1. RESEARCH COLLABORATION OF ARTIFICIAL INTELLIGENCE LITERATURE OUTPUT: A SCIENTOMETRIC ANALYSIS Presented by S.JEYAPRIYA, 2 nd MLIS, BDU, Trichy Guide Dr. N.AMSAVENI Assistant Professor, BDU, Trichy - 24

2. INTRODUCTION Scientometric 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.

3. 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.

4. 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.

5. 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 authors  To identify the weak and strong productivity of various continent and different countries.

6. 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).

7. Figure 4.1: Year wise Growth Trends in AI output during 1981 to 2010

8. Relative Growth Rate RGR 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 0.16 Overall mean Doubling Time value is 9.71 years.

9. 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:

10. CITATION ANALYSIS Table 4.3: Distributions of Citation on artificial intelligence research NCTNPCum. TNPTNCCum. TNC 03920 00 1 to 5 41848104 10061 6 to 10 12169320 920019261 More than 10147510795 88547107808 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.

11. Citation Scores and h – index of AI output S.NoYearTNPTNCNACICRh-indexTCSCPP 11981 to 85 47025537107.76 513263 2553 31 21986 to 90 938490815888.45 1250099 4908 26 31991 to 95 2354 31003509910.82 60569166 31003 65 4 1996 To 00 204816298494712.11 60162101 16298 25 5 2001 to 05 222126396587413.19 62994127 26396 43 6 2006 to 10 10191236728875.67 3441892 12367 23 7 Total 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 3593.6 Total 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.

12. Cumulative Authorship pattern during 1981-2010 S.No1981 to 851986 to 901991 to 951996 to 002001 to 052006 to 10Total 1184405107512301700 23476941 216337598810601206 18395631 383 200 678838740 13013817 467 148585515611 7542680 565 136489344380 4881902 647 117368305340 3641541 73785366217269 3461320 82669262163216 224960 92047195137236 201836 10 & >93993138176 224678 Total7011588509949475874 808826306 Single author contributed papers is 26.39 % double authors contributed papers is 21.41 % Triple authors contributing papers is 14.51% and Quadra 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.

13. Prolific Authors S. No Author nameR. O/P Rank % of 26306 TLCSTLCS/tTGCSTGCS/tTLCR 1 [Anonymous]64 - 0.24 00000 2 Klopman G33 1 0.13 20910.54125771.0195 3 Chau KW25 2 0.10 858.2833351.0785 4 Rosenkranz HS21 3 0.08 905.157935.7674 5 Cortes U16 4 0.06 212.2219923.7128 6 Emerenciano VP14 5 0.05 465.049110.27118 7 Sanchez-Marre M14 5 0.05 212.4519525.0126 8 Tadeusiewicz R14 5 0.05 101.269716.8410 9 Ferreira MJP13 6 0.05 465.049110.27110 10 Nissan E13 6 0.05 132.14213.2727 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.

14. 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.

15. Showing Lotka’s Law of Author Productivity No.of contribution X No.of contributorsY∑X = log x∑Y = log y∑X*Y∑X*X 19304 09.13800.000 2341768340.6938.8296.1180.480 3165849741.0988.5129.3461.206 437815121.3867.32110.1471.921 526213101.6097.17811.5492.589 617810681.7916.97412.4903.208 7614271.9456.05711.7813.783 8211682.0795.12410.6534.322 9131172.1974.76210.4624.827 10151502.3035.01111.5405.304 11131432.3974.96311.8965.746 126722.4854.27610.6266.175 132262.5653.2588.3576.579 143422.6393.7379.8626.964 161 2.7732.7727.6877.690 211 3.0453.0449.2699.272 251 3.2123.21810.33610.317 331 3.496 12.222 641 4.158 17.289 Total (sum)153362630641.871101.8284263.64109.893 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.

16. Continent Wise Research Output of Artificial intelligence S.NoContinentR. o/pPercentageTCS No. of Contributed countries 1Europe3846 35.63 4149341 (43.61) 2North America3188 29.53 521945 (5.32) 3Asia1672 15.49 1579727 (28.72) 4Unknown1409 13.05 8180- 5Australia325 3.01 21383 (3.19) 6South America259 2.40 18457 (7.45) 7Africa96 0.89 51011 (11.70) Total1079510012215794 (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.

17. Figure 4.3: Continent wise research output of Artificial intelligence

18. 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.

19. 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.