# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 3 The Deep Web Communication Protocol
### Message Structure and Data Model
The foundation for scalable, repeatable data discovery across scientific domains is laid by DWCP, which transforms intricate, user-driven interactions into programmable workflows. It also ensures that once a page is indexed, it becomes not just findable, but operational, i.e., ready to be re-queried. The integration of semantic retrieval, form understanding, and automated execution supports a scalable and reproducible model for machine-actionable scientific discovery. ### Limitations

Despite its strengths, the system has several limitations. DOM interaction based on XPath is still brittle, particularly when dynamic layout changes are present. This situation arises because even minor changes in the HTML structure or dynamic content rendering can cause XPath-based selectors to fail, leading to extraction errors or missed data. By routinely updating DWCP records as part of its iterative maintenance procedure, FAIRFind mitigates the inherent fragility of XPath-based DOM interaction in the face of structural changes to web pages. Failures caused by DOM changes are fixed in later iterations when DWCP records are regenerated to reflect the updated interface, even though automatic detection of DOM changes is not yet implemented. This guarantees that even when target websites' structures change, workflows will still be precise and executable. The current implementation does not support authentication, CAPTCHA resolution, or multi-step form workflows, which are common in more secure data portals. This is because here, the system needs user interaction, session state management, or the solving of visual challenges that cannot be reliably automated, making it difficult for systems like FAIRFind to access data behind secure or interactive gateways. Furthermore, this version does not support REST or GraphQL API-based access because these interfaces frequently call for pre-specified schemas, tokens, or rate-limiting logic that cannot be automatically deduced. Additionally, table extraction is limited to well-formed layouts and may not work on designs that are heavily styled or obfuscated. This is because data may be embedded in complex DOM hierarchies, styled with CSS to alter layout semantics, or even rendered via JavaScript, which makes it difficult to parse using conventional HTML parsers. While FAIRFind successfully transforms natural language queries into deep web interactions and retrieves structured outputs, certain use cases expose limitations of the current single-hop execution model. As illustrated in Figure 6, a user querying for population-scale genomic datasets and phenotypic annotations related to diabetics receives a table of relevant dataset names and references. This output correctly reflects the structure of the data as presented by the source portal. However, accessing the actual data files or corresponding metadata often requires additional navigation steps--clicking through summary pages, interacting with secondary forms, or resolving dynamically generated URLs. These multi-hop workflows are not yet automated in the current system and remain a key area for future extension. We plan to extend DWCP to represent sequences of dependent access paths, modeled as an ordered list or graph of DWCP records, with session management and parameter passing to maintain context between steps. LLM-based planning will guide intermediate actions, enabling FAIRFind to autonomously execute complex, multi-hop workflows while preserving reproducibility and FAIR compliance. As FAIRFind evolves, we recognize that many developers and advanced users may seek not just data, but the capability to embed access mechanisms directly into their computational workflows. In scenarios where automated pipelines need to compute high-level summaries, perform cross-database integration, or support interactive assistants, the primary requirement is not data per se, but the code needed to retrieve and manipulate it.