Enhancing Extraction Method for Aggregating Strength Relation Between Social Actors
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[21
21]
Enhancing Extraction Method
Metho d for Aggregating
Strength Relation Betw
Between Social
So cial Actors
Mahyuddin K.M.[0]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
[email protected]
Abstract.[00 ]There are differences in the resultant of extracting the relations between social actors based on two streams of approaches in principle.[0]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.[0]This paper serves
to reveal some formulations have the function for resolving this issue.
[0]
Based on the results of experiments conducted the expanded method is
the adequate.
Keywords:
Keywords:[00 ]Search engine· Search term
Singleton · Doubleton
1
·
Query
·
Social actor
·
Intro
Introduction
duction
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
W
e b [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.[4]This paper aimed to enhance the superficial
method for extracting social network from Web.
[5
5]
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].
c Springer International Publishing AG 2017
R. Silhavy et al. (eds.), Artificial Intelligence Trends in Intelligent Systems,
[0]
Advances in Intelligent Systems and Computing 573, DOI 10.1007/978-3-319-57261-1
31
[0]
Enhancing Extraction Method for Aggregating Strength Relation
313
Definition
De inition 1. L
e t A = {ai|i = , [ , }. is4na .1set ] of .social actors.[7]The social
network extraction (SNE) is A, V, R, E, γ1 , 2 with
γ the conditions as follows
1:1
A1 γ1 : A → V , nd
A2 γ2 : A × A → E .
a
where V =
∅, V = {vi |i = , [ , }. is7n1a .set ]of .vertices in G and E = {e j |j =
1, . [ , .} .is7ma set ]of edges in G, r G = V o
E
, as graph, and e j = rs in R, R is
a set of relations.
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,
q contains a name of social actor, for example q = ”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
De inition 2. A earch erm stk consists
t
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
De inition 3.[9]Ω is a set of web pages indexed search engine, if there are a table
relation of (ti , j ) such
ω that Ω = {(t, ω) ij }, here t i is wsearch terms and ωj is a
web pages, i = , .
,. 1j. = , ,
. 1The
I
cardinality
.
.
of, Ω is denoted
J
by |Ω |.
Definition
De inition 4. L
e t tx as search term, Ωx ⊆ Ω is a singleton space of event if
1 if tx is true at ω ∈ Ω ,
Ωx (tx )
=
0 otherwise.
Briefly we denote it as ω ⇒ tx is true.
Proposition
Proposition 1.[11 0]If tx ∈ , S is aSset of singleton search terms of search engine,
then there are vector space Ωx ⊆ Ω for generating hit count |Ωx |.
1[
Pro ]0f.
oF r tx ∈ , it means thatS tx ∈ ω and ω ∈ Ω, r S
Ω, very
o
eb ∈ e
page that is indexed by search engine contains at least one occurrence of t x ,
where based on Definition 3 Ω x = {(t x , x )ij }.
ω[3] Thus for all web pages be in
force {Ωx (tx )
} so that
= Ωx ⊆ Ω 1be the singleton search engine event of web
pages that contain an occurrence of tx ∈ ω x , and based on Definition 4, e ave w h
Ωx = {(ω x ⇒ tx )} as vector space. We call Ωx ⊆ Ω as singleton. The cardinality
of Ωx be |Ω x | is the hit count of singleton as follows
|Ωx| =
(Ωx (tx )
).
=
1
(1)
Ω
It proved.
w
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
De inition 5. L
e t tx is
earch erm.a
nippet
t s As et f ords,
i a .e.
s os xw=
{wi |i = ,
±.50}. 1A. list of. snippets
,
is L x = {s j |j = ,
.}x , m1.≤ Ωx.|,
then size of snippet |sx | = ±50 words and L x is a matrix Mj kthat consists of j
or ws and k columns where each column refers to one token of word w ∈ sj .
is
,
|
J
Lemma 1. If w is a token in L, then w statistically has the character.
Pro f. It is known that w ∈ sx , sx ∈
on Proposition 1 we have the hit count
obtain
1
mj k=
0
, e ave wLaswone
h token in L. ased
|Ωx |. et w ∈ MkLand if mj k∈ M j kwe
f w ∈ sj
i
otherwise,
J
Therefore, at column w we obtain |w| =
j=1 mj k as the weight of w, nd
proved that w has the character, i.e. the relative probability of w
p(w )
where |w|
Ωx | and
≤ |Ωx | =
|.
|w |
∈=[0, 1],
|Ωx |
B
a
(2)
0
Definition
De inition 6.[33 ]Suppose tx and ty is two search terms, Ωx ∩Ωy ⊆ Ω is a doubleton
space of event if
1 if tx and ty are true at ω ∈ Ω ,
Ω x (tx ∧ ty ) Ω y (tx =
∧ ty )
=
0 otherwise
where Ωx ⊆ Ω and Ω y ⊆ Ω .[3]Briefly we denote it as ω ⇒ tx ∧ ty is true.
Proposition
Proposition 2.[11 0]If tx , y ∈ t , D is a set ofD doubleton search term of search
engine, then there are vector space Ωx ∩ Ωy ⊆ Ω for generating hit count |Ωx ∩
Ωy |.
Pro f. Similar to Proposition 1, and based on Definitions 3 and 6,
hit count of doubleton as follows
|Ωx ∩ Ω y | =
(Ωx (tx ∧ ty ) ∩ Ωy (tx ∧ ty )) = 1).
Ω
It proved.[0]
e ave he w h
(3)
t
Enhancing Extraction Method for Aggregating Strength Relation
315
Lemma 2. If w is a token in LD as list of snippets based on doubleton, then w
statistically has the character in the doubleton.
Pro f. Similar to Lemma 1, and based on Definitions 5 and 6,
acter of w in the doubleton as follows
p D (w)
where |w|
e ave hew harh
t
|w |
= ∈ [0, 1],
|Ωx ∩ Ω y |
Ωx ∩ Ω
≤y| and |Ωx| ∩ Ωy | =
.
(4)
0
Fig. 1. Type of snippets based on co-occurrence (Google search engine)
As information of the relations between social actors, the doubleton naturally
be basic for refining the information about a social actor where one of search
terms be a keyword for other. Therefore, this is sufficient condition for eliminating the connatural trait of the singleton. The snippets of doubleton, however
naturally showed the different kind of information of relations. We conclude that
based on snippets of doubleton there are the direct relations and the indirect
relations, see Fig. 1. The snippet revealed an indirect relation with the presence
c
316
M.K.M. Nasution and O.S. Sitompul
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 Prop osed 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 generating relation between actors applied the similarity measurement [13].
Definition
De inition 7.[44 ]rs ∈ R is the strength relation between two social actors a, b ∈ A
if it meets the comparison among the different information of two actors (a
a and
b ) and the common information of them (a ∩ b ) in the similarity measurement.
Or sr = sim(a , b , a ∩ b ) in [0, 1], a ∩ b ≤ a and a ∩ b ≤ b .
Suppose we use Jaccard coefficient, we possess sr based on hit counts
sr =
where |Ωa ∩ Ω b|
|Ωa ∩ Ωb |
∈ [0, 1],
|Ωa| + |Ωb |
Ωa ∩−
Ωb |
|
Ωa | and
≤ |Ωa ∩ Ω|b |
Ωb |. ≤
(5)
|
Lemma 3.[44 ]If ir is a indirect relation between two social actors a, b ∈ A, then
ir statistically has the character in the doubleton.
Pro f. Suppose the indirect relations ir can 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 of ir as follows
p(ir)
where |ir|
|ir|
= ∈ [0, 1],
|Ωa ∩ Ωb |
Ωa ∩ Ω
≤b | and |Ωa| ∩ Ωb | =
.
(6)
0
Proposition
Proposition 3.[44 ]If sr is a strength relation between two social actors a, b ∈ A,
then the aggregation of sr consists of three binderies.
Pro f. 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 doubleton is as follows
|dr|
(7)
p(dr)
= ∈ [0, 1],
|Ωa ∩ Ωb |
where |dr|
Ωa ∩ Ω
≤b | and |Ωa| ∩ Ωb | =
.
0
Based n emma 3o and
L Eq. (7) we have two categories of relations based on
doubleton, i.e.[0]the indirect relation ir and the direct relation dr whereby their
Enhancing Extraction Method for Aggregating Strength Relation
317
characteristics are p(ir) nd p(dr), arespectively. However, 1 − p(ir) − p(dr) ≥ 0,
if p(ir) p(dr) +
− 1 = 0, we obtain
p(ur)
− (p(=
ir)
p(dr))1+
(8)
i.e. the character of relation has not be determined with certainty through the cooccurrence. Because p(ir), p(dr) nd p(ur) can
a 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,
sr dr = sr ∗ p(dr) ∈ [0, 1]
(9)
J2 A bindery of strength relations based on the indirect relations,
sr ir = 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
Proposition 4. If sr is a strength relation between two social actors a, b ∈ A,
then the aggregation of sr consists of sheets.
Pro f. Based on Proposition 3 and 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 relations sh, i.e.
sh = sr ∗ pD (w ) ∈ [0, 1]
(12)
318
M.K.M. Nasution and O.S. Sitompul
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. sr is the strength relation between two actors a, b ∈ A if and only
if there are aggregation.
3[
Pro ]f.
This is a direct consequence of Propositions 3 and 4 as the necessary
conditions, and Lemmas 1, 2 and 3 as the sufficient conditions, see Fig. 2.
generate (keyword)
INPUT : A set of actors
OUTPUT : aggregation of the strength relations
STEPS :
1. |Ωa|
t a query
← and search engine.
2. |Ωb |
tb query
← and search engine.
3. |Ωa ∩ Ωa |
ta ∧ tb←
query and search engine. A = {w1 , 2 , w .n }
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) nd sr ∗ p(ir)
a
7. Aggregating sr ∗ p(dr) nd sr ∗ p(ir)
a based on the summation of sheets per
domain.
8.[11]Measuring recall and precision of relations.
4
,
w
h
n
Exp eriment
eriment
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%). Therefore, 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 ) ave o
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, lecturer, professor, prof, researcher, reviewer, student, . }. .
.
(c) research group = {association, committee, group, institute, lab, laboratory, member, team, project, . }. .
.
Enhancing Extraction Method for Aggregating Strength Relation
319
20]
Table 1.[20
The strength relation, direct and indirect relations, and author-relationship
sr
1. Abdullah Mohd Zin
0.0482
0.0395
2.[20]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
1
&1
2& 2
3 &
activity
0.0023
0.0027
0.0000
article
0.0258
0.0613
0.0000
author
0.0103
0.1465
0.0000
authors
0.0156
0.0649
0.0001
journal
0.0371
0.1316
0.0001
journals
0.0400
0.0043
0.0000
paper
0.0289
0.0725
0.0000
patent
0.0000
0.0025
0.0000
presentation
0.0083
0.0299
0.0000
preceedings
0.0118
0.0497
0.0001
publication
0.0020
0.0115
0.0000
[20]
3
2. Indirect relations:
(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 ∩ Ω c | 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.[7] Therefore, based on Eq. (5) e ave
three strength relations sr like Table 1. 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
“Tengku Mohd Tengku Sembok”.
.
w h
320
M.K.M. Nasution and O.S. Sitompul
In this case, p(dr) nd p(ir) for
a a pair of actors there are in Table 1. While 100
snippets for each pair of actors are calculated pD (w) for each word and its value
is directly transferred to the sheets in the appropriate domain, such as Table 1.
Table 2. .
Aggregation
Recall
Precision
1 Author-relationship 61.76% 17.65%
2 Research group
55.88% 7.28%
3 Academic rule
4 Scientific event
61.94% 13.15%
61.76% 6.10%
5 Citation
50.01% 6.63%
We conduct an experiment using 65 social actors that have direct and indirect
relations between them, or n(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 Table 2, 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
25 211–
1. Adamic, L.A., Adar, A.:[0]Friends and neighbours on the web. Soc. Netw. 25,
230 (2003)
2. Nasution, M.K.M., Noah, S.A.:[0]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:10.1007/978-3-642-16248-068
3. Cullota, A., Bekkerman, R., McCallum, A.:[0]Extracting social networks and contact
information from email and the Web. In:[1]Proceedings of the 1st Conference on
Email and Anti-Spam (CEAS) (2004)
4. McCallum, A., Corrada-Emmanual, A., Wang, X.:[0] The author-recipient-topic
model for topic and role discovery in social networks, with application to Enron
and academic email. In:[0]Proceedings of the Workshop and Link Analysis, Counterrorism, and Security (in Conjunction with:[0]SIAM International Conference on
Data Mining), pp. 33–44 (2005)
Enhancing Extraction Method for Aggregating Strength Relation
321
5. Heras, S., Atkinson, K., Botti, V., Grasso, F., Juli´an, V., McBurney, P.:[0]Research
opportunities for argumentation in social networks. Artif. Intell. Rev. 39,
39 39–62
(2013)
6. Kautz, H., Selman, B., Shah, M.: ReferralWeb:[0]combining social networks and
collaborative filtering. Commun.[0]ACM 40 (3), 63–65 (1997)
7. Finin, T., Ding, L., Zhou, L., Joshi, A.:[0]Social networking on the semantic web.
Learn. Organ.[0]12 (5), 418–435 (2005)
8. Nasution, M.K.M., Sitompul, O.S., Sinulingga, E.P., Noah, S.A.:[0]An extracted
social network mining. In: SAI Computing Conference. IEEE (2016)
9. Nasution, M.K.M.:[0]Social network mining (SNM):[0]a definition of relation between
the resources and SNA. Int. J. Adv. Sci. Eng. Inf. Technol.[1]6 (6), 975–981 (2016)
10. Nasution, M.K.M.:[0]Modelling and simulation of search engine. In:[0]International
Conference on Computing and Applied Informatics (ICCAI). IOP (2016)
11. Nasution, M.K.M.:[0]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:[0]an advanced social networks extraction system from
the web. J. Web Semant. Sci. Serv.[0]Agents World Wide Web 5 , 262–278 (2007)
13. Nasution, M.K.M.: New similarity. In:[0]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.:[0]Topic and role discovery in
social networks. In:[0]Proceedings of the 19th International Joint Conference on
Artificial Intelligence, pp. 786–791 (2005)
16. Nasution, M.K.M., Mohd Noah, S.A.:[0]Extraction of academic social network from
[0]
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.[1]IEEE, Putrajaya (2011)
17. Nasution, M.K.M., Noah, S.A.:[0]A social network extraction perspective. In: CAMP
2012. IEEE (2012)
31 - Enhancing Extraction Method for Aggregating Strength Relation Between
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13.1% 39 matches
[1]
https://www.springerprofessional.de/en/e...relation-be/12204946
11.7% 37 matches
[2]
"3028-3639-1-RV.pdf" dated 2017-10-30
5.1% 18 matches
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"CR-INT110-Semantic interpretation ...ot; dated 2017-10-09
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"CR-INT136-Social network extractio...ot; dated 2017-10-09
3.8% 15 matches
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"CR-INT137-Enhancing to method for ...ot; dated 2017-10-09
3.0% 12 matches
[6]
https://www.researchgate.net/publication...etween_Social_Actors
3.6% 8 matches
[7]
"CR-INT135-Information Retrieval on...ot; dated 2017-10-09
2.9% 13 matches
[8]
https://link.springer.com/chapter/10.1007/978-3-319-67621-0_20
3.4% 13 matches
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https://archive.org/stream/arxiv-1212.4702/1212.4702_djvu.txt
2.7% 9 matches
[10]
https://arxiv.org/pdf/1303.3964.pdf
2.1% 7 matches
[11]
https://rd.springer.com/content/pdf/10.1007/978-3-319-05476-6_9.pdf
1.9% 6 matches
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www.emeraldinsight.com/doi/citedby/10.1108/09696470510611384
1.7% 5 matches
www.springer.com/us/book/9783319572604
[13] 1.7% 5 matches
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"3472-4529-1-SM.pdf" dated 2017-10-30
1.4% 5 matches
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dblp.uni-trier.de/db/conf/csoc/csoc2017-1
1.6% 4 matches
[17]
www.academia.edu/3144197/Simple_Search_Engine_Model_Adaptive_Properties_for_Doubleton
0.8% 3 matches
[18]
https://www.researchgate.net/profile/Mahyuddin_Nasution
1.1% 3 matches
[19]
dblp.uni-trier.de/pers/n/Nasution:Mahyuddin_K=_M=
1.3% 4 matches
[20]
www.yasni.de/mariati mohd zin/person information
1.0% 4 matches
[21]
it.usu.ac.id/index.php/penelitian-pengabdian/publikasi/49-daftar-publikasi-tahun-2017
1.1% 3 matches
[22]
https://www.researchgate.net/profile/Mah...citationCount&page=1
1.1% 3 matches
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"2668-3031-1-RV.pdf" dated 2017-09-26
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"ICIC_2017_paper_144.pdf" dated 2017-09-11
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"PTUPT_Sistem_Peringatan_Dini_Kebakar.pdf.pdf" dated 2017-09-09
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https://www.researchgate.net/publication...ed_on_Indonesian_NLP
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[21
21]
Enhancing Extraction Method
Metho d for Aggregating
Strength Relation Betw
Between Social
So cial Actors
Mahyuddin K.M.[0]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
[email protected]
Abstract.[00 ]There are differences in the resultant of extracting the relations between social actors based on two streams of approaches in principle.[0]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.[0]This paper serves
to reveal some formulations have the function for resolving this issue.
[0]
Based on the results of experiments conducted the expanded method is
the adequate.
Keywords:
Keywords:[00 ]Search engine· Search term
Singleton · Doubleton
1
·
Query
·
Social actor
·
Intro
Introduction
duction
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
W
e b [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.[4]This paper aimed to enhance the superficial
method for extracting social network from Web.
[5
5]
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].
c Springer International Publishing AG 2017
R. Silhavy et al. (eds.), Artificial Intelligence Trends in Intelligent Systems,
[0]
Advances in Intelligent Systems and Computing 573, DOI 10.1007/978-3-319-57261-1
31
[0]
Enhancing Extraction Method for Aggregating Strength Relation
313
Definition
De inition 1. L
e t A = {ai|i = , [ , }. is4na .1set ] of .social actors.[7]The social
network extraction (SNE) is A, V, R, E, γ1 , 2 with
γ the conditions as follows
1:1
A1 γ1 : A → V , nd
A2 γ2 : A × A → E .
a
where V =
∅, V = {vi |i = , [ , }. is7n1a .set ]of .vertices in G and E = {e j |j =
1, . [ , .} .is7ma set ]of edges in G, r G = V o
E
, as graph, and e j = rs in R, R is
a set of relations.
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,
q contains a name of social actor, for example q = ”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
De inition 2. A earch erm stk consists
t
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
De inition 3.[9]Ω is a set of web pages indexed search engine, if there are a table
relation of (ti , j ) such
ω that Ω = {(t, ω) ij }, here t i is wsearch terms and ωj is a
web pages, i = , .
,. 1j. = , ,
. 1The
I
cardinality
.
.
of, Ω is denoted
J
by |Ω |.
Definition
De inition 4. L
e t tx as search term, Ωx ⊆ Ω is a singleton space of event if
1 if tx is true at ω ∈ Ω ,
Ωx (tx )
=
0 otherwise.
Briefly we denote it as ω ⇒ tx is true.
Proposition
Proposition 1.[11 0]If tx ∈ , S is aSset of singleton search terms of search engine,
then there are vector space Ωx ⊆ Ω for generating hit count |Ωx |.
1[
Pro ]0f.
oF r tx ∈ , it means thatS tx ∈ ω and ω ∈ Ω, r S
Ω, very
o
eb ∈ e
page that is indexed by search engine contains at least one occurrence of t x ,
where based on Definition 3 Ω x = {(t x , x )ij }.
ω[3] Thus for all web pages be in
force {Ωx (tx )
} so that
= Ωx ⊆ Ω 1be the singleton search engine event of web
pages that contain an occurrence of tx ∈ ω x , and based on Definition 4, e ave w h
Ωx = {(ω x ⇒ tx )} as vector space. We call Ωx ⊆ Ω as singleton. The cardinality
of Ωx be |Ω x | is the hit count of singleton as follows
|Ωx| =
(Ωx (tx )
).
=
1
(1)
Ω
It proved.
w
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
De inition 5. L
e t tx is
earch erm.a
nippet
t s As et f ords,
i a .e.
s os xw=
{wi |i = ,
±.50}. 1A. list of. snippets
,
is L x = {s j |j = ,
.}x , m1.≤ Ωx.|,
then size of snippet |sx | = ±50 words and L x is a matrix Mj kthat consists of j
or ws and k columns where each column refers to one token of word w ∈ sj .
is
,
|
J
Lemma 1. If w is a token in L, then w statistically has the character.
Pro f. It is known that w ∈ sx , sx ∈
on Proposition 1 we have the hit count
obtain
1
mj k=
0
, e ave wLaswone
h token in L. ased
|Ωx |. et w ∈ MkLand if mj k∈ M j kwe
f w ∈ sj
i
otherwise,
J
Therefore, at column w we obtain |w| =
j=1 mj k as the weight of w, nd
proved that w has the character, i.e. the relative probability of w
p(w )
where |w|
Ωx | and
≤ |Ωx | =
|.
|w |
∈=[0, 1],
|Ωx |
B
a
(2)
0
Definition
De inition 6.[33 ]Suppose tx and ty is two search terms, Ωx ∩Ωy ⊆ Ω is a doubleton
space of event if
1 if tx and ty are true at ω ∈ Ω ,
Ω x (tx ∧ ty ) Ω y (tx =
∧ ty )
=
0 otherwise
where Ωx ⊆ Ω and Ω y ⊆ Ω .[3]Briefly we denote it as ω ⇒ tx ∧ ty is true.
Proposition
Proposition 2.[11 0]If tx , y ∈ t , D is a set ofD doubleton search term of search
engine, then there are vector space Ωx ∩ Ωy ⊆ Ω for generating hit count |Ωx ∩
Ωy |.
Pro f. Similar to Proposition 1, and based on Definitions 3 and 6,
hit count of doubleton as follows
|Ωx ∩ Ω y | =
(Ωx (tx ∧ ty ) ∩ Ωy (tx ∧ ty )) = 1).
Ω
It proved.[0]
e ave he w h
(3)
t
Enhancing Extraction Method for Aggregating Strength Relation
315
Lemma 2. If w is a token in LD as list of snippets based on doubleton, then w
statistically has the character in the doubleton.
Pro f. Similar to Lemma 1, and based on Definitions 5 and 6,
acter of w in the doubleton as follows
p D (w)
where |w|
e ave hew harh
t
|w |
= ∈ [0, 1],
|Ωx ∩ Ω y |
Ωx ∩ Ω
≤y| and |Ωx| ∩ Ωy | =
.
(4)
0
Fig. 1. Type of snippets based on co-occurrence (Google search engine)
As information of the relations between social actors, the doubleton naturally
be basic for refining the information about a social actor where one of search
terms be a keyword for other. Therefore, this is sufficient condition for eliminating the connatural trait of the singleton. The snippets of doubleton, however
naturally showed the different kind of information of relations. We conclude that
based on snippets of doubleton there are the direct relations and the indirect
relations, see Fig. 1. The snippet revealed an indirect relation with the presence
c
316
M.K.M. Nasution and O.S. Sitompul
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 Prop osed 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 generating relation between actors applied the similarity measurement [13].
Definition
De inition 7.[44 ]rs ∈ R is the strength relation between two social actors a, b ∈ A
if it meets the comparison among the different information of two actors (a
a and
b ) and the common information of them (a ∩ b ) in the similarity measurement.
Or sr = sim(a , b , a ∩ b ) in [0, 1], a ∩ b ≤ a and a ∩ b ≤ b .
Suppose we use Jaccard coefficient, we possess sr based on hit counts
sr =
where |Ωa ∩ Ω b|
|Ωa ∩ Ωb |
∈ [0, 1],
|Ωa| + |Ωb |
Ωa ∩−
Ωb |
|
Ωa | and
≤ |Ωa ∩ Ω|b |
Ωb |. ≤
(5)
|
Lemma 3.[44 ]If ir is a indirect relation between two social actors a, b ∈ A, then
ir statistically has the character in the doubleton.
Pro f. Suppose the indirect relations ir can 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 of ir as follows
p(ir)
where |ir|
|ir|
= ∈ [0, 1],
|Ωa ∩ Ωb |
Ωa ∩ Ω
≤b | and |Ωa| ∩ Ωb | =
.
(6)
0
Proposition
Proposition 3.[44 ]If sr is a strength relation between two social actors a, b ∈ A,
then the aggregation of sr consists of three binderies.
Pro f. 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 doubleton is as follows
|dr|
(7)
p(dr)
= ∈ [0, 1],
|Ωa ∩ Ωb |
where |dr|
Ωa ∩ Ω
≤b | and |Ωa| ∩ Ωb | =
.
0
Based n emma 3o and
L Eq. (7) we have two categories of relations based on
doubleton, i.e.[0]the indirect relation ir and the direct relation dr whereby their
Enhancing Extraction Method for Aggregating Strength Relation
317
characteristics are p(ir) nd p(dr), arespectively. However, 1 − p(ir) − p(dr) ≥ 0,
if p(ir) p(dr) +
− 1 = 0, we obtain
p(ur)
− (p(=
ir)
p(dr))1+
(8)
i.e. the character of relation has not be determined with certainty through the cooccurrence. Because p(ir), p(dr) nd p(ur) can
a 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,
sr dr = sr ∗ p(dr) ∈ [0, 1]
(9)
J2 A bindery of strength relations based on the indirect relations,
sr ir = 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
Proposition 4. If sr is a strength relation between two social actors a, b ∈ A,
then the aggregation of sr consists of sheets.
Pro f. Based on Proposition 3 and 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 relations sh, i.e.
sh = sr ∗ pD (w ) ∈ [0, 1]
(12)
318
M.K.M. Nasution and O.S. Sitompul
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. sr is the strength relation between two actors a, b ∈ A if and only
if there are aggregation.
3[
Pro ]f.
This is a direct consequence of Propositions 3 and 4 as the necessary
conditions, and Lemmas 1, 2 and 3 as the sufficient conditions, see Fig. 2.
generate (keyword)
INPUT : A set of actors
OUTPUT : aggregation of the strength relations
STEPS :
1. |Ωa|
t a query
← and search engine.
2. |Ωb |
tb query
← and search engine.
3. |Ωa ∩ Ωa |
ta ∧ tb←
query and search engine. A = {w1 , 2 , w .n }
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) nd sr ∗ p(ir)
a
7. Aggregating sr ∗ p(dr) nd sr ∗ p(ir)
a based on the summation of sheets per
domain.
8.[11]Measuring recall and precision of relations.
4
,
w
h
n
Exp eriment
eriment
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%). Therefore, 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 ) ave o
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, lecturer, professor, prof, researcher, reviewer, student, . }. .
.
(c) research group = {association, committee, group, institute, lab, laboratory, member, team, project, . }. .
.
Enhancing Extraction Method for Aggregating Strength Relation
319
20]
Table 1.[20
The strength relation, direct and indirect relations, and author-relationship
sr
1. Abdullah Mohd Zin
0.0482
0.0395
2.[20]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
1
&1
2& 2
3 &
activity
0.0023
0.0027
0.0000
article
0.0258
0.0613
0.0000
author
0.0103
0.1465
0.0000
authors
0.0156
0.0649
0.0001
journal
0.0371
0.1316
0.0001
journals
0.0400
0.0043
0.0000
paper
0.0289
0.0725
0.0000
patent
0.0000
0.0025
0.0000
presentation
0.0083
0.0299
0.0000
preceedings
0.0118
0.0497
0.0001
publication
0.0020
0.0115
0.0000
[20]
3
2. Indirect relations:
(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 ∩ Ω c | 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.[7] Therefore, based on Eq. (5) e ave
three strength relations sr like Table 1. 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
“Tengku Mohd Tengku Sembok”.
.
w h
320
M.K.M. Nasution and O.S. Sitompul
In this case, p(dr) nd p(ir) for
a a pair of actors there are in Table 1. While 100
snippets for each pair of actors are calculated pD (w) for each word and its value
is directly transferred to the sheets in the appropriate domain, such as Table 1.
Table 2. .
Aggregation
Recall
Precision
1 Author-relationship 61.76% 17.65%
2 Research group
55.88% 7.28%
3 Academic rule
4 Scientific event
61.94% 13.15%
61.76% 6.10%
5 Citation
50.01% 6.63%
We conduct an experiment using 65 social actors that have direct and indirect
relations between them, or n(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 Table 2, 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
25 211–
1. Adamic, L.A., Adar, A.:[0]Friends and neighbours on the web. Soc. Netw. 25,
230 (2003)
2. Nasution, M.K.M., Noah, S.A.:[0]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:10.1007/978-3-642-16248-068
3. Cullota, A., Bekkerman, R., McCallum, A.:[0]Extracting social networks and contact
information from email and the Web. In:[1]Proceedings of the 1st Conference on
Email and Anti-Spam (CEAS) (2004)
4. McCallum, A., Corrada-Emmanual, A., Wang, X.:[0] The author-recipient-topic
model for topic and role discovery in social networks, with application to Enron
and academic email. In:[0]Proceedings of the Workshop and Link Analysis, Counterrorism, and Security (in Conjunction with:[0]SIAM International Conference on
Data Mining), pp. 33–44 (2005)
Enhancing Extraction Method for Aggregating Strength Relation
321
5. Heras, S., Atkinson, K., Botti, V., Grasso, F., Juli´an, V., McBurney, P.:[0]Research
opportunities for argumentation in social networks. Artif. Intell. Rev. 39,
39 39–62
(2013)
6. Kautz, H., Selman, B., Shah, M.: ReferralWeb:[0]combining social networks and
collaborative filtering. Commun.[0]ACM 40 (3), 63–65 (1997)
7. Finin, T., Ding, L., Zhou, L., Joshi, A.:[0]Social networking on the semantic web.
Learn. Organ.[0]12 (5), 418–435 (2005)
8. Nasution, M.K.M., Sitompul, O.S., Sinulingga, E.P., Noah, S.A.:[0]An extracted
social network mining. In: SAI Computing Conference. IEEE (2016)
9. Nasution, M.K.M.:[0]Social network mining (SNM):[0]a definition of relation between
the resources and SNA. Int. J. Adv. Sci. Eng. Inf. Technol.[1]6 (6), 975–981 (2016)
10. Nasution, M.K.M.:[0]Modelling and simulation of search engine. In:[0]International
Conference on Computing and Applied Informatics (ICCAI). IOP (2016)
11. Nasution, M.K.M.:[0]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:[0]an advanced social networks extraction system from
the web. J. Web Semant. Sci. Serv.[0]Agents World Wide Web 5 , 262–278 (2007)
13. Nasution, M.K.M.: New similarity. In:[0]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.:[0]Topic and role discovery in
social networks. In:[0]Proceedings of the 19th International Joint Conference on
Artificial Intelligence, pp. 786–791 (2005)
16. Nasution, M.K.M., Mohd Noah, S.A.:[0]Extraction of academic social network from
[0]
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.[1]IEEE, Putrajaya (2011)
17. Nasution, M.K.M., Noah, S.A.:[0]A social network extraction perspective. In: CAMP
2012. IEEE (2012)