Enhancing Unsupervised Knowledge Graph Question Answering through Adaptive Exploration
Authors: Jia Zhu, Xi Yang


Reviewer paper summaries: The paper introduces a Knowledge Graph Question Answering framework suitable for the unsupervised setting. 
The developed method consists of two stages. First, the KG is explored by randomly walking through it using specific sampling strategies. During the exploration a sub-program is initialized with a randomly sampled class and then the sub-program is iteratively grown using guided random walk. The classes are grounded by randomly sampling among entities. Multiple such grounded programs are created.
For each program, a question is generated to create an exploration corpus. Based on the complexity of the program, a specific prompting strategy is chosen. The prompts are continuously improved through refinement. Dynamic contextual re-ranking is used to ensure the alignment of the LLM output with the intended task. 
Finally, Hybridized Learning with Transferable Case Instances is employed. Here, for a test question a set of candidate subprograms is initialized. Then, the method identifies which programs should be expanded, does this and at then end the final prediction is generated. The results are optimized using a candidate re-ranking step. The given method is compared against several other methods and surpasses their performance considerably on both datasets. There is ablation study with the majority of it written in the appendix.
Summary of strengths: The results are quite strong in comparison to the other recent methods.
An ablation study analyzing the impact of the different steps was undertaken.
Summary of weaknesses: 1) It is not fully clear to me what the actual novelties of the approach are. The paper needs to be improved to clarify this. In the appendix it is written that the reasoning methodology closely mirrors that of another work. The graph expansion seemingly does as well. The same applies to the “Hybridized Learning with Transferable Case Instances”. My guess would be that the creation and usage of the exploration graph is the main novelty. If that is the case, please make it stand out more.
2) The only thing for which it is mentioned that it is a novelty is the candidate selection step but it is only mentioned in the appendix.  Should this not be part of the main paper?
3) It is unclear to me what exactly is adopted from “Introduction to neural network-based question answering over knowledge graphs” by Chakraborty et al. Please clarify this. 
4) There are several steps or concepts which are not properly explained, namely :
- What a program is in this context? Please give an example of a program.
- How the grounding of the program works?
- What the diversified sampling strategies are?
- How the specific prompting strategies are chosen?
- Does the continuous improvement mean Human-in-the-loop?
- What is the context of the task $T$? It is mentioned in line 194.
- What the symbols $D$, $f^{-1}$, $I$, $f$ stand for? 
- “We leverage hybridized learning with transferable case instances (HLTCI) as a scoring function, integrating insights obtained from the exploration corpus (Gu 203 et al., 2023).” - What exactly is meant by that? The term HLTCI is not used in the cited work (if I have not missed it). 

I know that this is a short paper but terms should at least be introduced and used notation should be defined. Substituting some definitions with references to existing is fine. Nevertheless, after reading the referenced works, it was hard to connect the works to the paper at hand.

5) It should be more clearly stated why the method allows unsupervised KGQA. Sometimes the term “fine-tuning” appears. Why does this not conflict with the approach being unsupervised?

6) Figure 1 does contain vital information not mentioned in the text (e.g., that entity linking is used). Also, please increase its size.

I feel that the work would benefit from being written as a long paper. It is lacking a lot of information which is vital to understand the actual method.
Comments, suggestions and typos: - Typos in Figure 1: 
	- “Initialize set candidate set”
	- “likelihood of each sub-program serving as a valid …”
- Line 184 vs. 187: Once, the numerator contains |x|, once |y|. Is this correct? What does |x| or |y| stand for? Maybe also properly describe what x or y are.
Overall assessment scores: 2