PREDICTING TECHNOLOGY CONVERGENCE BETWEEN KNOWLEDGE MANAGEMENT AND ARTIFICIAL INTELLIGENCE FIELDS

Yıl 2024, Sayı: 60, 35 - 52, 17.01.2024

Aylin Sabancı Bayramoğlu Serkan Dolma

https://doi.org/10.30794/pausbed.1321966

Öz

The aim of this study is to examine the technological convergence between the fields of knowledge management and
artificial intelligence. For this purpose, patent data from 2015 to 2021 was utilized. The current relationship between these
fields was analyzed using network analysis methods. The link prediction method identified potential areas for technological
connections. The themes of the predicted technology convergence were determined using community detection and topic
modeling methods. The findings of this study indicate that methods and techniques like machine learning, neural networks,
and natural language processing are being utilized in the development of new technologies. In this context, semantic
web concepts, such as knowledge graphs and ontologies, are expected to come to the forefront in the future for better
management, interpretation, and effective utilization of knowledge. Considering these concepts, it is evident that artificial
intelligence methods and techniques could be widely applied in health for drug and treatment recommendation systems, and
in industry for the management and error prediction of hardware systems. Knowledge management and artificial intelligence
technologies can also be leveraged in developing intelligent question-answering systems and educational applications.

Anahtar Kelimeler

Technology convergence, Network analysis, Link prediction, Topic modelling

BİLGİ YÖNETİMİ VE YAPAY ZEKÂ ALANLARI ARASINDAKİ TEKNOLOJİ YAKINSAMASININ ÖNGÖRÜLMESİ

Öz

Bu çalışmanın amacı, bilgi yönetimi ve yapay zekâ alanları arasındaki teknoloji yakınsamasını incelemektir. Bu amaç
doğrultusunda 2015-2021 yıllarını kapsayan patent verileri kullanılmıştır. Ağ analizi yöntemiyle alanların mevcut ilişkisi analiz
edilmiştir. Bağlantı tahmin yöntemi kullanılarak alanlar arasında potansiyel olarak bağlantı oluşması beklenen teknoloji alanları
belirlenmiştir. Öngörülen teknoloji yakınsamasının temaları topluluk tespiti ve konu modelleme yöntemleri kullanılarak tespit
edilmiştir. Bu çalışmada elde edilen bulgular makine öğrenmesi, sinir ağları ve doğal dil işleme gibi yöntem ve tekniklerin yeni
teknolojilerin geliştirilmesinde kullanıldığını göstermektedir. Bu bağlamda önümüzdeki dönemde bilginin daha iyi yönetilmesi,
anlamlı hale getirilmesi ve etkili bir şekilde kullanılması için bilgi grafiği ve ontoloji gibi anlamsal web kavramları ön plana
çıkmaktadır. Bu kavramlar göz önünde bulundurulduğunda yapay zekâ yöntem ve tekniklerinin sağlık alanında ilaç ve tedavi
öneri sistemlerinde, endüstride donanımsal sistemlerin yönetilmesi ve hata öngörülmesinde yaygın olarak kullanılabileceğini
göstermektedir. Bilgi yönetimi ve yapay zekâ teknolojileri ayrıca zeki soru-cevap sistemlerinin ve eğitim uygulamalarının
geliştirilmesinde kullanılabilir.

Anahtar Kelimeler

Teknoloji yakınsaması, Ağ analizi, Bağlantı tahmini, Konu modelleme

Kaynakça

• Abu-Salih, B., Al-Qurishi, M., Alweshah, M., Al-Smadi, M., Alfayez, R., & Saadeh, H. (2023). Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities. Journal of Big Data, 10(1), 81.
• Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social networks, 25(3), 211-230.
• Akhavan, P., Ebrahim, N. A., Fetrati, M. A., & Pezeshkan, A. (2016). Major trends in knowledge management research: a bibliometric study. Scientometrics, 107(3), 1249-1264.
• Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6(1).
• Al-Taie, M. Z., and Kadry, S. (2017). Python for Graph and Network Analysis, Cham: Springer International Publishing.
• Avdeenko, T. V., Makarova, E. S., & Klavsuts, I. L. (2016, October). Artificial intelligence support of knowledge transformation in knowledge management systems. In 2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE) (Vol. 3, pp. 195-201). IEEE.
• Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. science, 286(5439), 509-512.
• Begler, A., & Gavrilova, T. (2018). Artificial intelligence methods for knowledge management systems (No. 15106).
• Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84.
• Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing social networks. Sage.
• Cao, Q. (2018). Semantic Technologies for the Modeling of Condition Monitoring Knowledge in the Framework of Industry 4.0. In EKAW (Doctoral Consortium).
• Chi, Y., Yu, C., Qi, X., & Xu, H. (2018). Knowledge management in healthcare sustainability: a smart healthy diet assistant in traditional Chinese medicine culture. Sustainability, 10(11), 4197.
• Curran, C. S., & Leker, J. (2011). Patent indicators for monitoring convergence–examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256-273.
• Devadas, T. J., & Ganesan, R. (2012). Intelligent Agent-Based Knowledge Management and Knowledge Discovery. International Journal of Advanced Research in Computer Science, 3(2).
• Desmarais, B. A., & Cranmer, S. J. (2012). Statistical inference for valued-edge networks: The generalized exponential random graph model. PloS one, 7(1), e30136.
• Duan, Y., & Guan, Q. (2021). Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model. Scientometrics, 126(5), 3749-3773.
• Feng, S., & Law, N. (2021). Mapping Artificial Intelligence in Education Research: a Network‐based Keyword Analysis. International Journal of Artificial Intelligence in Education, 31(2), 277-303.
• Feng, S., An, H., Li, H., Qi, Y., Wang, Z., Guan, Q., ... & Qi, Y. (2020). The technology convergence of electric vehicles: Exploring promising and potential technology convergence relationships and topics. Journal of Cleaner Production, 260, 120992.
• Freeman, W. J. (1979). Nonlinear dynamics of paleocortex manifested in the olfactory EEG. Biological Cybernetics, 35(1), 21-37.
• Gaviria-Marin, M., Merigó, J. M., & Baier-Fuentes, H. (2019). Knowledge management: A global examination based on bibliometric analysis. Technological Forecasting and Social Change, 140, 194-220.
• Grootendorst, M. (2020). BERTopic: Leveraging BERT and c-TF-IDF to CreateEasily Interpretable Topics. Zenodo. doi:10.5281/zenodo.4381785.
• Guan, Q., An, H., Gao, X., Huang, S., & Li, H. (2016). Estimating potential trade links in the international crude oil trade: A link prediction approach. Energy, 102, 406-415.
• Gulavani, S. S., & Joshi, M. (2011). Knowledge Management using Artificial Intelligence Techniques. In Proceedings of the 5th National Conference; INDIACom-2011. Computing for Nation Development, March (pp. 10-11).
• Gupta, B., Iyer, L. S., & Aronson, J. E. (2000). Knowledge management: practices and challenges. Industrial management & data systems.
• Güneş, İ., Gündüz-Öğüdücü, Ş., & Çataltepe, Z. (2016). Link prediction using time series of neighborhood-based node similarity scores. Data Mining and Knowledge Discovery, 30(1), 147-180.
• Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36.
• He, C., Shi, F., & Tan, R. (2022). A synthetical analysis method of measuring technology convergence. Expert Systems with Applications, 118262.
• Houari, N., & Far, B. H. (2004, August). Application of intelligent agent technology for knowledge management integration. In Proceedings of the Third IEEE International Conference on Cognitive Informatics, 2004. (pp. 240-249). IEEE.
• Huang, L., Yu, C., Chi, Y., Qi, X., & Xu, H. (2019, February). Towards smart healthcare management based on knowledge graph technology. In Proceedings of the 2019 8th International Conference on Software and Computer Applications (pp. 330-337).
• Iakovidou, N., Symeonidis, P., & Manolopoulos, Y. (2010, November). Multiway spectral clustering link prediction in protein-protein interaction networks. In Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine (pp. 1-4). IEEE.
• Jaccard, P. (1901). Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaudoise Sci Nat, 37, 547-579.
• Jalili, M., Orouskhani, Y., Asgari, M., Alipourfard, N., & Perc, M. (2017). Link prediction in multiplex online social networks. Royal Society open science, 4(2), 160863.
• Jallow, H., Renukappa, S., & Suresh, S. (2020, December). Knowledge management and artificial intelligence (AI). In ECKM 2020 21st European Conference on Knowledge Management (p. 363). Academic Conferences International Limited.
• Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99.
• Jeong, S., Kim, J. C., & Choi, J. Y. (2015). Technology convergence: What developmental stage are we in?. Scientometrics, 104, 841-871.
• Jung, S., Kim, K., & Lee, C. (2021). The nature of ICT in technology convergence: A knowledge-based network analysis. Plos one, 16(7), e0254424.
• Kim, J., Kim, S., & Lee, C. (2019). Anticipating technological convergence: Link prediction using Wikipedia hyperlinks. Technovation, 79, 25-34.
• Knobloch, J., Kaltenbach, J., & Bruegge, B. (2018, May). Increasing student engagement in higher education using a context-aware Q&A teaching framework. In Proceedings of the 40th International Conference on Software Engineering: Software Engineering Education and Training (pp. 136-145).
• Kumar, A., Singh, S. S., Singh, K., & Biswas, B. (2020). Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications, 553, 124289.
• Lei, C., & Ruan, J. (2013). A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity. Bioinformatics, 29(3), 355-364.
• Lei, Z., & Wang, L. (2020). Construction of organisational system of enterprise knowledge management networking module based on artificial intelligence. Knowledge Management Research & Practice, 1-13
• Li, G., & Zhao, T. (2021, November). Approach of intelligence question-answering system based on physical fitness knowledge graph. In 2021 4th international conference on robotics, control and automation engineering (RCAE) (pp. 191-195). IEEE.
• Liben-Nowell, D., & Kleinberg, J. (2003, November). The link prediction problem for social networks. In Proceedings of the twelfth international conference on Information and knowledge management (pp. 556-559).
• Liu, N., Shapira, P., & Yue, X. (2021). Tracking developments in artificial intelligence research: Constructing and applying a new search strategy. Scientometrics, 126(4), 3153-3192.
• Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications, 390(6), 1150-1170.
• Martínez, V., Berzal, F., & Cubero, J. C. (2016). A survey of link prediction in complex networks. ACM computing surveys (CSUR), 49(4), 1-33.
• Nemati, H. R., Steiger, D. M., Iyer, L. S., & Herschel, R. T. (2002). Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision Support Systems, 33(2), 143-161.
• Newman, M. (2010) Networks: An Introduction. Oxford University Press, Oxford.
• Newman, M. E. (2001). Clustering and preferential attachment in growing networks. Physical review E, 64(2), 025102.
• Özçınar, H. (2015). Mapping teacher education domain: A document co-citation analysis from 1992 to 2012. Teaching and Teacher Education, 47, 42-61.
• ÖZÇINAR, H., & ÖZTÜRK, T. (2022). Eğitim bilimleri çalışmalarında kullanılan ağ yaklaşımının kavramsal haritalanması. Pamukkale Üniversitesi Eğitim Fakültesi Dergisi, 1-23.
• Pai, R. Y., Shetty, A., Shetty, A. D., Bhandary, R., Shetty, J., Nayak, S., ... & D'souza, K. J. (2022). Integrating artificial intelligence for knowledge management systems–synergy among people and technology: a systematic review of the evidence. Economic Research-Ekonomska Istraživanja, 1-23.
• Pavlov, M., & Ichise, R. (2007). Finding experts by link prediction in co-authorship networks. FEWS, 290, 42-55.
• Phan, A. C., Phan, T. C., & Trieu, T. N. (2022). A systematic approach to healthcare knowledge management systems in the era of big data and artificial intelligence. Applied Sciences, 12(9), 4455.
• Qi, Y., Bar‐Joseph, Z., & Klein‐Seetharaman, J. (2006). Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins: Structure, Function, and Bioinformatics, 63(3), 490-500
• Richardson, M., & Domingos, P. (2002, July). Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 61-70). Rosenberg, N. (1963). Technological change in the machine tool industry, 1840–1910. The journal of economic history, 23(4), 414-443.
• Sanzogni, L., Guzman, G., & Busch, P. (2017). Artificial intelligence and knowledge management: questioning the tacit dimension. Prometheus, 35(1), 37-56.
• Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In The adaptive web (pp. 291-324). Springer, Berlin, Heidelberg.
• Schmoch, U. (2008). Concept of a technology classification for country comparisons. Final report to the world intellectual property organisation (wipo), WIPO.
• Serenko, A. (2013). Meta-analysis of scientometric research of knowledge management: discovering the identity of the discipline. Journal of Knowledge Management. , 773- 812
• Tabassum, S., Pereira, F. S., Fernandes, S., & Gama, J. (2018). Social network analysis: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), e1256.
• WIPO Guide to Using Patent Information. (2022). (n.p.): WIPO.
• WIPO. (2019). WIPO technology trends 2019: Artificial intelligence. Geneva: World Intellectual Property Organization.
• Wohlfarth, T., & Ichise, R. (2008, November). Semantic and event-based approach for link prediction. In International Conference on Practical Aspects of Knowledge Management (pp. 50-61). Springer, Berlin, Heidelberg.
• Wu, S., Sun, J., & Tang, J. (2013, February). Patent partner recommendation in enterprise social networks. In Proceedings of the sixth ACM international conference on Web search and data mining (pp. 43-52)
• Xu, J., & Chen, H. (2008). The topology of dark networks. Communications of the ACM, 51(10), 58-65.
• Zhang, M., Cui, Z., Jiang, S., & Chen, Y. (2018, April). Beyond link prediction: Predicting hyperlinks in adjacency space. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
• Zhou, T., Lu, L., & Zhang, Y. C. (2009). Predicting missing links via local information. The European Physical Journal B, 71(4), 623-630.
• Zhou, Z. W., Ting, Y. H., Jong, W. R., & Chiu, M. C. (2022). Knowledge Management for Injection Molding Defects by a Knowledge Graph. Applied Sciences, 12(23), 11888.