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

DWCP generation and execution are implemented in Python, using modules for the following:

* Form discovery and analysis: Selenium + BeautifulSoup parse HTML DOM and detect interactive components. * Semantic reasoning:Large language models (LLMs) generate form summaries, infer sample values, and validate field semantics. * Access path construction:A DWCP object is constructed with all necessary details and serialized to JSON. * Execution engine:A headless browser reproduces interactions by consuming DWCP records. ### Protocol Operations

Table 2 summarizes the key features of DWCP as a standardized protocol, including its data format, mandatory and optional fields, supported access types, transport mechanisms, security options, and execution guarantees. DWCP supports stateless communication between agents and services. Agents start by contacting possible deep web resources with discovery requests. In response, services provide organized summaries

of the datasets they make available. Then, using the given specifications, agents create customized access requests. The protocol uses thorough access path descriptions to guarantee reproducible interactions. A service gives agents all the information they need to submit requests and parse responses when it describes a form-based resource. This covers response parsing instructions, submission endpoints, and parameter specifications. DWCP-specific error codes are added to standard HTTP conventions for error handling. Services use structured error responses to show authentication failures, rate limiting constraints, and parameter validation errors. ## 4 System Logic

In this section, we will detail our architecture's key modules and explain how they work together to enable FAIR-compliant data discovery. Our system enhances FAIRsharing's data accessibility by creating an automated pipeline. This pipeline can discover, extract, and semantically query deep web content. ### Architecture Overview

We propose a modular system organized into four main components:

\[\text{System}=(\text{H},\text{C},\text{I},\text{R}) \tag{1}\]

where H denotes the metadata harvester, which gathers structured information from FAIRsharing records. C is the deep web crawler and form analyzer that explores web interfaces to locate data entry points, while I refers to the data extractor and vector indexer that processes retrieved content into structured representations. These three components collectively form the data indexing pipeline. The fourth component, R, is the semantic query and retrieval engine that enables natural language access to the indexed data. Figure 1 shows the flow of interactions between these components. The data indexing pipeline is depicted in the upper portion of the diagram, where relevant database pages are retrieved and metadata from FAIRsharing is collected. In order to extract forms, embedded tables, or FTP data schemas, the crawler explores these pages. Semantically interpreting forms, choosing the right fields with relevant example inputs, and simulating form submissions when needed are all performed by the system using an LLM. Whether they are taken from files, tables, or forms, the extracted schemas are saved in the indexed dataset after being encoded into DWCP records. The query engine workflow is depicted in the diagram's lower section. A user submits a natural language query, and the indexed dataset matches it to retrieve related resources and schemas. The system simulates the submission after using the LLM to determine the proper input values if the relevant access path is a form. The system parses the structured content directly if it is a table or file (TABLE/FTP). The user receives the final structured data in both situations.