Where did you obtain the ground truth answers for the CWQ dataset?
I just reran the provided SPARQL queries on my installation of Freebase and I am getting 81.11% answers correct.
Shouldn't I be getting 100%?

Hi! Thank you very much for your work.
In your paper you write "ToG first prompts LLMs to automatically extract the topic entities in question and gets the top-N topic entities", but could not find the prompt, which you used to extract those topic entities?
In the repository they seem to be already hard-coded in the dataset JSONs due to a previous pre-processing step?

Could you describe how you extracted the topic entities?

What's the correct result after running the Test example?
My result is {'head': {'link': [], 'vars': ['name3']}, 'results': {'distinct': False, 'ordered': True, 'bindings': []}}.
I think there is some mistake, but I follow the readme in the "Freebase" folder.

Hi,
Thanks for sharing the code.

The id2entity_name_or_type function seems not function as expected due to variable overwriting.

Is that right if I rewrite into this ? Thanks.

def id2entity_name_or_type(entity_id): sparql_txt = sparql_id % (entity_id, entity_id) sparql = SPARQLWrapper(SPARQLPATH) sparql.setQuery(sparql_txt)

Is this method validated on relatively complex knowledge graph inference datasets such as win18rr or nell datasets? Recent papers have generally not tested on these datasets, is this because the previous dataset was too complex or because of other considerations?

What's the correct result after running the Test example?
My result is {'head': {'link': [], 'vars': ['name3']}, 'results': {'distinct': False, 'ordered': True, 'bindings': []}}.
I think there is some mistake, but I follow the readme in the "Freebase" folder.

Hello,

I have a question regarding the FilterFreebase data set which is approximately 125GB in size after filtering. Can you please advise on how much memory the virtuoso.db file might approximately occupy after the data has been imported into the Virtuoso database?

Thank you for your assistance.

Hi,

Once you generate the initial topic entities using the LLM, how do you find the corresponding entities in the KG? The LLM could output entities that do not exist in the KG. Also, what is the prompt used to find the topic entities?

Thanks,
Mohammad

Hi! Thank you for your work!

I ran the test example without installing the Freebase dataset and encountered an error at "results = sparql.query().convert()", which says: "{URLError} <urlopen error [Errno 111] Connection refused". When I run main_freebase.py, it also reports "urllib.error.URLError: <urlopen error [Errno 110] Connection timed out". Can this issue be resolved by installing the Freebase dataset, or is it caused by an internet connection error? Thank you!

Additionally, is it possible to avoid installing the dataset? My server doesn't have 400GB of RAM, making it very difficult for me to run the program. Projects like RoG (Reasoning on Graph) can run on WebQSP and CWQ with only a few hundred MBs of memory. Is it possible to run ToG with a single dataset (like WebQSP) with less memory consumption? Thank you!

Have you tried running it on Windows?

hi, thanks for your talent work.
I see that topic entities in CWQ have already been linked to Freebase and Wikidata. In the paper, it says"TOG first prompts LLMs to automatically extract the topic entities in question and gets the top-N topic entities. " But the preprocessing details are missing in the code.

Hi, I found some bug while reimplement ToG:

• ./ToG/utils.py line 155: there is a typo of missing underline between “pre” and “head”
• ./ToG/README.md lines 12 and 23: duplicated argument name, I think the duplicated one should be temperature_reasoning.
• ./ToG/prompt_list.py lines 14 and 20: typo, it should be Entities instead of Entites. Does it affect LLM’s performance?
• ./ToG/utils.py lines 105-110: why the code called llama server when ”llama" not in engine.lower()? I think it should call GPT 3.5-turbo in this situation.

Hi, there

I appreciate the work you've put into this project.

I kindly request that you upload the input-output text data from LLMs during the "Thinking" process. This will provide a deeper understanding of the model's thinking process and its responses. It can also assist in identifying any biases or limitations in the model's output.

Thank you in advance for your consideration.

How much does it cost to call the GPT3.5API if the results of the experiment need to be replicated?

Hello,

I am trying to reproduce the results on WebQSP using Chat-GPT-3.5, but I am facing some challenges with the provided command. I find that the exploration process is sensitive to the temperature. I was wondering if you could provide the correct command to reproduce the results successfully.

I currently use python main_freebase.py --dataset webqsp --max_length 256 --temperature_exploration 1 --temperature_reasoning 0 --width 3 --depth 2 --remove_unnecessary_rel True --LLM_type gpt-3.5-turbo --opeani_api_keys sk-xxxx --num_retain_entity 5 --prune_tools llm

Thank you!