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Research Article | Open Access | Download PDF
Volume 13 | Issue 10 | Year 2026 | Article Id. IJECE-V13I10P114 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I10P114

An Explainable Semantic Enterprise Research Network for Institutional Collaboration Recommendation

Ameen Shaheen, Aysh Alhroob, Wael Alzyadat

Received Revised Accepted Published
17 Jul 2026 03 Oct 2026 14 Oct 2026 28 Oct 2026

Citation :

Ameen Shaheen, Aysh Alhroob, Wael Alzyadat, "An Explainable Semantic Enterprise Research Network for Institutional Collaboration Recommendation," International Journal of Electronics and Communication Engineering, vol. 13, no. 10, pp. 239-254, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I10P114

Abstract

Universities need practical methods to integrate internal scholarly data for collaboration, discovery, and institutional decision-making. However, research information is often distributed across institutional records, ORCID profiles, and Scopus indicators, limiting the ability to identify meaningful internal collaboration opportunities. This paper presents an AI-supported explainable semantic enterprise research network for institutional collaboration recommendation. The proposed system integrates internal university records, ORCID-linked information, and Scopus-related indicators into a unified research intelligence platform. Researcher profiles are constructed from publication metadata, keywords, topics, citation indicators, publication-quality measures, and institutional attributes. The system uses OpenAI text-embedding-3-small to represent researcher profiles and applies cosine similarity to retrieve candidate collaborators. A structured AI-enrichment stage then generates shared topics, a recommendation rationale, a suggested project idea, and initial collaboration steps using pseudonymized researcher profiles. The platform was evaluated using institutional case study data, expert-based relevance assessment, additional baseline comparisons, ablation analysis, sensitivity analysis, and a preliminary user-acceptance assessment. The deployed system included 89 users, including 86 teaching and research staff, with 71% ORCID coverage and 67% Scopus coverage. The expert evaluation covered 45 recommendations for 9 target researchers and achieved a mean relevance score of 4.28, Precision@5 of 0.89, and NDCG@5 of 0.90. The proposed method performed better than keyword-overlap, BM25, and dense-embedding baselines. A preliminary System Usability Scale assessment with 15 users produced a mean score of 78.6. The results indicate that semantic profiling, institutional indicators, and explainable recommendations can support internal collaboration discovery and research monitoring.

Keywords

AI-Supported recommendation, Enterprise research network, Semantic researcher profiling, Institutional collaboration, Research analytics.

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