RESEARCH COLLABORATION OF ARTIFICIAL INTELLIGENCE LITERATURE OUTPUT: A

SCIENTOMETRIC ANALYSIS

Presented byS.JEYAPRIYA, 2nd MLIS,

BDU, Trichy

GuideDr. N.AMSAVENI

Assistant Professor, BDU, Trichy - 24

 

INTRODUCTIONScientometric studies are used to identify the pattern of publication, authorship, citations, growth pattern and other attributes and secondary journal coverage.

In the present study, we did the Scientometric study of the research performance on Artificial Intelligence, a significantly growing area in the knowledge-driven world.

Scientometrics

Scientometrics, according to Garfield, is “the study of the measurement of scientific and technological progress (Garfield, 1979). Its origin is in the quantitative study of science policy research, or the science of science, which focuses on a wide variety of quantitative measurements, or indicators, of science at large.

The 1970s saw the development of Scientometric as an operational activity - a response to the pressing demand for the ‘measuring of science’, especially in Russia and the USA. Since Vassily V. Nalimov coined the term ‘Scientometric’ in the 1960s.

SCOPE OF THE PRESENT STUDY

Different kinds of sources are published related the artificial intelligence research and its consequence on maintaining the information technology.

The database (WoS) covers (Bibliographic data) information relating to the titles, authors, author affiliation, methodology adopted and the continent and country coverage of the comprehensive publications during the study period (1981 to 2010).

It aims to evaluate the research activity of the Continent and Country wise output on Artificial Intelligence research.

OBJECTIVES OF THE STUDY

To identify year wise growth, RGR and exponential growth rate of artificial intelligence output.To analyse the authorship pattern, prolific authors and examine the extent of research Collaboration.To identify the citation scores and citation level and citation impact of the artificial intelligence research output.To apprehend and test of collaborative index, degrees of collaboration and h – index value;To find out the prolific authors performance, authorship pattern of research output on Artificial intelligence.To apply the Lotka’s law for measuring the n value for contributing authorsTo identify the weak and strong productivity of various continent and different countries.

ANALYSIS AND INTERPRETATION

This study is based on scientometric analysis of research trend of artificial intelligence literature output for the years 1981 - 2010. Scientometrics has typically been defined as the quantitative study of science and technology. Scientometrics includes all quantitative aspects of the science of science, communication in science and science policy (Wilson 2001).

Growth Trends inArtificial intelligence

0.40.5

0.91.1

1.51.4

21.7

1.5

2

4

4.6

3.9

4.8

4.4

4 4 4

3.63.4

3.83.7

4.34.34.5

5.2

4.64.9

5.45.5

0

1

2

3

4

5

6

1975 1980 1985 1990 1995 2000 2005 2010 2015

Years

Pe

rce

nta

ge

sFigure 4.1: Year wise Growth Trends in AI output

during 1981 to 2010

Relative Growth RateRGR values are the First decade 0.24;

Second decade 0.15 and Third decade 0.84.

and Doubling Time (Dt) value measured from this analysis is for

First decade 3.18 years,Second decade value is 12.26 and Third year value is 13.7 yrs.

Overall mean relative growth rate value is 0.16Overall mean Doubling Time value is 9.71 years.

CITATION IMPACT OF RESEARCH OUTPUT

Impact suggested by Nagpaul (1995), Garg and Pandhi (1999) have been used for inter comparison of quality by making unit of citation indicators such as CPP and TNP % (Garg et al. 2009). CPP is based on the publication output and the number of citations received by these papers, citation per paper for different countries and different institutions has been calculated.

Citation per paper has been calculated by using the following formula:

CITATION ANALYSIS Table 4.3: Distributions of Citation on artificial intelligence research

NC TNP Cum. TNP TNC Cum. TNC

0 3920 39200 0

1 to 5 4184 8104 10061 10061

6 to 10 1216 9320 9200 19261

More than 10 1475 10795 88547 107808

• Out of the total Indian publications of Artificial intelligence is 10,795 papers, with an average output of 359.83 papers per year. • Total citation score value is 1,07,808, average citation per article is 9.986.• Analysis of citation data indicates that, out of the 10,795 published papers, 3920 (36.31 %) papers did not have any citations. Remaining (6875) 63.69 % of articles had one or more citations. • 4184 (38.75%) papers received citations between one to five. 1216 (11.26 %) papers received citations between six to ten. Remaining 1475 (13.66 %) of articles were received more than ten citations.

Citation Scores and h – index of AI output

S.No Year TNP TNC NA CI CR h-index TCS CPP

1 1981 to 85 470 2553 710 7.765132 63

255331

2 1986 to 90 938 4908 1588 8.4512500 99

490826

3 1991 to 95 2354 31003 5099 10.8260569 166

3100365

4 1996 To 00 2048 16298 4947 12.1160162 101

1629825

5 2001 to 05 2221 26396 5874 13.1962994 127

2639643

6 2006 to 10 1019 12367 2887 5.6734418 92

1236723

7Total 10795

(359.83)

107808

(3593.6)

26306

(876.87)

66.93

(2.44)

298434

(9947.8)

765

(25.5)

107808

(3593.6)280 (9.33)

23

Total TNP is 10795, and its average value of individual years is 359.83.Total citation Scores value is 107808 and its average value is 3593.6Total Collaborative index value is 66.93, average CI value is 2.44.Total cited reference value is 298434 and its average value is 9947.8. Totally h index value is 765 and its average value is 25.5. 107808 TCS measured, and it calculated for individual year value is 3593.6 times.Total CPP value is 280 and its average value at individual year is 9.33.

Cumulative Authorship pattern during 1981-2010S.No 1981 to 85 1986 to 90 1991 to 95 1996 to 00 2001 to 05 2006 to 10 Total

1 184 405 1075 1230 1700 2347 6941

2 163 375 988 1060 1206 1839 5631

3 83 200 678 838 740 1301 3817

4 67 148 585 515 611 754 2680

5 65 136 489 344 380 488 1902

6 47 117 368 305 340 364 1541

7 37 85 366 217 269 346 1320

8 26 69 262 163 216 224 960

9 20 47 195 137 236 201 836

10 & > 9 39 93 138 176 224 678

Total 701 1588 5099 4947 5874 8088 26306

Single author contributed papers is 26.39 %double authors contributed papers is 21.41 %Triple authors contributing papers is 14.51% andQuadra authors contributing papers is 10.19 % respectively. It is found the collaborative author’s productivity is more than single author contribution. Single author productivity is only 26.39 percent whole multi author’s productivity is at 73.61 percents.

Prolific AuthorsS.

NoAuthor name R. O/P

Rank% of 26306

TLCS TLCS/t TGCS TGCS/t TLCR

1[Anonymous] 64

-0.24

0 0 0 0 0

2 Klopman G 33 1 0.13209 10.54 1257 71.01 95

3 Chau KW 25 2 0.1085 8.28 333 51.07 85

4Rosenkranz HS 21

30.08

90 5.1 579 35.76 74

5 Cortes U 16 4 0.0621 2.22 199 23.71 28

6 Emerenciano VP 14 5 0.0546 5.04 91 10.27 118

7 Sanchez-Marre M 14 5 0.0521 2.45 195 25.01 26

8Tadeusiewicz R 14

50.05

10 1.26 97 16.84 10

9 Ferreira MJP 13 6 0.0546 5.04 91 10.27 110

10 Nissan E 13 6 0.0513 2.14 21 3.27 27

The authors of Klopman G, Rosenkranz HS, Emerenciano VD, HSU YY and Chau KW were identified the most productive authors. At specifically identified the Active Author is Chau KW.

Degree of Collaboration

• The degree of collaboration is 0.74 during the study period 1981 to 2010. i.e., out of the total 10795 literature published, 74 percentages of them are published under joint venture. • During the year 1981 to 2010 the degree of collaboration was of a constant value of 0.73 and 0.71. • It is seen clearly from the above that the degree of collaboration in producing research output on Artificial intelligence research has shown an increasing trend during the study period since it is a new discipline. • Based on this study, the result of the degree of collaboration C = 0.74. i.e, 74 percent of collaborative authors’ articles published during the study periods.

Showing Lotka’s Law of Author ProductivityNo.of contribution

XNo.of contributors Y ∑X = log x ∑Y = log y ∑X*Y ∑X*X

1 9304 9304 0 9.138 0 0.000

2 3417 6834 0.693 8.829 6.118 0.480

3 1658 4974 1.098 8.512 9.346 1.206

4 378 1512 1.386 7.321 10.147 1.921

5 262 1310 1.609 7.178 11.549 2.589

6 178 1068 1.791 6.974 12.490 3.208

7 61 427 1.945 6.057 11.781 3.783

8 21 168 2.079 5.124 10.653 4.322

9 13 117 2.197 4.762 10.462 4.827

10 15 150 2.303 5.011 11.540 5.304

11 13 143 2.397 4.963 11.896 5.746

12 6 72 2.485 4.276 10.626 6.175

13 2 26 2.565 3.258 8.357 6.579

14 3 42 2.639 3.737 9.862 6.964

16 1 16 2.773 2.772 7.687 7.690

21 1 21 3.045 3.044 9.269 9.272

25 1 25 3.212 3.218 10.336 10.317

33 1 33 3.496 3.496 12.222 12.222

64 1 64 4.158 4.158 17.289 17.289

Total (sum) 15336 26306 41.871 101.828 4263.64 109.893

It explains the fact that the tabulated value shows that observed authors’ value is higher than the expected value. Thus the present analysis clearly invalidates Lotka's findings.

Continent Wise Research Output of Artificial intelligence

S.No Continent R. o/p Percentage TCSNo. of Contributed

countries

1 Europe 3846 35.63 41493 41 (43.61)

2 North America 3188 29.53 52194 5 (5.32)

3 Asia 1672 15.49 15797 27 (28.72)

4 Unknown 1409 13.05 8180 -

5 Australia 325 3.01 2138 3 (3.19)

6 South America 259 2.40 1845 7 (7.45)

7 Africa 96 0.89 510 11 (11.70)

Total 10795 100 122157 94 (100)

European and North American continent has highest number of publications and the largest TCS. They dominated in the first and second position.Asian continent, Australia continent, South America and Africa continents were stood in the position of third, fourth, fifth and sixth with regards to the artificial intelligence research out put.

Figure 4.3: Continent wise research output of Artificial intelligence

Europe41%

N. America34%

Asia18%

Australia3%

Africa1%

S. America2.4%

Europe N. America Asia Australia S. America Africa

FINDINGS   Distribution by different sources of research output on Artificial intelligence

publications when examined reveals a maximum contribution in the years of 2010, 2009 and 2006. The entire study period records a mean RGR of 0.16. The DT for publications at the cumulative level has been computed at 9.71 years. Analysis of citation data indicates that, out of the 10,795 published papers, 3920 (36.31 %) papers did not have any citation and the remaining 63.69 percents had one or more citations. It is seen from the authorship pattern analysis that collaborative author’s productivity is more than single author contribution. The author of “Klopman G” has published the highest number of articles have been 33 (0.13 %). At specifically identified the Active Author is Chau KW. The degree of collaboration is 0.74 during the study periods of 1981 to 2010. Continent wise analysis that the European continent has taken the first place. UK, USA, France and Spain are most productive countries.

CONCLUSION

 

Due to technological importance and expected economic activity, Artificial intelligence has been intensively investigated by scientometric methods.In this study, the current status of artificial intelligence has been presented. Initially frequency and percentile method have been evolved chronologically.