# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 3 The Deep Web Communication Protocol
### Message Structure and Data Model
that is dynamically created according to the properties of the target web resource directs each LLM invocation. These prompts use FAIRsharing metadata, HTML fragments that have been extracted, and parsed interface elements like field names, form methods, and placeholder text. We enable the model to accurately reason about its purpose, whether it is to identify the most relevant form, summarize access behavior or generate plausible input values, by customizing the prompt to the structure and semantics of each page. We use a variety of prompting techniques, such as instruction-style prompting and chain-of-thought prompting, to increase reliability across various web layouts [46,47]. A key design principle is the use of structured output constraints. All model responses are expected to conform to a pre-defined JSON schema. This schema defines the required fields, value types, and nesting rules for each component of the output. Structured outputs enable automatic validation, simplify downstream integration into indexing systems, and ensure consistent, interpretable model behavior across diverse queries and data sources. ## 5 Evaluation Framework

To assess the performance and generalization ability of our system across different access types and language model variants, we constructed an evaluation framework centered on real-world FAIRsharing entries. The evaluation focuses not only on retrieval accuracy but also on execution success and system efficiency. ### Benchmark Construction

We begin by sampling a balanced benchmark set of 50 FAIRsharing records, selected to ensure a balanced coverage of the three DWCP-supported access types (forms, tables, and downloadable files). Each record includes the site URL, page URL, and the corresponding DWCP access path, which serve as ground truth for evaluation. This is denoted as

\[\mathcal{D}=\{d_{1},d_{2},\ldots,d_{50}\} \tag{5}\]

where \(D\) denotes the dataset of FAIRsharing records, and \(d_{i}\) is the \(i\)-th record selected to ensure coverage of all DWCP-supported access types. For each \(d_{i}\), we extract a ground truth tuple:

\[g_{i}=\big{(}\text{site\_url}_{i},\text{page\_url}_{i},\text{access\_path}_{i} \big{)} \tag{6}\]

where \(site\_url_{i}\) is the homepage of the resource, \(page\_url_{i}\) is the page hosting the data, and \(access\_path_{i}\) is the DWCP-specified interaction workflow. These serve as reference points for evaluating site ranking, page retrieval, access path identification, and execution success. To evaluate both precise and abstract language inputs, we generate two types of queries (see Appendix A for the full prompt template) for each \(d_{i}\in\mathcal{D}\):

* Asimple query\(q_{i}^{\text{simple}}\) using exact field names and form inputs. * Acomplex query\(q_{i}^{\text{complex}}\) that captures intent abstractly. For each record \(d_{i}\), we generate 2 natural language queries, resulting in a total of 100 queries. The full query set is

\[\mathcal{Q}_{\text{eval}}=\{(q_{i}^{s},q_{i}^{c})\}_{i=1}^{50} \tag{7}\]

where \(Q_{\text{eval}}\) is the evaluation query set, with \(q_{i}^{s}\) and \(q_{i}^{c}\) denoting the simple and complex queries generated for \(d_{i}\) using GPT-40, incorporating table headers, form metadata, and page content for semantic relevance. Table 3 presents simple and complex example queries. For semantic retrieval, we retrieved the top-\(k=10\) most similar schema documents per query to ensure coverage while maintaining precision. During query generation and form interpretation, the LLMs were configured with a temperature of 0 to favor more factually consistent and deterministic outputs, which was critical for producing valid and reproducible DWCP records. The structured output schema enforced the inclusion of all required fields. We found these settings provided a good balance between factual accuracy, diversity of results, and runtime efficiency in our experiments.