In T245848#5990316, @Dzdaniel wrote:

Hi all! I am still stuck on the 1st micro task. I've spent some time reviewing the definitions of revid and qid but still I don't understand what I have to do next. If someone could help I would greatly appreciate it.
Thanks in advance

In T246013#5983454, @Tanvi_Singhal wrote:

In T246013#5976783, @Sargamm wrote:

I was just testing and playing around with different entities, I ended up getting a negative value for the cosine similarity. Has anyone else encountered it? what should this imply?

Hey, @Sargamm as @Soniya51 mentioned that cosine similarities range from -1 to 1. Imagine two embeddings as 2 vectors in an n-dimensional space and we are calculating the cosine of the angle between two vectors. From 0 to 180 cos (theta) could range from -1 to 1. If both vectors are aligned in same direction that means they are similar and angle between them is zero degrees and cos(0)=1.

Alright! Thank you

I was just testing and playing around with different entities, I ended up getting a negative value for the cosine similarity. Has anyone else encountered it? what should this imply?

@Sseidmed are you now clear with it? as @Soniya51 just answered. Just adding to it, the Q# are qids for the wikidata entity, that values is linked to.

In T246013#5973604, @Sseidmed wrote:

Hey all! I seem to be getting a hang of this project and the functions, with trial and error. In the second function, we are looking to have properties and values as outputs. What exactly do those properties and values describe about a particular page/article? What kind of information do they give us?

@Tanvi_Singhal the probable reason for the difference of Africa's data could be because Africa is a continent and USA and Poland are countries, so the properties and their values for Africa differ more than USA and Poland. This is what you mean right @Isaac ?

Hi @Tanvi_Singhal @Dikshagupta99, I think you're right, the cosine similarity is very close to 1 and a possible reason for it is because the two entities share the same attributes of data ( as you mentioned). I also was checking similarity between document_embeddings, For example I compared Greenland with a variety of other entities ranging from some unrelated politicians, Greenland's Prime Minister to it's continent(North America) , neighboring country( Canada ) and capital city( Nuuk) . What I found out was that even though Greenland's PM and Greenland are somewhat related, their cosine similarity was just 0.366 and the cosine Similarity with it's continent being 0.726, with it's neighboring country was 0.923 and with it's capital the cosine similarity was 0.955. Also, cosine similarity with some other country( in another continent) like Tanzania, the cosine similarity was 0.76 . So what I am concluding from this is that, similar attributes lead to higher cosine similarity like all these were geographic regions, which was not the case when the country was compared with Personalities.

okay, Thank you!!

what exactly is the datatype of embeddings in task 3? Is it an array of tuples or what? How to access the corresponding embeddings of a particular property?

In T246013#5959633, @Dikshagupta99 wrote:

Hi, Can someone please provide me with some revids. I want to check if my function1 is working fine with all revids or not.

In T246013#5957178, @Tasbiha211 wrote:

How do I get page properties or even the page URL using revision ID?

In the third function, to convert the claims in a wikiData item into a document embedding, what exactly is the embeddings argument being passed to the function? how are the corresponding embeddings of the properties being accessed?

@Isaac okay, Thank you!

In the second function to gather wikiData claims for a wikiData item, do we need to also include tuples of properties that appear under another property? for eg- in https://www.wikidata.org/wiki/Q2427544 the property, 'point in time' that appears under 'Hugo Award for Best Novel' which is the value for the property, 'award received'

Hi Everyone! i'm Sargam, an outreachy applicant from IIIT-Delhi. Hoping to be an active contributor here.