Enhancing Extraction Method for Aggregating Strength Relation Between Social Actors

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Advances in Intelligent Systems and Computing

Volume 573

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About this Series

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Radek Silhavy

•

Roman Senkerik

Zuzana Kominkova Oplatkova

Zdenka Prokopova

•

Petr Silhavy

Editors

Arti

_ﬁ

cial Intelligence Trends

in Intelligent Systems

Proceedings of the 6th Computer

Science On-line Conference 2017

(CSOC2017), Vol 1

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Editors

Radek Silhavy

Faculty of Applied Informatics Tomas Bata University in Zlí_n Zlin

Czech Republic Roman Senkerik

Czech Republic

Zuzana Kominkova Oplatkova Faculty of Applied Informatics Tomas Bata University in Zlí_n Zlin

Czech Republic

Zdenka Prokopova

Czech Republic Petr Silhavy

Czech Republic

ISSN 2194-5357 ISSN 2194-5365 (electronic) Advances in Intelligent Systems and Computing

ISBN 978-3-319-57260-4 ISBN 978-3-319-57261-1 (eBook) DOI 10.1007/978-3-319-57261-1

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Preface

This book constitutes the refereed proceedings of the Artiﬁcial Intelligence Trends in Intelligent Systems Section of the 6th Computer Science On-line Conference 2017 (CSOC 2017), held in April 2017.

Particular emphasis is laid on modern trends in artiﬁcial intelligence and its application to intelligent systems. New algorithms, methods, and applications of optimization algorithm, neural network, and deep learning and hybrid algorithms are also presented.

The volume Artificial Intelligence Trends in Intelligent Systems brings and presents new approaches and methods to real-world problems and exploratory research that describes novel approaches in the definedfields.

CSOC 2017 has received (all sections) 296 submissions, in which 148 of them were accepted for publication. More than 61% of accepted submissions were received from Europe, 34% from Asia, 3% from Africa, and 2% from America. Researches from 27 countries participated in CSOC 2017 conference.

CSOC 2017 conference intends to provide an international forum for the dis-cussion of the latest high-quality research results in all areas related to computer science. The addressed topics are the theoretical aspects and applications of com-puter science, artiﬁcial intelligences, cybernetics, automation control theory, and software engineering.

Computer Science On-line Conference is held online, and modern communi-cation technology which is broadly used improves the traditional concept of sci-entiﬁc conferences. It brings equal opportunity to participate to all researchers around the world.

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

March 2017 Radek Silhavy

Petr Silhavy Zdenka Prokopova Roman Senkerik Zuzana Kominkova Oplatkova

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Organization

Program Committee

Program Committee Chairs

Zdenka Prokopova, Ph.D., Associate Professor, Tomas Bata University in Zlin, Faculty of Applied Informatics, email: prokopova@fai.utb.cz

Zuzana Kominkova Oplatkova, Ph.D., Associate Professor, Tomas Bata University in Zlin, Faculty of Applied Informatics, email: kominkovao-platkova@fai.utb.cz

Roman Senkerik, Ph.D., Associate Professor, Tomas Bata University in Zlin, Faculty of Applied Informatics, email: senkerik@fai.utb.cz

Petr Silhavy, Ph.D., Senior Lecturer, Tomas Bata University in Zlin, Faculty of Applied Informatics, email: psilhavy@fai.utb.cz

Radek Silhavy, Ph.D., Senior Lecturer, Tomas Bata University in Zlin, Faculty of Applied Informatics, email: rsilhavy@fai.utb.cz

Roman Prokop, Ph.D., Professor, Tomas Bata University in Zlin, Faculty of Applied Informatics, email: prokop@fai.utb.cz

Prof. Viacheslav Zelentsov, Doctor of Engineering Sciences, Chief Researcher of St. Petersburg Institute for Informatics and Automation of Russian Academy of Sciences (SPIIRAS).

Program Committee Members

Boguslaw Cyganek, Ph.D., DSc, Department of Computer Science, University of Science and Technology, Krakow, Poland.

Krzysztof Okarma, Ph.D., DSc, Faculty of Electrical Engineering, West Pomeranian University of Technology, Szczecin, Poland.

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Monika Bakosova, Ph.D., Associate Professor, Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology, Bratislava, Slovak Republic.

Pavel Vaclavek, Ph.D., Associate Professor, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czech Republic.

Miroslaw Ochodek, Ph.D., Faculty of Computing, Poznan University of Technology, Poznan, Poland.

Olga Brovkina, Ph.D., Global Change Research Centre Academy of Science of the Czech Republic, Brno, Czech Republic & Mendel University of Brno, Czech Republic.

Elarbi Badidi, Ph.D., College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates.

Luis Alberto Morales Rosales, Head of the Master Program in Computer Science, Superior Technological Institute of Misantla, Mexico.

Mariana Lobato Baes, M.Sc., Research-Professor, Superior Technological of Libres, Mexico.

Abdessattar Chaâ_{ri, Professor, Laboratory of Sciences and Techniques of} Automatic control & Computer engineering, University of Sfax, Tunisian Republic. Gopal Sakarkar, Shri. Ramdeobaba College of Engineering and Management, Republic of India.

V.V. Krishna Maddinala, Assistant Professor, GD Rungta College of Engineering & Technology, Republic of India.

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

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Roman Senkerik Petr Silhavy Radek Silhavy Jiri Vojtesek Eva Volna Janez Brest Ales Zamuda Roman Prokop Boguslaw Cyganek Krzysztof Okarma Monika Bakosova Pavel Vaclavek Olga Brovkina Elarbi Badidi

Organizing Committee Chair

Radek Silhavy, Ph.D., Tomas Bata University in Zlin, Faculty of Applied Informatics, email: rsilhavy@fai.utb.cz

Conference Organizer (Production)

OpenPublish.eu s.r.o.

Web:http://www.openpublish.eu Email: csoc@openpublish.eu

Conference Website, Call for Papers

http://www.openpublish.eu

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Enhancing Extraction Method for Aggregating

Strength Relation Between Social Actors

Mahyuddin K.M. Nasution(B)_{and Opim Salim Sitompul}

Information Technology Department, Fakultas Ilmu Komputer Dan Teknologi Informasi (Fasilkom-TI), and Information System Centre,

Universitas Sumatera Utara, 1500 USU, Medan, Sumatera Utara, Indonesia mahyuddin@usu.ac.id

Abstract. There are differences in the resultant of extracting the rela-tions between social actors based on two streams of approaches in prin-ciple. However, one of the methods like the superficial methods can upgraded to make the information extraction by using the principles of the other methods, and this needs proof systematically. This paper serves to reveal some formulations have the function for resolving this issue. Based on the results of experiments conducted the expanded method is the adequate.

Keywords: Search engine

·

Search term

·

Query

·

Social actor

·

Singleton

·

Doubleton

1 Introduction

Extracting social network from Web has carried out with a variety of approaches ranging from simple to complex [1]. Unsupervised method or superficial method generally more concise and low cost, but only generates the strength relations between social actors from heterogeneous and unstructured sources such as the Web [2]. Instead, supervised methods are generally more complicated and high cost and it produces labels of relationship between social actors, but it came from sources, homogeneous and semi-structured like corpuses [3,4]. However, to generate social networks that enable to express semantically meaning is not easy [5]. This requires a method to represent their privilege of both methods: An approach is not only produces a relationship but re-interpret the relationship based on the aggregation principle. This paper aimed to enhance the superficial method for extracting social network from Web.

2 Problem Definition

The initial concept semantically of the extraction of social network from Web is to explore a series of names through co-occurrence using search engine [6,7]. Then, the extraction of social network made possible by involving the occurrence. Formally, the following we stated extracting social networks [8,9].

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Springer International Publishing AG 2017

R. Silhavy et al. (eds.),Artificial Intelligence Trends in Intelligent Systems,

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Enhancing Extraction Method for Aggregating Strength Relation 313

Occurrence and co-occurrence individually are a query (q) representing a social actor and a query representing a pair of social actors. On the occurrence, qcontains a name of social actor, for exampleq=”Mahyuddin K. M. Nasution”. While on the co-occurrence,q contains two names of social actors, for example q =”Mahyuddin K. M. Nasution”, ”Shahrul Azman Noah” [2]. Therefore, names of social actors are the search terms, and we define it formally as follows

Definition 2. A search term tk consists of words or phrase, i.e.tk ={wk|k=

1, . . . , o}.

We use the well query to pry information from the Web by submitting it to search engine. A search engine works on a collection of documents or web pages, or more precisely as follows [10].

Definition 3. Ωis a set of web pages indexed search engine, if there are a table

relation of(ti, ωj)such thatΩ={(t, ω)ij}, whereti is search terms andωj is a

Proposition 1. Iftx∈ S,S is a set of singleton search terms of search engine,

then there are vector spaceΩx⊆Ω for generating hit count|Ωx|.

Proof. For tx ∈ S, it means that tx ∈ ω and ω ∈ Ω, or S ∈ Ω, every web page that is indexed by search engine contains at least one occurrence of tx, where based on Definition3 Ωx = {(tx, ωx)ij}. Thus for all web pages be in force {Ωx(tx) = 1} so thatΩx⊆Ω be the singleton search engine event of web pages that contain an occurrence oftx∈ωx, and based on Definition4, we have

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314 M.K.M. Nasution and O.S. Sitompul

As information of any social actor, the singleton is the basic of search engine property that statistically related to the social actor. In this case, the singleton be the necessary condition for gaining the information of social actor from Web although it contains connatural trait (bias and ambiguity), and naturally it becomes the social dynamic of human beings [2]. Hit count is main information for a social actor based on Web, and validation of this information can obtained by crawling one after one the snippets list returned by the search engine [11].

Definition 5. Let tx is a search term. A snippet is a set of words, i.e. sx =

{wi|i = 1, . . . ,±50}. A list of snippets is Lx ={sj|j = 1, . . . , J}x,m ≤ |Ωx|, then size of snippet|sx|=±50words andLx is a matrixMjk that consists ofj rows and kcolumns where each column refers to one token of word w∈sj.

Lemma 1. If wis a token inL, thenwstatistically has the character.

Proof. It is known that w ∈ sx, sx ∈ L, we havew as one token inL. Based proved thatwhas the character, i.e. the relative probability ofw

p(w) = |w|

Proof. Similar to Proposition1, and based on Definitions3 and 6, we have the hit count of doubleton as follows

|Ωx∩Ωy|=

Ω

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Lemma 2. If wis a token inLD as list of snippets based on doubleton, thenw

statistically has the character in the doubleton.

Proof. Similar to Lemma1, and based on Definitions5 and6, we have the char-acter of win the doubleton as follows

pD(w) =

|w| |Ωx∩Ωy|

∈[0,1], (4) where |w| ≤ |Ωx∩Ωy| and|Ωx∩Ωy| = 0.

Fig. 1.Type of snippets based on co-occurrence (Google search engine)

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of three (triple) dots between two names of social actors. Triple dots naturally is a word in text. The direct relations represented by direct co-occurrences like co-author, but the indirect relations represented by indirect co-occurrences such as citation or present on same event.

3 The Proposed Approach

The method of extracting information from Web recognized as the superficial method, categorized in unsupervised stream, involving a search engine to obtain the information like the hit counts used in computation [12]. Generally, for gen-erating relation between actors applied the similarity measurement [13].

Definition 7. rs∈Ris the strength relation between two social actorsa, b∈A

if it meets the comparison among the different information of two actors (aand

b)and the common information of them (a∩b)in the similarity measurement. Orsr=sim(a,b,a∩b)in [0,1],a∩b≤a anda∩b≤b.

Suppose we use Jaccard coefficient, we possesssrbased on hit counts

sr= |Ωa∩Ωb|

|Ωa|+|Ωb| − |Ωa∩Ωb|

∈[0,1], (5) where|Ωa∩Ωb| ≤ |Ωa| and|Ωa∩Ωb| ≤ |Ωb|.

Lemma 3. If ir is a indirect relation between two social actors a, b∈A, then

ir statistically has the character in the doubleton.

Proof. Suppose the indirect relationsircan be recognized in each snippet based on doubleton, we have number of the indirect relations in the snippets list based on doubleton or |ir|, |ir| = number of snippets contain triple dots. Therefore, we generate the character ofiras follows

p(ir) = |ir|

|Ωa∩Ωb|

∈[0,1], (6) where|ir| ≤ |Ωa∩Ωb|and|Ωa∩Ωb| = 0.

Proposition 3. If sr is a strength relation between two social actorsa, b∈A,

then the aggregation ofsr consists of three binderies.

Proof. Suppose p(ir) ∈ [0,1] (Eq. (6)) as probability of the indirect relation based on doubleton, then probability of the direct relation (dr) based on dou-bleton is as follows

p(dr) = |dr|

|Ωa∩Ωb|

∈[0,1], (7) where|dr| ≤ |Ωa∩Ωb|and|Ωa∩Ωb| = 0.

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characteristics arep(ir) andp(dr), respectively. However, 1−p(ir)−p(dr)≥0, ifp(ir) +p(dr)−1= 0, we obtain

p(ur) = 1−(p(ir) +p(dr)) (8) i.e. the character of relation has not be determined with certainty through the co-occurrence. Becausep(ir),p(dr) andp(ur) can be considered as the percentage values, the multiplication of a characteristic with the strength relation regarded as bindery based on type of relations. Therefore, we have three bindings of the strength relations as follows

J1 A bindery of strength relations based on the direct relations,

srdr=sr∗p(dr)∈[0,1] (9) J2 A bindery of strength relations based on the indirect relations,

srir=sr∗p(ir)∈[0,1] (10) J3 A bindery of strength relations based on the unclear relations,

srur=sr∗p(ur)∈[0,1] (11)

Fig. 2.Type of relations based the social network extraction

Proposition 4. If sr is a strength relation between two social actors a, b∈A,

then the aggregation ofsr consists of sheets.

Proof. Based on Proposition 3and by applying Eq. (4) to the strength relation sr, we can generate the aggregations based on words and we call it as the sheets of relationssh, i.e.

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Generally, this concept is considered to be an approach to the concept of latent semantic analysis [14] that have been put forward and produce labels on the social networks based on the supervised stream or the generative probabilistic model (PGM) [4,15]. This approach as enhancing for superficial method [16,17].

Theorem 1. sris the strength relation between two actors a, b∈Aif and only

if there are aggregation.

Proof. This is a direct consequence of Propositions3 and 4 as the necessary conditions, and Lemmas1,2 and3as the sufficient conditions, see Fig.2.

generate (keyword) INPUT : A set of actors

OUTPUT : aggregation of the strength relations STEPS :

1. |Ωa| ←ta query and search engine. 2. |Ωb| ←tb query and search engine.

3. |Ωa∩Ωa| ←ta∧tb query and search engine.A={w1, w2, . . . , wn} ←Collect words-(terms) per a pair of actors from snippets based on doubleton. 4. |dr| ←List of snippets based on doubleton.

5. |ir| ←List of snippets based on doubleton. 6. sr∗p(dr) andsr∗p(ir)

7. Aggregating sr∗p(dr) andsr∗p(ir) based on the summation of sheets per domain.

8. Measuring recall and precision of relations.

4 Experiment

In this experiment, we implicate n = 469 social actors or n(n−1) = 219,492 potential relations. There are 30,044 strength relations between 469 actors or 14% of potential relations, among them (a) 4,422 direct relations (2%), (b) 21,462 indirect relations (10%), and (c) 4,160 direct and indirect relations (2%). There-fore, there are 21,462 lists of snippets of doubleton (LD) contain the triple dots

in all snippets, or there are 4,422 lists of snippets of doubleton (LD) have no

dots in all snippets.

Suppose we define the ontology domain and taxonomically we interpret in a set of words as follows

1. Direct relations:

(a) author-relationship ={activity, article, author, authors, award, journal, journals, paper, patent, presentation, proceedings, publication, theme, poster,. . .}.

(b) academic rule ={supervisor, cosupervisor, editor, editors, graduate, lec-turer, professor, prof, researcher, reviewer, student,. . .}.

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Table 1.The strength relation, direct and indirect relations, and author-relationship

sr

1. Abdullah Mohd Zin 0.0482 0.0395

2. Abdul Razak Hamdan 0.0237

3. Tengku Mohd Tengku Sembok

dr ir dr ir

1. Abdullah Mohd Zin 0.0163 0.0815 0.0975 0.0612 2. Abdul Razak Hamdan 0.0000 0.2349 3. Tengku Mohd Tengku Sembok

(a) scientific event = {chair, conference, conferences, meeting, programme, schedule, seminar, session, sponsor, symposium, track, workshop,. . .}. (b) citation ={reference, references, bibliography,. . .}

With the concept of aggregation starting from the bindery, each bindery consists of chapters (domains), and each chapter contains the sheets (words).

For example, hit counts (|Ωa|) of “Abdullah Mohd Zin”, “Abdul Razak Hamdan”, and “Tengku Mohd Tengku Sembok” are 7,740, 8,280, and 3,860, respectively. While|Ωa∩Ωb|between “Abdullah Mohd Zin” and “Abdul Razak Hamdan” is 736,|Ωa∩Ωc|between “Abdullah Mohd Zin” and “Tengku Mohd Tengku Sembok” is 441, and|Ωb∩Ωc|between “Abdullah Razak Hamdan” and “Tengku Mohd Tengku Sembok” is 281. Therefore, based on Eq. (5) we have three strength relations srlike Table1. From 100 snippets based on doubleton, we have:

1. 60 snippets contain the indirect relations and 12 snippets contain the direct relations for “Abdullah Mohd Zin” and “Abdul Razak Hamdan”,

2. 27 snippets contain the indirect relations and 43 snippets contain the direct relations for “Abdullah Mohd Zin” and “Tengku Mohd Tengku Sembok”, and 3. 66 snippets contain the indirect relations for “Abdul Razak Hamdan” and

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In this case,p(dr) andp(ir) for a pair of actors there are in Table1. While 100 snippets for each pair of actors are calculatedpD(w) for each word and its value

is directly transferred to the sheets in the appropriate domain, such as Table1.

Table 2..

We conduct an experiment using 65 social actors that have direct and indirect relations between them, orn(n−1) = 4,160 potential relations. Based on survey we obtain the relevant relation and this is a comparison of the results obtained through extraction from Web. Based on Table2, the recall and the precision give the impression that the activation of each aggregation of the strength relation as adequate.

5 Conclusion and Future Work

By studying the principle of methods for extraction the relation between social actors, we have an enhanced method for aggregation the relations to interpret more rich about social. Thus, this new method still needs further verification. Future work we study about combination between sheets and domain based on ontology.

References

1. Adamic, L.A., Adar, A.: Friends and neighbours on the web. Soc. Netw.25, 211– 230 (2003)

2. Nasution, M.K.M., Noah, S.A.: Superficial method for extracting social network for academics using web snippets. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 483–490. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16248-0 68

3. Cullota, A., Bekkerman, R., McCallum, A.: Extracting social networks and contact information from email and the Web. In: Proceedings of the 1st Conference on Email and Anti-Spam (CEAS) (2004)

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5. Heras, S., Atkinson, K., Botti, V., Grasso, F., Juli´an, V., McBurney, P.: Research opportunities for argumentation in social networks. Artif. Intell. Rev.39, 39–62 (2013)

6. Kautz, H., Selman, B., Shah, M.: ReferralWeb: combining social networks and collaborative filtering. Commun. ACM40(3), 63–65 (1997)

7. Finin, T., Ding, L., Zhou, L., Joshi, A.: Social networking on the semantic web. Learn. Organ.12(5), 418–435 (2005)

8. Nasution, M.K.M., Sitompul, O.S., Sinulingga, E.P., Noah, S.A.: An extracted social network mining. In: SAI Computing Conference. IEEE (2016)

9. Nasution, M.K.M.: Social network mining (SNM): a definition of relation between the resources and SNA. Int. J. Adv. Sci. Eng. Inf. Technol.6(6), 975–981 (2016) 10. Nasution, M.K.M.: Modelling and simulation of search engine. In: International

Conference on Computing and Applied Informatics (ICCAI). IOP (2016)

11. Nasution, M.K.M.: New method for extracting keyword for the social actor. In: Nguyen, N.T., Attachoo, B., Trawi´nski, B., Somboonviwat, K. (eds.) ACIIDS 2014. LNCS (LNAI), vol. 8397, pp. 83–92. Springer, Cham (2014). doi:10.1007/ 978-3-319-05476-6 9

12. Matsuo, Y., Mori, J., Hamasaki, M., Nishimura, T., Takeda, T., Hasida, K., Ishizuka, M.: POLYPHONET: an advanced social networks extraction system from the web. J. Web Semant. Sci. Serv. Agents World Wide Web5, 262–278 (2007) 13. Nasution, M.K.M.: New similarity. In: Annual Applied Science and Engineering

Conference (AASEC). IOP (2016)

14. Blei, D.M., Ng, A.Y., Jordan, M.J.: Latent Dirichlet allocation. J. Mach. Learn. Res.3, 993–1022 (2003)

15. McCallum, A., Corrada-Emmanual, A., Wang, X.: Topic and role discovery in social networks. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence, pp. 786–791 (2005)

16. Nasution, M.K.M., Mohd Noah, S.A.: Extraction of academic social network from online database. In: Mohd Noah, S.A. et al. (eds.) Proceeding of 2011 International Conference on Semantic Technology and Information Retrieval (STAIRS 2011), pp. 64–69. IEEE, Putrajaya (2011)

Enhancing Extraction Method for Aggregating Strength Relation Between Social Actors

Advances in Intelligent Systems and Computing

Volume 573

Radek Silhavy

Roman Senkerik

Zuzana Kominkova Oplatkova

Zdenka Prokopova

Petr Silhavy

Editors

Arti

ﬁ

cial Intelligence Trends

in Intelligent Systems

Proceedings of the 6th Computer

Science On-line Conference 2017

(CSOC2017), Vol 1

Preface

Organization

Program Committee

Program Committee Chairs

Program Committee Members

Organizing Committee Chair

Conference Organizer (Production)

Conference Website, Call for Papers

Contents

Enhancing Extraction Method for Aggregating

Strength Relation Between Social Actors

·

·

·

·

·

1

Introduction

2

Problem Definition

3

The Proposed Approach

4

Experiment

5

Conclusion and Future Work

References

_ﬁ