# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 3 The Deep Web Communication Protocol
### Message Structure and Data Model
#### 4.5.4 Dynamic Interaction and Data Extraction

Once the most relevant DWCP object is retrieved, the system parses its access path field and simulates the corresponding interaction based on the declared access type. If the type is form, the system uses input values inferred from the user's query to populate the fields and then submits the form via browser automation. For html table access, the system loads the target page and extracts the table contents using either Selenium or an LLM-assisted parser capable of handling dynamically rendered or non-standard table structures. When the access type is ftp, the system directly downloads the linked file and parses it to retrieve structured data from its contents. If the interaction involves a form, an LLM infers realistic values based on field names and the semantics of the query. These values are programmatically injected into the form before submission. #### 4.5.5 Output Rendering

The final output of the FAIRFind system is returned as one or more structured data tables in tabular format. This design ensures that responses to user queries yield usable, well-organized datasets--rather than ambiguous or overly general descriptions--making them immediately suitable for downstream analysis, integration, or reuse in FAIR-aligned research workflows. Figure 2 displays the semantic query interface of FAIRFind. In this example, we use the query \(Q\) where a user is requesting proteins that are associated with diseases and annotated with specific UniProt functional information and confidence scores. The system parses this intent and executes structured retrieval across deep web-indexed sources. The output presents a curated table listing protein entries, including gene symbols, UniProt identifiers, annotation scores, and full protein names. This example highlights FAIRFind's ability to transform unstructured user questions into structured outputs, bridging the gap between complex biomedical queries and actionable dataset retrieval. ### User Interface and Interaction Design

The FAIRFind system is deployed as a web-based application with an intuitive and accessible interface designed for end-users in the life sciences community. As shown in Figure 2, the main screen features a single query input box where users can enter natural language or schema-aware queries. To assist users who are unfamiliar with the system or the underlying data, an Example Question button is provided, which pre-fills the input box with a representative query illustrating the system's capabilities. Upon clicking the Submit button, FAIRFind processes the query and renders the results in a structured tabular format below the input area. To support continuous improvement and user engagement, the interface also includes a dedicated feedback button that allows users to report issues, suggest improvements, or submit bug reports. Feedback collected through this channel informs iterative updates to the system and helps address any usability or technical issues encountered by end-users. ### Prompt Engineering and Output Structuring

FAIRFind integrates large language models (LLMs) into key stages of its indexing pipeline, where conventional rule-based methods are either too strict, inefficient or unreliable. To ensure consistency, interpretability, and robustness, we adopt structured prompt engineering techniques paired with constrained output schemas. A context-aware prompt

Figure 2: Semantic query interface of FAIRFind for deep web biomedical data. The interface allows users to search scientific deep web resources using natural language. It interprets user queries, matches them to appropriate access paths, and returns structured data. In this example, the user requests proteins associated with diseases and annotated with UniProt functional metadata and scores. The interface parses this natural language input and returns a tabular dataset, each annotated with UniProt ID, annotation score, and protein name.