ResearchMatch: A Semantic-Bibliometric Prototype for Evidence-Supported Researcher Discovery

Daniel Segboru Emmanuel; Ikiomoye Emmanuel

doi:10.63158/journalisi.v8i5.1841

Authors

Daniel Segboru Emmanuel Bangor University
Ikiomoye Emmanuel University of South Africa

DOI:

https://doi.org/10.63158/journalisi.v8i5.1841

Keywords:

Researcher Discovery, Semantic Search, scholarly metadata, expert finding, author disambiguation, OpenAlex, MiniLM

Abstract

ResearchMatch is a semantic-bibliometric prototype for evidence-supported discovery of publication-based researcher profiles in user-defined academic themes. It integrates scholarly metadata from OpenAlex, CrossRef and DOAJ with all-MiniLM-L6- v2 embeddings and MongoDB Atlas Vector Search to match keywords, abstracts and uploaded documents against stored researcher profiles. The database contains 18,532 stored profile records, not independently verified unique researchers, because of metadata incompleteness and author ambiguity. A six-query pilot benchmark across computer science, medicine, engineering, social science, education and humanities compared keyword matching, BM25, OpenAlex search, citation-based ranking, MiniLM dense retrieval and hybrid BM25-MiniLM retrieval. ResearchMatch MiniLM achieved P@5 of 0.77, MAP of 0.72 and nDCG@10 of 0.78, while the hybrid approach achieved 0.80, 0.76 and 0.82, respectively. Representative outputs also provided ORCID, affiliation and supporting publication evidence for inspection. The study evaluates evidence availability rather than user-perceived explainability, and no formal user study was conducted. The findings provide preliminary retrieval evidence but do not establish general effectiveness or researcher-profile fairness. ResearchMatch therefore remains a prototype requiring larger expert-curated benchmarks, user evaluation and production-scale validation.

Downloads

Download data is not yet available.

References

[1] X.-L. Pham and T. T. Le, “Bibliometric analysis and systematic review of research on expert finding: A PRISMA-guided approach,” Int. Arab J. Inf. Technol., vol. 21, no. 4, pp. 661–674, Jul. 2024, doi: 10.34028/iajit/21/4/9.

[2] X. Zhao and Y. Zhang, “Reviewer assignment algorithms for peer review automation: A survey,” Inf. Process. Manag., vol. 59, no. 5, Art. no. 103028, Sep. 2022, doi: 10.1016/j.ipm.2022.103028.

[3] N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proc. 2019 Conf. Empirical Methods Natural Language Process. and 9th Int. Joint Conf. Natural Language Process. (EMNLP-IJCNLP), Hong Kong, China, Nov. 2019, pp. 3982–3992, doi: 10.18653/v1/D19-1410.

[4] I. Beltagy, K. Lo, and A. Cohan, “SciBERT: A pretrained language model for scientific text,” in Proc. 2019 Conf. Empirical Methods Natural Language Process. and 9th Int. Joint Conf. Natural Language Process. (EMNLP-IJCNLP), Hong Kong, China, Nov. 2019, pp. 3615–3620, doi: 10.18653/v1/D19-1371.

[5] A. Cohan, S. Feldman, I. Beltagy, D. Downey, and D. Weld, “SPECTER: Document-level representation learning using citation-informed transformers,” in Proc. 58th Annu. Meeting Assoc. Comput. Linguistics, Online, Jul. 2020, pp. 2270–2282, doi: 10.18653/v1/2020.acl-main.207.

[6] J. Priem, H. Piwowar, and R. Orr, “OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts,” in Proc. 26th Int. Conf. Science, Technology and Innovation Indicators (STI 2022), Granada, Spain, Sep. 2022, doi: 10.5281/zenodo.6936227.

[7] E. Álvarez-García, D. García-Costa, I. De Waele, A. Marusic, and F. Grimaldo, “Expert assignment system based on natural language processing for Marie Sklodowska-Curie actions,” Sci. Rep., vol. 16, no. 1, Art. no. 6396, Jan. 2026, doi: 10.1038/s41598-026-37115-8.

[8] Y. Zhang and X. Chen, “Explainable recommendation: A survey and new perspectives,” Found. Trends Inf. Retr., vol. 14, no. 1, pp. 1–101, 2020, doi: 10.1561/1500000066.

[9] S. Khalid et al., “Comprehensive review of academic search systems: Evolution, analysis, and future research directions,” Soc. Netw. Anal. Min., vol. 15, Art. no. 66, Jul. 2025, doi: 10.1007/s13278-025-01476-1.

[10] L. M. de Campos, J. M. Fernández-Luna, J. F. Huete, F. J. Ribadas-Pena, and N. Bolaños, “Information retrieval and machine learning methods for academic expert finding,” Algorithms, vol. 17, no. 2, Art. no. 51, Jan. 2024, doi: 10.3390/a17020051.

[11] Y. Wang, J. Liu, X. Xu, X. Ke, T. Wu, and X. Gou, “Efficient and effective academic expert finding on heterogeneous graphs through (k, P)-core based embedding,” ACM Trans. Knowl. Discov. Data, vol. 17, no. 6, pp. 1–35, 2023, doi: 10.1145/3578365.

[12] I. A. Mannix and E. Yulianti, “Academic expert finding using BERT pre-trained language model,” Int. J. Adv. Intell. Inform., vol. 10, no. 2, pp. 280–295, May 2024, doi: 10.26555/ijain.v10i2.1497.

[13] W. Wang, F. Wei, L. Dong, H. Bao, N. Yang, and M. Zhou, “MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers,” Adv. Neural Inf. Process. Syst., vol. 33, pp. 5776–5788, 2020.

[14] P. J. Rousseeuw, “Silhouettes: A graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math., vol. 20, pp. 53–65, Nov. 1987, doi: 10.1016/0377-0427(87)90125-7.

[15] G. Hendricks, D. Tkaczyk, J. Lin, and P. Feeney, “Crossref: The sustainable source of community-owned scholarly metadata,” Quant. Sci. Stud., vol. 1, no. 1, pp. 414–427, 2020, doi: 10.1162/qss_a_00022.

[16] Deepak and S. Sheoran, “Vector search in large language models: Experimental evaluation with MongoDB Atlas,” J. Cybersecur. Inf. Manag., vol. 17, no. 2, pp. 146–166, 2026, doi: 10.54216/JCIM.170211.

[17] L. E. Cheby, “Open access metadata for journals in Directory of Open Access Journals: Who, how, and what scheme?,” School Inf. Student Res. J., vol. 6, no. 1, Art. no. 4, 2016, doi: 10.31979/2575-2499.060104.

[18] H. Singh, R. Singh, A. Malhotra, and M. Kaur, “Developing a biomedical expert finding system using Medical Subject Headings,” Healthc. Inform. Res., vol. 19, no. 4, pp. 243–249, Dec. 2013, doi: 10.4258/hir.2013.19.4.243.

[19] M. Y. Bayraktar and M. Kaya, “Expert recommendation system to promote development-focused universities,” Türk Doğa ve Fen Derg., vol. 13, no. 2, pp. 30–33, Jun. 2024, doi: 10.46810/tdfd.1397626.

[20] D. Wu, S. Fan, and F. Yuan, “Research on pathways of expert finding on academic social networking sites,” Inf. Process. Manag., vol. 58, no. 2, Art. no. 102475, Mar. 2021, doi: 10.1016/j.ipm.2020.102475.

[21] Y. Li, S. Ma, and R. Huang, “Social context analysis for topic-specific expert finding in online learning communities,” in Smart Learning Environments, M. Chang and Y. Li, Eds. Berlin, Germany: Springer, 2015, pp. 57–74, doi: 10.1007/978-3-662-44447-4_4.

[22] S. Jimenez, F. N. Silva, G. Dueñas, and A. Gelbukh, “ProficiencyRank: Automatically ranking expertise in online collaborative social networks,” Inf. Sci., vol. 588, pp. 231–247, Apr. 2022, doi: 10.1016/j.ins.2021.11.067.