# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 3 The Deep Web Communication Protocol
### Message Structure and Data Model
## 8 Conclusions and Future Works

This work presents FAIRFind, a novel system that operationalizes the FAIR principles within the FAIRsharing ecosystem by transforming static metadata into executable, machine-actionable workflows. While FAIRsharing curates metadata about standards and repositories, it does not expose datasets hidden behind dynamic web interfaces. FAIRFind bridges this gap by autonomously crawling portals, interpreting forms and tables using large language models, and constructing structured, reproducible workflows for deep web access. At the core of FAIRFind is DWCP, a formal, model-agnostic specification that encodes user interface interactions, including input schemas, submission logic, and result extraction, into standardized, machine-readable instructions. This transforms web-based data access from a manual process into a reusable computational asset and advances the Accessibility and Reusability aspects of FAIR. Our evaluation demonstrates over 90% success in structured data retrieval, with DeepSeek-R1 offering faster and more reliable performance, while LLaMA 3.3 achieves higher precision at increased latency. FAIRFind supports both schema-driven and natural language queries, making it suitable for diverse scientific workflows. Beyond its technical contributions, FAIRFind represents a shift from passive registries to interactive and intelligent access frameworks. By automating access to previously hidden datasets, it empowers researchers and AI systems to conduct larger-scale integration, hypothesis testing, and data-driven exploration. In the future, we intend to expand DWCP to accommodate dependent access path sequences, represented as ordered lists or graphs of DWCP records, with parameter passing and session management to preserve context between steps. FAIRFind will be able to independently carry out complex multi-hop workflows while maintaining reproducibility and FAIR compliance thanks to LLM-based planning that will direct intermediate actions. Future research will also tackle issues like robust multi-step interaction planning, schema-aware input generation, and support for authenticated sessions. Our goal is to extend FAIRFind beyond FAIRsharing to other data-rich scientific registries and domains in order to increase its impact. Incorporating vision-based interface recognition to manage non-standard layouts, creating backup plans for unsuccessful DOM interactions, and implementing FAIRFind as an open, community-driven service that is constantly improved based on user feedback are further directions. ## 9 System Availability and Experimental Environment

The FAIRFind system is currently in its testing phase, ahead of its official release. Once publicly available, it will be accessible at [[http://dblab.nkn.uidaho.edu/fairfind/](http://dblab.nkn.uidaho.edu/fairfind/)]([http://dblab.nkn.uidaho.edu/fairfind/](http://dblab.nkn.uidaho.edu/fairfind/)) (accessed on 13 July 2025). To power our local LLM models, we have used two NVIDIA Ada 600 GPUs (Nvidia, Santa Clara, CA, USA), each equipped with 48 GB of RAM, alongside an AMD Ryzen Threadripper PRO 5955 WX CPU and 256 GB of system RAM (AMD, Santa Clara, CA, USA). To ensure continuous improvement, we have also included a support system where users can submit comments, report bugs or system failures, and offer suggestions. The software environment consists of Ubuntu 20.04 LTS, Python 3.10, and other components, including Selenium with ChromeDriver for browser automation, BeautifulSoup for HTML parsing, ChromaDB as the vector database, SentenceTransformers and Ollama. Conceptualization, H.M.J. ; Methodology, S.N.S. and S.Y.R. ; Software, S.N.S., K.N. and H.H.R. ; Validation, S.N.S., S.Y.R. and K.N. ; Formal analysis, S.N.S. and H.M.J. ; Investigation, S.N.S., S.Y.R., K.N. and H.M.J. ; Resources, H.M.J. ; Data curation, S.Y.R. and K.N. ; Writing--original draft, S.N.S. ; Writing--review & editing, S.Y.R., K.N., H.R.R. and H.M.J. ; Supervision, H.M.J. ; Project administration, K.N. and H.M.J. ; Funding acquisition, H.M.J.