better performance based on computation. Furthermore, to test this formula will be built larger datasets.

References

1. Mahyuddin K. M. Nasution, and S. A. M. Noah. 2010. Superficial method for extracting social network for academic using web snippets. In Yu, J. et al., (eds.). Rough Set and Knowledge Technology, LNAI 6401, Heidelberg, Springer: 483-490. 2. Mahyuddin K. M. Nasution. 2016. Social network mining (SNM): A definition of relation between the resources and SNA. International Journal on Advanced Science Engineering Information Technology 6(6): 975-981.

3. Mahyuddin K. M. Nasution and Shahrul Azman Noah. 2012. Information Retrieval Model: A Social Network Extraction Perspective. CAMP 2012, IEEE.

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5. Mahyuddin K. M. Nasution, Opim Salim Sitompul, Emerson P. Sinulingga, and Shahrul Azman Noah. 2016. An extracted social network mining. SAI Computing Conference, IEEE.

6. Mahyuddin K. M. Nasution. 2016. Modelling and simulation of search engine. International Conference on Computing and Applied Informatics (ICCAI), IOP. 7. Mahyuddin K. M. Nasution. 2014. New method for extracting keyword for the

social actor. Intelligent Information and Database Systems LNCS Volume 8397: 83-92.

8. Mahyuddin K. M. Nasution. 2012. Simple search engine model: Adaptive proper-ties. Cornell University Library arXiv:1212.3906v1.

9. Mahyuddin K. M. Nasution. 2012. Simple search engine model: Adaptive properties for doubleton. Cornell University Library arXiv:1212.4702v1.

10. Mahyuddin K. M. Nasution. 2016. New similarity. Annual Applied Science and Engineering Conference (AASEC), IOP.

Information Retrieval Based on the Extracted Social Network

Advances in Intelligent Systems and Computing

Volume 662

Radek Silhavy

Petr Silhavy

Zdenka Prokopova

Editors

Applied Computational

Intelligence and

Mathematical Methods

Computational Methods in Systems

and Software 2017, vol. 2

Preface

Organization

Program Committee

Program Committee Chairs

International Program Committee Members

Organizing Committee Chair

Conference Organizer (Production)

Conference Website, Call for Papers

Contents

Information Retrieval Based on the Extracted

Social Network

1

Introduction

2

Problem Definition

3

An Approach

4

Information Retrieval

5

Conclusion and Future Work

References