Разработка метода идентификации экспертов в университетах
Цель данной работы заключается в разработке метода идентификации экспертов в университетах, позволяющего решать широкий спектр проблем за счет тонкой настройки в соответствии с запросом и использования нескольких релевантных источников. Исследование носит качественный характер и использует методологию Design Science Research (DSR).
Для достижения этой цели были поставлены следующие задачи:
• определить основные процессы в университетах, которые могут быть улучшены путем внедрения метода идентификации экспертов;
• перечислить и проанализировать известные методы и приемы процесса идентификации экспертов и оценить их применимость к процессам, выявленным на предыдущем этапе;
• разработать метод идентификации экспертов, применимый к любому высшему учебному заведению на основе существующих методов и приемов;
• продемонстрировать этот метод на экспертах определенного университета и подтвердить корректность и точность результатов.
В рамках исследования был разработан метод идентификации экспертов, который позволяет более гибко настраивать процесс определения местоположения экспертизы, решать широкий спектр проблем, имеющих отношение к академической среде, повышать эффективность значительной доли внутренних процессов, поддерживать принятие стратегических решений и управление человеческими ресурсами посредством отображения общей экспертизы организации. Предложенный метод учитывает особенности академической среды и был применен для создания профилей экспертизы нескольких экспертов Высшей школы менеджмента СПбГУ.
TABLE OF CONTENTS
TABLE OF CONTENTS 2
INTRODUCTION 4
CHAPTER 1. THEORETICAL BACKGROUND OF EXPERTISE LOCATION PROCESS 6
Knowledge mapping 6
Expertise location systems 15
CHAPTER 2. RESEARCH GAP AND METHODOLOGY 21
Research gap 21
Research goals and objectives 22
Design science research methodology 22
Data collection methods 24
CHAPTER 3. METHOD DESIGN AND DEVELOPMENT 25
Purposes of expertise location and valid sources of expertise 25
Requirements for the method of expertise location 26
General concepts of the method of expertise location 28
Method for expertise location 30
CHAPTER 4. METHOD DEMONSTRATION 34
Description of organization and environment 34
Identifying relevant goals of expertise search 35
Gathering primary data 36
Data processing and intermediate results 39
Query-based final stage of data processing and visualization 42
DISCUSSIONS 45
Benefits and limitations of the method 45
Suggestions for further research 46
CONCLUSION 47
REFERENCES 48
Appendix 1 54
Appendix 2 56
Appendix 3 58
Appendix 4. 59
In current rapidly changing environment with growing complexity of all processes and products it is becoming crucial to manage knowledge assets both for individuals in order to be able to solve challenging problems and increase personal effectiveness and for organizations to gain competitive advantage, assess and mitigate risks caused by concentration of knowledge within several experts by capturing this knowledge and making it easily accessible throughout the company, to determine knowledge gaps to further increase overall organization effectiveness by educating employees to internalize potentially important knowledge, to understand and describe organization core competences and capabilities.
. Knowledge management as a field of study has existed for more than 30 years. The concept of knowledge primarily was related mostly to the academic field, but now it has become a crucial element of organizational life (Kapur, 2020).
Knowledge management aims to develop tools, practices and frameworks to find, extract and effectively manage knowledge, which is also developing rapidly and gaining more attention lately. One of important problems that knowledge management tries to solve is that a great deal of knowledge is tacit and either is not formally described or is impossible to be extracted, being accessible only through reaching for a suitable expert.
Knowledge mapping is a powerful method enabling company to connect experts, access knowledge in time, identify knowledge assets and flow, identify existing knowledge resources and knowledge gaps. It also can be utilized to show the important stages to build up a specific capability (Faisal et al., 2019).
Main tools that are most widely used in knowledge mapping require participation of several experts, which takes time and lays restrictions upon the possible frequency of updating the map. At the same time keeping knowledge map as relevant as possible is crucial, since relying on outdated map can lead to negative consequences such as impossibility to get access to knowledge or wrong decisions in terms of managing HR (e.g. developing outdated competences or irrelevant skills), which undermine the whole concept.
Another problem is that proper knowledge map has to be perfectly balanced between simplicity and sufficiency of detail, so that it could be easily grasped at organization level and at the same time useful at operation level. In order to achieve this, we have to design the map for the particular organization and for the particular purposes. We suppose that there are ways that allow educational organizations to identify experts in specific fields by analyzing their publications, participation in conferences or some other activities that can be automatically gathered and processed to create and update at least some types of knowledge maps without need to conduct personal interviews consuming significant amount of time and effort.
But in order to draw a map, showing where knowledge is created, located and describing how it flows throughout organization we have to identify experts first. That means we need to assess expertise of each potential expert in each domain of knowledge relevant for a specific organization.
Moreover, expertise location system might be helpful even without further integration of its results into knowledge maps. People often prefer to consult with an expert when they face a problem they do not know how to solve so providing them with a useful tool to do that is a good application of plain expertise location system.
Afzal, M.T.; Maurer, H.A. Expertise Recommender System for Scientific Community. J. UCS 2011, 17, 1529–1549.
• Alarfaj, F.; Kruschwitz, U.; Hunter, D.; Fox, C. Finding the Right Supervisor: Expert-Finding in a University Domain. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, Montréal, QC, Canada, 3–8 June 2012; pp. 1–6.
• Anderson, P. Perspective: Complexity Theory and Organization Science. Organ. Sci. 1999, 10, 216–232.
• Balaid A.; Rozan M.; Hikmi S..; Memon J. Knowledge maps: A systematic literature review and directions for future research. International Journal of Information Management. 2016, 36, 451-475.
• Balog, K.; Azzopardi, L.; De Rijke, M. A Language Modeling Framework for Expert Finding. Inf. Process. Manag. 2009, 45, 1–19.
• Balog, K.; Azzopardi, L.; De Rijke, M. Formal Models for Expert Finding in Enterprise Corpora. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, WA, USA, 6–11 August 2006; pp. 43–50.
• Balog, K.; Fang, Y.; De Rijke, M.; Serdyukov, P.; Si, L. Expertise Retrieval. Found. Trends® Inf. Retr. 2012, 6, 127–256.
• Bargent, J. 11 Steps to Building a Knowledge Map. 2002
• Berendsen, R.; Rijke, M.; Balog, K.; Bogers, T.; Bosch, A. On the Assessment of Expertise Profiles. J. Assoc. Inf. Sci. Technol. 2013, 64, 2024–2044.
• Chan, K.; Liebowitz, J. The synergy of social network analysis and knowledge mapping: a case study. International Jounal of Management and Decision Making. 2006, 7(1), 19–35.
• Chen, H.-H.; Treeratpituk, P.; Mitra, P.; Giles, C.L. Csseer: An Expert Recommendation System Based on Citeseerx. In Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, Indianapolis, IN, USA, 22–26 July 2013; pp. 381–382.
• Chung, W.; Chen, H.; Nunamaker Jr. J.F. Business intelligence explorer: a knowledge map framework for discovering business intelligence on the web. 2003. 36th Hawaii international conference on system sciences, pp. 1–10
• Cifariello, P.; Ferragina, P.; Ponza, M. Wiser: A Semantic Approach for Expert Finding in Academia Based on Entity Linking. Inf. Syst. 2019, 82, 1–16.
• Craswell, N.; Hawking, D.; Vercoustre, A.-M.; Wilkins, P. P@ Noptic Expert: Searching for Experts Not Just for Documents. In Ausweb Poster Proceedings; Southern Cross University: Queensland, Australia, 2001; p. 17.
• Dang, Y.; Zhang, Y.; Chen, H.; Larson, C. C. Castillo-Chavez, H. Chen, W. B. Lober, M. Thurmond, & D. Zeng (Eds.), Knowledge mapping for bioterrorism-related literature. 2011, ,27, pp. 311–338
• Das, S.; Mitra, P.; Giles, C. Learning To Rank Homepages for Researchername Queries. In the International Workshop on Entity-Oriented Search; ACM, EOS, SIGIR 2011 Workshop: Beijing, China, 2011; pp. 53–58.
• Datta, A.; Yong, J.T.T.; Braghin, S. The Zen of Multidisciplinary Team Recommendation. J. Assoc. Inf. Sci. Technol. 2014, 65, 2518–2533.
• Davoodi, E.; Afsharchi, M.; Kianmehr, K. A Social Network-Based Approach to Expert Recommendation System. In International Conference on Hybrid Artificial Intelligence Systems; Springer: Berlin/Heidelberg, Germany, 2012; pp. 91–102.
• Davoodi, E.; Kianmehr, K.; Afsharchi, M. A Semantic Social Network-Based Expert Recommender System. Appl. Intell. 2013, 39, 1–13.
• Deng, H.; Han, J.; Lyu, M.R.; King, I. Modeling and Exploiting Heterogeneous Bibliographic Networks for Expertise Ranking. In Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, Washington, DC, USA, 10–14 June 2012; pp. 71–80.
• Deng, H.; King, I.; Lyu, M.R. Enhanced Models for Expertise Retrieval Using Community-Aware Strategies. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2012, 42, 93–106.
• Driessen, S.; Huijsen, W.-O.; Grootveld, M. A framework for evaluating knowledge-mapping tools. Journal of Knowledge Management. 2007, 11(2), 109–117
• Duffy, J. The KM technology infrastructure. Information Management Journal. 2000, 34(2), 62–66
• Earl, M. Knowledge management strategies: toward taxonomy. Journal of Management Information Systems. 2001, 18(1), 215–233.
• Ehrlich, K. Locating Expertise: Design Issues for an Expertise Locator System. Shar. Expertise-Beyond Knowledge Management; Mit Press: Cambridge, MA, USA, 2003; pp. 137–158.
• Eppler, M. J. Making knowledge visible through knowledge maps: concepts, elements cases. Handbook on knowledge management (Vol. 1) Berlin Heidelberg: Springer. 2004, pp. 189–205
• Eppler, M. J.; Simon, H. A. A process-based classification of knowledge maps and application examples. Knowledge and Process Management. 2008, 15(1), 59–71
• Faisal, Hafiz & Rahman, Atta & Zaman, Gohar. Knowledge Mapping for Research Papers. 2019, 19, 158-164
• Fang, Y.; Si, L.; Mathur, A. Facfinder: Search for Expertise in Academic Institutions; Technical Report: Serc-Tr-294; Department of Computer Science, Purdue University: West Lafayette, Indiana, 2008.
• Fang, Y.; Si, L.; Mathur, A.P. Discriminative Graphical Models for Faculty Homepage Discovery. Inf. Retr. 2010, 13, 618–635.
• Fazel-Zarandi, M.; Devlin, H.J.; Huang, Y.; Contractor, N. Expert Recommendation Based on Social Drivers, Social Network Analysis, and Semantic Data Representation. In Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, Chicago, IL, USA, 27 October 2011; pp. 41–48. Appl. Sci. 2019, 9, 4250 29 of 32
• Gilbert, J.; Raub, K. Managing knowledge-Building blocks for success, John Wiley & Sons. 2000, p. 360
• Gollapalli, S.D.; Mitra, P.; Giles, C.L. Similar Researcher Search in Academic Environments. In Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, Washington, DC, USA, 10–14 June 2012; pp. 167–170.
• Hansen, B. H.; Kautz, K. Knowledge mapping: a technique for identifying knowledge flows in software organisations. In Software process improvement. 2004. pp. 126–137. Berlin Heidelberg: Springer-Verlag.
• Heck, T.; Hanraths, O.; Stock, W.G. Expert Recommendation for Knowledge Management in Academia. In Proceedings of the Association for Information Science and Technology, ASIST 2011, New Orleans, LA, USA, 9–13 October 2011; pp. 1–4.
• Hellström, T.; Husted, K. Mapping knowledge and intellectual capital in academic environments: a focus group study. Journal of Intellectual Capital. 2004, 5(1), 165–180.
• Hofmann, K.; Balog, K.; Bogers, T.; De Rijke, M. Contextual Factors for Finding Similar Experts. J. Assoc. Inf. Sci. Technol. 2010, 61, 994–1014.
• Huang, C.; Yao, L.; Wang, X.; Benatallah, B.; Sheng, Q.Z. Expert as a Service: Software Expert Recommendation Via Knowledge Domain Embeddings in Stack Overflow. In Proceedings of the 2017 IEEE International Conference on Web Services (ICWS), Honolulu, HI, USA, 25–30 June 2017; pp. 317–324.
• Huff A. Mapping strategic knowledge. London: Sage Publications. 2002, p. 302
• Husain O.; Salim. N; Alias R.; Abdelsalam S.; Hassan A. Expert Finding Systems: A Systematic Review. Applied Sciences. 2019, 9, 4250
• Juang, M.-C.; Huang, C.-C.; Huang, J.-L. Efficient Algorithms for Team formation with a Leader in Social Networks. J. Supercomput. 2013, 66, 721–737.
• Kapur, R. Knowledge Management. 2020. Accessible online (last accessed 19.05.2020): https://www.researchgate.net/publication/338421805_Knowledge_Management
• Kargar, M.; An, A. Discovering Top-K Teams of Experts with/without a Leader in Social Networks. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, Scotland, UK, 24–28 October 2011; pp. 985–994.
• Kim, S.; Suh, E.; Hwang, H. Building the knowledge map: an industrial case study. Journal of Knowledge Management. 2003, 7(2), 34–45
• Kong, W.; Liu, Q.; Yang, Z.; Han, S. Collaborative Filtering Algorithm Incorporated with Cluster-Based Expert Selection. J. Inf. Comput. Sci. 2012, 9, 3421–3429.
• Lecocq, R. Knowledge mapping: a conceptual model. 2006, pp. 1–96
• Lee J.; Fink, D. Knowledge mapping: encouragements and impediments to adoption. Journal of Knowledge Management. 2013, 17(1), 16–28.
• Li, C.-T.; Shan, M.-K.; Lin, S.-D. Context-Based People Search in Labeled Social Networks. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, Scotland, UK, 24–28 October 2011; pp. 1607–1612.
• Li, M.; Liu, L.; Li, C.-B. An Approach to Expert Recommendation Based on Fuzzy Linguistic Method and Fuzzy Text Classification in Knowledge Management Systems. Expert Syst. Appl. 2011, 38, 8586–8596.
• Liebowitz, J. Linking social network analysis with the analytic hierarchy process for knowledge mapping in organizations. Journal of Knowledge Management. 2005, 9(1), 76–86.
• Lin, S.; Hong, W.; Wang, D.; Li, T. A Survey on Expert Finding Techniques. J. Intell. Inf. Syst. 2017, 49, 1–25.
• Liu, D.; Xu, W.; Du, W.; Wang, F. How to Choose Appropriate Experts for Peer Review: An Intelligent Recommendation Method in a Big Data Context. Data Sci. J. 2015, 14, 16.
• Liu, D.-R.; Chen, Y.-H.; Kao, W.-C.; Wang, H.-W. Integrating Expert Profile, Reputation and Link Analysis for Expert Finding in Question-Answering Websites. Inf. Process. Manag. 2013, 49, 312–329.
• Liu, L.; Li, J.; Lv, C. A method for enterprise knowledge map construction based on social classification. Systems Research and Behavioral Science. 2009, 205(2), 541–551
• Logan, D.; Caldwell, F. Knowledge mapping: five key dimensions to consider. Gartner-Group. 2000, 30–50
• Mangaravite, V.; Santos, R.L.; Ribeiro, I.S.; Gonçalves, M.A.; Laender, A.H. The Lexr Collection for Expertise Retrieval in Academia. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy, 17–21 July 2016; pp. 721–724.
• Mansingh, G.; Osei-Bryson, K.-M.; Hirata, T. Building ontology-based knowledge maps to assist knowledge process outsourcing decisions. Knowledge Management Research & Practice. 2009, 7(1), 37–51
• Mcdonald, D.W. Evaluating Expertise Recommendations. In Proceedings of the 2001 International ACM Siggroup Conference on Supporting Group Work, Boulder, CO, USA, 30 September–3 October 2001; pp. 214–223.
• Mcdonald, D.W.; Ackerman, M.S. Expertise Recommender: A Flexible Recommendation System and Architecture. In Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, Philadelphia, PA, USA, 2000; pp. 231–240.
• Mockus, A.; Herbsleb, J.D. Expertise Browser: A Quantitative Approach To Identifying Expertise. In Proceedings of the 24th International Conference on Software Engineering, Orlando, FL, USA, 25–25 May 2002; pp. 503–512. Appl. Sci. 2019, 9, 4250 27 of 32
• Moreira, C.; Wichert, A. Finding Academic Experts on A Multisensor Approach Using Shannon’s Entropy. Expert Syst. Appl. 2013, 40, 5740–5754.
• Naeem, M.; Khan, M.B.; Afzal, M.T. Expert Discovery: A Web Mining Approach. J. Data Min. 2013, 1, 35–47.
• Neshati, M.; Beigy, H.; Hiemstra, D. Expert Group Formation Using Facility Location Analysis. Inf. Process. Manag. 2014, 50, 361–383.
• Neshati, M.; Beigy, H.; Hiemstra, D. Multi-Aspect Group formation Using Facility Location Analysis. In Proceedings of the 17th Australasian Document Computing Symposium, Dunedin, New Zealand, 5–6 December 2012; pp. 62–71.
• Neshati, M.; Fallahnejad, Z.; Beigy, H. On Dynamicity of Expert Finding in Community Question Answering. Inf. Process. Manag. 2017, 53, 1026–1042.
• Neshati, M.; Hiemstra, D.; Asgari, E.; Beigy, H. Integration of Scientific and Social Networks. World Wide Web 2014, 17, 1051–1079.
• Pei, X.; Wang, C. A study on the construction of knowledge map in matrix organizations. International conference of knowledge management and information retrieval, 2009. (pp. 102–107)
• Petkova, D.; Croft, W.B. Proximity-Based Document Representation for Named Entity Retrieval. In Proceedings of the 16th ACM Conference on Conference on Information and Knowledge Management, Lisbon, Portugal, 6–10 November 2007; pp. 731–740.
• Price, S.; Flach, P.A. Computational Support for Academic Peer Review: A Perspective From Artificial Intelligence. Commun. ACM 2017, 60, 70–79.
• Protasiewicz, J.; Pedrycz, W.; Kozłowski, M.; Dadas, S.; Stanisławek, T.; Kopacz, A.; Gał ˛e˙zewska, M. A Recommender System of Reviewers and Experts in Reviewing Problems. Knowl. Based Syst. 2016, 106, 164–178.
• Quintas, P. Implications of the division of knowledge for innovation in networks. In Networks, alliances and partnerships in the innovation process. Springer US. 2002, pp. 135–162.
• Rao, L.; Mansingh, G.; Osei-Bryson, K.-M. Building ontology based knowledge maps to assist business process re-engineering. Decision Support Systems. 2012, 52(3), 577–589
• Renukappa, Egbu C. Knowledge mapping: concepts and benefits for a sustainable urban environment. The 20th annual conference association of researchers in construction management (ARCOM). 2004, Vol. 2 (pp. 1–12).
• Ribeiro, I.S.; Santos, R.L.; Gonçalves, M.A.; Laender, A.H. On Tag Recommendation for Expertise Profiling: a Case Study in the Scientific Domain. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, Shanghai, China, 2–6 February 2015; pp. 189–198.
• Sateli, B.; Löffler, F.; König-Ries, B.; Witte, R. Scholarlens: Extracting Competences from Research Publications for the Automatic Generation of Semantic User Profiles. PeerJ Comput. Sci. 2017, 3, 121.
• Silva, A.T.P. A Research Analytics Framework for Expert Recommendation in Research Social Networks; City University of Hong Kong: Hong Kong, China, 2014.
• Silva, T.; Guo, Z.; Ma, J.; Jiang, H.; Chen, H. A Social Network-Empowered Research Analytics Framework for Project Selection. Decis. Support Syst. 2013, 55, 957–968.
• Smirnova, E.; Balog, K. A User-Oriented Model for Expert Finding. Adv. Inf. Retr. 2011, 6611, 580–592.
• Sriharee, G.; Anekboon, K. Multiple Aspect Ranking for Researcher Expertise Finding. In Proceedings of the IEEE International Conference on Knowledge Engineering and Applications (Ickea), Singapore, 28–30 September 2016; pp. 207–211.
• Stankovic, M.; Wagner, C.; Jovanovic, J.; Laublet, P. Looking for Experts? What Can Linked Data do for You? Available online: https://www.researchgate.net/publication/221023369_Looking_for_Experts_What_can_Linked_Data_do_for_You (accessed on 10 May 2020).
• Sun, J.; Xu, W.; Ma, J.; Sun, J. Leverage Raf to Find Domain Experts on Research Social Network Services: A Big Data Analytics Methodology With Mapreduce Framework. Int. J. Prod. Econ. 2015, 165, 185–193.
• Sziklai, B. How to Identify Experts in a Community? Int. J. Game Theory 2018, 47, 155–173.
• Tang, J.; Zhang, J.; Yao, L.; Li, J.; Zhang, L.; Su, Z. Arnetminer: Extraction and Mining of Academic Social Networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA, 24–27 August 2008; pp. 990–998.
• Taylor, M.; Richards, D. Finding and Validating Expertise; ECIS: London, UK, 2011.
• Tiwana, A. Knowledge management toolkit, the amrit tiwana knowledge management toolkit. First Edit.1999, pp. 1–482.
• Torkzadeh Mahani, N.; Dehghani, M.; Mirian, M.S.; Shakery, A.; Taheri, K. Expert Finding by the Dempster-Shafer Theory for Evidence Combination. Expert Syst. 2018, 35, 12231.
• Tran, H.D.; Cabanac, G.; Hubert, G. Expert Suggestion for Conference Program Committees. In Proceedings of the 11th International Conference on Research Challenges in Information Science (Rcis), Brighton, UK, 10–12 May 2017; pp. 221–232.
• Uddin, M.N.; Duong, T.H.; Oh, K.-J.; Jo, G.-S. An Ontology Based Model for Experts Search and Ranking. In Asian Conference on Intelligent Information and Database Systems; Springer: Berlin/Heidelberg, Germany; pp. 150–160.
• Vestal, W. Knowledge mapping: the essentials for success. Houston, TX: APQC Publications. 2005 p. 75.
• Wang, G.A.; Jiao, J.; Abrahams, A.S.; Fan, W.; Zhang, Z. Expertrank: A Topic-Aware Expert Finding Algorithm for Online Knowledge Communities. Decis. Support Syst. 2013, 54, 1442–1451.
• Wang, Q.; Ma, J.; Liao, X.; Du, W. A Context-Aware Researcher Recommendation System for Universityindustry Collaboration on R&D Projects. Decis. Support Syst. 2017, 103, 46–57.
• Weiss, D.J.; Shanteau, J. Empirical Assessment of Expertise. Hum. Factors 2003, 45, 104–116.
• Wexler, M. N. The who, what and why of knowledge mapping. Journal of Knowledge Management. 2001, 5(3), 249–264
• Wu, C.-J.; Chung, J.-M.; Lu, C.-Y.; Lee, H.-M.; Ho, J.-M. Using Web-Mining for Academic Measurement and Scholar Recommendation in Expert Finding System. In Proceedings of the 2011 IEEE/Wic/Acm International Conference on Web Intelligence and Intelligent Agent Technology (Wi-Iat), Lyon, France, 22–27 August 2011; pp. 288–291.
• Wu, H.; He, J.; Pei, Y.; Long, X. Finding Research Community in Collaboration Network with Expertise Profiling. In International Conference on Intelligent Computing; Springer: Berlin/Heidelberg, Germany, 2010; pp. 337–344.
• Xu, W.; Sun, J.; Ma, J.; Du, W. A Personalized Information Recommendation System for R&D Project Opportunity Finding in Big Data Contexts. J. Netw. Comput. Appl. 2016, 59, 362–369.
• Xu, Y.; Guo, X.; Hao, J.; Ma, J.; Lau, R.Y.K.; Xu, W. Combining Social Network and Semantic Concept Analysis for Personalized Academic Researcher Recommendation. Decis. Support Syst. 2012, 54, 564–573.
• Yang, M.-S.; Kang, N.-K.; Kim, Y.-J.; Kim, J.-S.; Choi, K.-N.; Kim, Y.-K. Expert Recommendation System Based on Analyzing Expertise and Networks of Human Resources in National Science & Technology Information Service. J. Central South Univ. 2013, 20, 2212–2218.
• Yasin, F.; Egbu, C. Critical steps to knowledge mapping in facilities management organisation. 27th annual ARCOM conference. 2011, 603–612.
• Yimam-Seid, D.; Kobsa, A. Expert-Finding Systems for Organizations: Problem and Domain Analysis and the Demoir Approach. J. Organ. Comput. Electron. Commer. 2003, 13, 1–24.
• Zhan, Z.; Yang, L.; Bao, S.; Han, D.; Su, Z.; Yu, Y. Finding Appropriate Experts for Collaboration. In International Conference on Web-Age Information Management; Springer: Berlin/Heidelberg, Germany, 2011; pp. 327–339.
• Zhao, G.; Miao, P.; Guan, Y. Construction of intelligence knowledge map for complex product development. Journal of Engineering Science and Technology Review. 2013, 6(3), 82–87
Последние выполненные заказы
Хочешь уникальную работу?
Больше 3 000 экспертов уже готовы начать работу над твоим проектом!
4.8 (30 отзывов)
5 (20 отзывов)
4.2 (6 отзывов)
5 (158 отзывов)
4.4 (59 отзывов)
4.8 (211 отзывов)
4.8 (2878 отзывов)
4.9 (66 отзывов)
4.8 (17 отзывов)
