SSSOM-Py: Supporting New RDF Serialization

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Hey guys! With the normalized form of RDF serialization about to be merged, it's time to dive into how SSSOM-Py will support it. This new format, while similar to what SSSOM-Py has been handling, has enough differences that our current parser throws a fit. So, let's explore the options to make sure SSSOM-Py plays nice with the new standard.

The Challenge: LinkML Dependence

Currently, both the SSSOM/RDF parser and writer in SSSOM-Py heavily rely on the LinkML runtime. The parser is basically a thin layer over LinkML's RDFLibLoader, and the writer uses LinkML's rdflib_dumper. This approach works fine when the RDF serialization perfectly aligns with LinkML's RDF translation rules. However, the new normalized form of SSSOM/RDF doesn't quite fit that mold. This means we need to rethink our strategy to ensure SSSOM-Py can handle the new standard effectively. The core issue is that the existing tools within SSSOM-Py are predicated on a specific RDF structure dictated by LinkML. The new normalized form, while conceptually similar, diverges enough that it breaks compatibility. We're essentially dealing with a situation where our existing machinery, designed for a particular input, is now faced with a slightly different format. This necessitates either adapting the input to fit the machinery, modifying the machinery itself, or finding an entirely new approach. The challenge lies in choosing the most efficient and maintainable path forward, balancing the need for compatibility with the desire for a clean and robust codebase. Each of the proposed solutions below tackles this challenge from a different angle, weighing the trade-offs between development effort, complexity, and long-term maintainability. Ultimately, the goal is to seamlessly integrate the new normalized RDF serialization into SSSOM-Py, ensuring that users can effortlessly work with the updated standard without encountering compatibility issues or unexpected behavior. This requires a careful consideration of the existing architecture, the nature of the changes in the normalized form, and the potential impact on other parts of the SSSOM-Py ecosystem.

Option A: Custom Code – The Cleanest Approach

Let's talk about Option A: ditching LinkML's RDF-related methods and writing custom code for RDF conversion, using SSSOM's own translation rules. In my opinion, this is the best solution. It's the cleanest and most flexible, although it might require more upfront work. But honestly, the other approaches might not be any easier in the long run.

Why Custom Code?

  • Full Control: We have complete control over how SSSOM is serialized to and from RDF. No more being tied to LinkML's rules.
  • Flexibility: We can easily adapt to future changes in the SSSOM standard without being constrained by LinkML.
  • Clarity: Custom code, well-written, can be more understandable and maintainable than complex workarounds.

Potential Drawbacks:

  • Initial Effort: Writing custom code requires a significant initial investment of time and effort.
  • Testing: Thorough testing is crucial to ensure the custom code is robust and handles all possible scenarios correctly.

In-Depth Look: Diving deep into custom code offers a tailored solution that directly aligns with SSSOM's specific needs. Unlike relying on external libraries with their own inherent constraints, crafting our own code gives us the freedom to implement the exact serialization and deserialization logic required. This approach allows us to optimize for performance, maintainability, and future extensibility. By taking ownership of the code, we gain the ability to adapt quickly to evolving standards and incorporate new features without being held back by the limitations of external dependencies. However, it's crucial to recognize the initial investment required for development and testing. Building a robust and reliable custom solution demands a thorough understanding of RDF standards, SSSOM's data model, and potential edge cases. Rigorous testing is essential to ensure that the code functions correctly across various scenarios and handles unexpected inputs gracefully. Despite the upfront effort, the long-term benefits of custom code can outweigh the initial costs, providing a cleaner, more flexible, and more maintainable solution that perfectly fits SSSOM's unique requirements. This approach empowers us to take full control of the serialization process, ensuring that SSSOM data is accurately and efficiently represented in RDF format.

Option B: Pre- and Post-Processing with LinkML – Avoidable Complexity

Now, let's consider Option B: sticking with the LinkML runtime but adding pre- and post-processing steps. The idea is to transform the RDF graph into the format LinkML expects before parsing, and then transform the output from LinkML into the new normalized form. This might seem quicker to code initially, but it introduces a lot of unnecessary complexity.

The Process:

  1. From RDF: Add a pre-processing step to massage the RDF graph into the shape LinkML wants.
  2. To RDF: Use LinkML to generate a first graph, then post-process it into the new normalized form.

Why This Might Not Be Ideal:

  • Complexity: Pre- and post-processing adds extra layers of code, making things harder to understand and maintain.
  • Performance: Transformations can be computationally expensive, slowing down the process.
  • Fragility: The transformations are tightly coupled to both LinkML's internal workings and the specifics of the normalized form, making the solution fragile to changes in either.

Detailed Explanation: The allure of Option B lies in its perceived speed of implementation. By leveraging the existing LinkML runtime, we can avoid the need to write entirely new serialization and deserialization logic. However, this approach comes at a significant cost in terms of complexity and maintainability. The pre- and post-processing steps act as intermediaries, attempting to bridge the gap between the new normalized RDF form and the expectations of the LinkML runtime. This introduces a layer of abstraction that can be difficult to reason about, making it harder to debug and maintain the code. Furthermore, the transformations themselves can be computationally expensive, potentially impacting the performance of SSSOM-Py. Each transformation step requires traversing the RDF graph, identifying specific patterns, and modifying the structure accordingly. This process can be time-consuming, especially for large and complex graphs. Moreover, the transformations are inherently fragile. They rely on specific assumptions about the structure of both the input RDF and the output generated by LinkML. If either of these formats changes, the transformations may break, requiring significant rework. In essence, Option B trades initial development speed for long-term maintainability and performance. While it may seem like a quick fix in the short term, the added complexity and fragility can lead to significant headaches down the line. A more robust and sustainable solution would prioritize a cleaner and more direct approach, even if it requires more upfront effort.

Option C: JSON-LD – Still Undefined

Finally, there's Option C: using JSON-LD, as suggested in #592, at least for converting to RDF. The problem here is that the JSON-LD serialization of SSSOM isn't formally defined yet, just like the non-LD JSON serialization. It would make sense for SSSOM/JSON-LD to be equivalent to SSSOM/RDF, but that's not decided yet.

The JSON-LD Question:

  • Undefined Standard: Without a formal definition, we're shooting in the dark.
  • Potential Equivalence: If SSSOM/JSON-LD and SSSOM/RDF are equivalent, this could be a viable option.

Why This Might Be Problematic:

  • Uncertainty: Relying on an undefined standard is risky. It could change in the future, breaking our code.
  • Complexity: JSON-LD adds another layer of complexity to the mix.

Further Considerations: Option C introduces the allure of leveraging JSON-LD, a widely adopted standard for representing linked data in JSON format. However, the lack of a formal definition for SSSOM's JSON-LD serialization casts a shadow of uncertainty over this approach. Without a clear specification, we risk building a solution that is incompatible with future iterations of the standard, leading to potential breakage and rework. Furthermore, even if a formal definition is eventually established, there's no guarantee that it will align seamlessly with SSSOM's RDF serialization. Discrepancies between the two formats could introduce inconsistencies and complexities in data exchange and integration. The key to making Option C viable lies in establishing a clear and well-defined mapping between SSSOM's data model and its JSON-LD representation. This mapping should ensure that the JSON-LD serialization accurately reflects the semantics of the RDF serialization, allowing for seamless conversion between the two formats. If such a mapping can be established and maintained, JSON-LD could offer a valuable alternative for representing SSSOM data, particularly in environments where JSON is the preferred data format. However, until a formal definition is in place, Option C remains a risky proposition with the potential for significant challenges and uncertainties. Therefore, it's crucial to prioritize the development of a clear and comprehensive JSON-LD specification before fully embracing this approach. This will ensure that SSSOM's JSON-LD serialization is robust, interoperable, and aligned with the broader linked data ecosystem.

Conclusion: My Recommendation

So, what's the best way forward? In my humble opinion, Option A – writing custom code – is the winner. It might take more effort upfront, but it gives us the most control, flexibility, and clarity in the long run. Options B and C introduce unnecessary complexity and uncertainty. Let's build a solid foundation for SSSOM-Py that can handle the new RDF serialization with ease! What do you guys think?