Abstract: In the modern interdependent academic and professional landscape, finding the most appropriate domain expert is essential for cultivating collaboration, stimulating research, and industrial innovation. Yet, existing expert identification processes are typically disorganized, time-consuming, and decentralized. The Domain Expert Finder System (DEFS) rectifies these shortcomings by using web scraping to gather publicly accessible information from academic institutions, professional networks, and scholarly stores. This information is filtered, processed, and saved into a structured database, and users can access complete expert profiles through an interactive and searchable platform. In contrast to current systems that depend only on publication records or social networks, DEFS encompasses both faculty members and located students, hence expanding the range of expert discoveries. This paper overviews the system architecture, methodologies, comparative studies, and real-world implementations of DEFS. It talks about the major advantages like enhanced search accuracy, scalability of data, and automation along with ethical issues, technological hurdles, and prospects. Through a critical analysis of the literature and experimental verification, this survey points out the ways in which DEFS is an efficient and accessible solution to the issue of expert identification in contemporary knowledge environments.
Keywords: Expert Discovery, Web Scraping, Academic Profiles, Automation, Data Mining, Faculty Search, Placed Students, Knowledge Graphs
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DOI:
10.17148/IJARCCE.2025.14632
