# A FAIR Resource Recommender System for Smart Open Scientific Inquiries
## 3 The Deep Web Communication Protocol
### Message Structure and Data Model
DeepSeek-R1 and Owen3 perform comparably, with scores of 0.520 and 0.480, respectively. Gemma, however, lags behind at 0.449. This drop relative to site match rates indicates that even when the correct domain is identified, locating the exact interactive or data-bearing page presents a more nuanced challenge for the models. The most challenging retrieval task turns out to be recovering the exact DWCP access path block, which includes the interaction method and related metadata. Here, LLaMA 3.3 continues to outperform others, with a schema match rate of 0.531. DeepSeek-R1 follows at 0.449, Qwen3 at 0.418, and Gemma at 0.408. The decline in scores from site- to access-level accuracy illustrates the increasing difficulty of aligning user queries with detailed, structured access pathways. ### Data Retrieval Performance

Figure 4 reports the Success@1 rate for each evaluated model. This metric captures the percentage of queries for which the system successfully retrieved a valid structured dataset \(\mathcal{D}_{q}\) from the predicted access path. Unlike exact match metrics, Success@1 reflects the end-to-end robustness of the system--combining semantic retrieval accuracy, form simulation reliability, and extraction logic effectiveness. Across the board, models exhibit high retrieval success rates, demonstrating the system's robustness in executing diverse deep web interactions. DeepSeek-R1 achieves the highest Success@1 rate at 98.98%, successfully returning structured data for 97 out of 98 test queries. This performance validates its strong balance between fast execution and dependable access path resolution, even when schema-level precision varies. Figure 3: Exact match accuracy of four open-source language models—LLaMA 3.3, DeepSeek-R1, Owen3, and Gemma—across three retrieval metrics: site match, page match, and access path (schema) match. Each bar represents the proportion of queries for which the system, using the respective model, correctly identified the target repository domain (site), the specific data-containing page (page), and the structured access path required to extract the data (access-info). The results reveal a consistent performance advantage for LLaMA 3.3 across all tasks, especially in more fine-grained retrieval steps. Gemma closely follows with a 97.96% success rate. Despite modest lag in page and schema match accuracy, it generalizes well across form and table interfaces and exhibits minimal failure cases. Both LLaMA 3.3 and Owen3 yield a success rate of 93.88%. LLaMA's lower score is primarily attributed to occasional delays and form execution failures, which offset its strong retrieval precision. Meanwhile, Qwen3's fragility in handling complex forms--particularly value inference and field alignment--contributes to its few retrieval failures, despite an otherwise efficient runtime profile. ### Retrieval Time and Efficiency

Figure 5 presents the distribution of end-to-end retrieval time per query across all evaluated models. This measurement encompasses the complete execution pipeline, including vector-based semantic search, schema resolution, form population (if applicable), page loading, and structured data extraction. As such, it offers a realistic assessment of each model's responsiveness in real-world deployment conditions. LLaMA-70B exhibited higher response times, including several outliers. We note that some of the higher response times, particularly the outliers, are attributable to delays or unavailability of the external resource servers themselves, which are often hosted on non-production academic infrastructure and outside the control of FAIRFind. Among the evaluated models, LLaMA 3.3 incurs the highest average retrieval latency, with a mean of 39.3 s per query. This elevated duration stems from both its large model size (70B parameters) and the longer generation completions typically produced during form interpretation and sample input inference.